58 avsnitt • Längd: 50 min • Månadsvis
Are you tired of spending hours mastering the latest data science techniques, only to struggle translating your brilliant models into brilliant paychecks?
It’s time to debug your career with Value Driven Data Science. This isn’t your average tech podcast – it’s a weekly masterclass on turning data skills into serious clout, cash and career freedom.
Each episode, your host Dr Genevieve Hayes chats with data pros who offer no-nonsense advice on:
• Creating data solutions that bosses can’t ignore;
• Bridging the gap between data geeks and decision-makers;
• Charting your own course in the data science world;
• Becoming the go-to data expert everyone wants to work with; and
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Whether you’re eyeing the corner office or sketching out your data venture on your lunch break, Value Driven Data Science is here to help you rewrite your career algorithm.
From algorithms to autonomy – it’s time to drive your value in data science.
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Genevieve Hayes Consulting Episode 58: Why Great Data Scientists Ask ‘Why?’ (And How It Can Transform Your Career)
Curiosity may have killed the cat, but for data scientists, it can open doors to leadership opportunities.
In this episode, technology leader Andrei Oprisan joins Dr Genevieve Hayes to share how his habit of asking deeper questions about the business transformed him from software engineer #30 at Wayfair to a seasoned technology executive and MIT Sloan MBA candidate.
You’ll discover:
Andrei Oprisan is a technology leader with over 15 years of experience in software engineering, specializing in product development, machine learning, and scaling high-performance teams. He is the founding Engineering Lead at Agent.ai and is also currently completing an Executive MBA through MIT’s Sloan School of Management.
[00:00:00] Dr Genevieve Hayes: Hello, and welcome to Value Driven Data Science, the podcast that helps data scientists transform their technical expertise into tangible business value, career autonomy, and financial reward. I’m Dr. Genevieve Hayes, and today I’m joined by Andrei Oprisan. Andrei is a technology leader with over 15 years of experience in software engineering.
[00:00:24] Specializing in product development, machine learning, and scaling high performance teams. He is the founding engineering lead at Agent. ai, and is also currently completing an executive MBA through MIT’s Sloan School of Management. In this episode, we’ll be discussing how data scientists can grow into business leadership roles by exploring Andre’s own career evolution from technology specialist to seasoned technology leader.
[00:00:55] And more importantly, we’ll be sharing specific steps that you can take to follow his path. So get ready to boost your impact, earn what you’re worth, and rewrite your career algorithm. Andre, welcome to the show.
[00:01:09] Andrei Oprisan: Thank you. Great to be here. Great
[00:01:11] Dr Genevieve Hayes: We’re at the dawn of the AI revolution with everyone wanting to get in on the act and many organizations terrified of being left behind.
[00:01:21] As a result, there are more technical data science and AI centric roles being advertised now than ever before. However, this also brings with it unprecedented opportunities for data scientists to make the leap into business leadership, if they’re willing and if they know how. And those are two very big ifs, because in my experience, Many data scientists either don’t know how to successfully make this transition, or write off the possibility of doing so entirely for fear that it’ll take them too far away from the tools.
[00:01:55] Now, Andre you started your career as a software engineer, but have since held a number of technology leadership roles, including VP of Engineering at Liberty Mutual Insurance, Chief Technology Officer at OneScreen. ai, And your current role is head of engineering at agent. ai. What is it that first started you on the path from technical specialist to business leader?
[00:02:21] Andrei Oprisan: question. So for me, it was all about asking deeper questions as to the why and that led me to ask them more questions, you know, but why and why again, why are we doing this? Why are we prioritizing this kind of work? What makes us believe this is the right kind of feature, to work on as a developer which inevitably leads to some kind of business questions some questions about. Who the customer is and why we’re serving those customers are those customers, right? Kinds of customers. To serve in the 1st place, or, should we be thinking about different kinds of customer personas?
[00:02:56] And what does that mean? All the way to, how do you actually make money as a business? Why are we doing this? Is it to drive efficiency? Is it to serve a new, on top market potentially? And so. As you mentioned, I started as a developer, I started my career at Wayfair back in the early days when they were, I think it was engineer number 30 company of 100 or so people back in the early 2000s.
[00:03:20] And we were. Developing big features. I remember I own a big part of baby and wedding registries and checkout and customer reviews. And I was building more and more features and I was sitting and also in more meetings with product managers who are usually the kind of the interface right in a tech world to sort of the business.
[00:03:42] And I kept asking more and more questions around it. Hey, but why are we doing this? Why are we solving for baby registries? Why are we solving for wedding registries?
[00:03:51] So again. For me, it really started from early days of my career, all the way through later stages, where I was always asking more questions about, is it the right thing?
[00:03:59] The highest value thing that we can work on as engineers, as developers, as technical folks, or is there something more valuable that we should be working on that we should be aware of? That we should be asking deeper questions about. And it really started with that kind of inquisitive nature, always asking, why are we doing this?
[00:04:16] You know, I’m here as part of this team, and I want to understand why we’re doing these things. So I can be more effective. So I can make sure that, I. Do as much as possible to make a successful
[00:04:27] Dr Genevieve Hayes: That approach of asking all those why questions, that’s what they recommend to people in pretty much every management consulting advice book. The three. of Management Consulting. Why this? Why now? Why me? Did you pick that up from reading some sort of Management Consulting book or do you just have an naturally inquisitive nature?
[00:04:48] Andrei Oprisan: now for me it was more natural, maybe a bit stubborn, maybe depending on what you ask, maybe a bit , irreverent just to sort of asking the question. So, , why are we doing this? But as a developer, as you’re building out features, you can build a very simple version of an ask or you can build something very complex that needs to scale. That needs to take into account a number of different kinds of factors. And so we really started with. Trying to understand, okay, what is the actual technical requirement and why do we think that is
[00:05:16] and that’s usually defined by some kind of either tech lead and a team or a product manager or some combination thereof. And I found that to be very helpful, both for me and those non technical counterparts to ask those why questions because it really revealed a lot of the assumptions that went into the road map that went into even the business thinking there’s obviously some assumption that.
[00:05:41] For instance, we’re going to invest in scale from a dev ops standpoint, for example to make sure these servers don’t tip over. We’ll be able to handle more traffic because we expect growth. Okay. But when is that? Why is that?
[00:05:53] And it started from me, just not really understanding the business and wanting to learn and more wanting to learn on a deeper level to say, okay. I can understand. I became an expert in baby and wedding registries and all the competitors and I think that that’s part of what’s necessary to be able to build.
[00:06:12] Good products that kind of obsession, with the product and , asking questions until you really understand the landscape and what you should and shouldn’t be building. I think those are critical aspects of knowing what to build and not to build to be able to.
[00:06:26] And get some better outcomes.
[00:06:28] Dr Genevieve Hayes: And so by asking these questions, did senior leadership see that as a sign that you had management or leadership potential and then did you naturally get promoted or did you actively seek out those business leadership roles?
[00:06:44] Andrei Oprisan: I think a little bit of both, but more likely in the beginning. It was more the former, so I was asking. More of the questions for the sake of the questions and really wanting. To build a better product, which then led to just more responsibilities. And it was clear to me that I wanted.
[00:07:02] Those kinds of questions to be asked and answered. And many times they want, many of those sort of technical conversations they were having, those kinds of questions weren’t really asked by the technical folks. And so I became the kind of person that would always ask those questions and always.
[00:07:19] Push us to get good answers to those questions and really test those assumptions over time, as I became more senior in my roles building more complex systems that led to more complex questions that needed answers and increasingly got in front of more senior folks.
[00:07:37] So what became conversations Within a team with a product manager or a junior product manager talking to junior engineers became conversations, between senior engineers. And directors of thought up and things like that. And so, I just became part of. In those rooms where those conversations were happening at a higher level that led me to ask more important white questions more around.
[00:08:01] The business strategy, why do we think this is the right segment to tackle? Why do we think we’re going to build technology that is really differentiated, that is not just another solution that we could have just bought off the shelf.
[00:08:13] And those are very interesting conversations to have. And I think that the kinds of conversations that we don’t get to really have, we’re not really focused on both the technical, but not technical just for the sake of technical sort of solutioning, but technology in the service of the business and the service of a business that is, wanting to grow and stay competitive and and be able to win at whatever the business is trying to do,
[00:08:40] Dr Genevieve Hayes: It sounds like your nature made you very well suited to a business leadership role, even though you started off as a technical specialist. But I’ve met a lot of data scientists over the years who are very adamant that they don’t want to move away from purely technical roles and into leadership roles.
[00:09:01] For example, I’ve been in teams where the team leader role has It’s been advertised and every single technical person in that team has refused to apply for it because they don’t want to move away from the tools. Is this something that you experienced early in your career?
[00:09:19] Andrei Oprisan: definitely, and that’s part of every individuals journey as we’re moving through those individual contributor ranks. There are levels to the individual contributor roles, you can go from junior to very senior, to principal or staff or a member of technical staff and different companies have the sort of laddering that can even go up to the equivalent on the sort of management side, all the way to VP levels Microsoft is famous for, their laddering where you can have Distinguished engineers that are the equivalent of VPs will have hundreds of people who are reporting to them and have similar compensation structures.
[00:09:55] So, again, it is possible. Not every organization is set up for that. And so I think part of this has to 1st, start with the right level of research and say, okay. If I’m the kind of person that wants to do only technical work. Will the career progression and this organization really support my objective,
[00:10:14] if the most senior level that you can go to might be just a senior engineer level, that might be okay. And that might be the right place for you. But if you want me more responsible and we want to be more of an architect or someone who. Is coordinating, larger, project deployments across multiple divisions,
[00:10:37] I would say, figure out if the organization. As those kinds of opportunities, and in many cases, they don’t, because they don’t know that I need, it hasn’t been proven as an actual need. So, part of it is, how comfortable are you? And being that sort of trailblazer and taking some risks and, of crafting your own role versus, working within the existing bounds where you may have a well defined ladder.
[00:11:03] And, in other cases, it might be that, no, there is a ceiling and in many organizations, that is the case, especially in a non technology companies, and companies that certainly have a technology or it department and some fashion. But they might not have, the same level that you can go to.
[00:11:21] Compared to in a potential business role and that needs to be a decision that is that made to say, okay, is this the right kind of place for me? Can I grow and learn? To the level that I’m looking to grow and learn to and then figure out, if you can sort of.
[00:11:36] Move beyond some of those limitations, what are they and what are you comfortable with?
[00:11:41] Dr Genevieve Hayes: Early in my career, it was the case that basically in Australia, if you wanted to get beyond a very moderate salary, you had to go into management if you’re a technical person. But. In recent years there are an increasing number of companies and organizations that are building in that technical stream.
[00:12:03] I think Deloitte in Australia now does have a technical stream where you can get quite senior. And I know of some government organizations that also do. I’m not quite sure how well that works in practice, but it’s a move in the right direction.
[00:12:20] Andrei Oprisan: Right, and I think that’s that’s only increased over time. I’ve only seen companies create more opportunities for those very senior technical folks, not fewer. So, again, I think it is encouraging, but I’d also say, you’re not going to find the same.
[00:12:36] Leveling across the board for technical folks as you would, let’s say for management oriented and at a certain point, need to make the decision in terms of. Do you want to stay as an individual and the whole contributor, or are you open to management?
[00:12:51] It doesn’t mean from a management standpoint, you’re not technical or, you’re not needing to your technical skills, but it may mean that, yes, you’re no longer coding every day. Right, you are maybe at best reviewing architecture documents and really pressure testing the way the systems are designed and having bigger conversations around, cost optimization and.
[00:13:14] Privacy and security implications of the work that is being done and making sure that then those are addressed. Which again, there are different kinds of challenges. They’re still technically challenging. And you’re going to need good advice from additional folks, individual contributors on the teams, but they are different.
[00:13:32] Dr Genevieve Hayes: The other thing I’d add to all this is, even if you choose to remain in that individual contributor stream, as you move up the ranks, you are still going to be associating more and more with senior leadership and having to think about things from a business point of view. It doesn’t matter whether you’re managing staff or not.
[00:13:51] You need to become more business centric. And that idea that a lot of very technical data scientists have of just being left alone in a room to code all day. That’s not going to happen once you get above a certain level regardless of if you’re technical or a leader.
[00:14:10] Andrei Oprisan: That’s right, and I think it’s. Figuring out the right balance of enough technical work, and that can mean different things over time with enough. Organizational impact, which is another way to look at the business elements of. You know, we’re doing a bunch of work, but again, is it making money?
[00:14:29] Is it helping our customers get more of what they need? Is it improving some kind of output that the organization is measuring. If we can’t answer any of those questions , to some level of sophistication, then, if we’re working on the right thing or not, would we even know,
[00:14:45] and would it even about it may be a very interesting technical problem, of course, but does it matter at all? will anyone even see it when you care? I think by, understanding the business understanding, maybe how many eyeballs. The product is going to get in front of and what the assumptions are and even, coming up with some of those numbers is going to really affect what you’re thinking about what you’re building and why you’re building.
[00:15:09] Dr Genevieve Hayes: It sounds like you making that transition from being a technical expert to being a business leader was very organic for you, but was there ever a point in time where you actually consciously thought, okay, I’m actually focusing on this business leadership thing. I’m no longer a technical specialist.
[00:15:28] I am a data science or engineering leader.
[00:15:32] Andrei Oprisan: Yes, when I transitioned from Wayfair I work for an eCommerce consulting shop. So there is where I learned a lot of my sort of consulting skills and really understand how to talk to. Chief marketing officers and CEO. So understand, what exactly are you trying to accomplish?
[00:15:48] But in those conversations, it became very clear to me that I needed to understand more about the business, not less, even as I was very technical, I was a tech lead, I was running the technology team, in charge with the recruiting with defining the staffing plans and also architecting some of the solutions.
[00:16:10] And so it became very clear that I needed to understand even more. About what the actual goals were of the organization, because the very first iteration of the project we came in with the wrong assumptions completely, and we came up with some technical solutions that made no sense for where they were trying to go.
[00:16:30] 2, 3, 5 years later we came up with something that made sense for a proof of concept and sort to get to an initial contract. But actually, we were setting them up for failure in 4 to 5 years were actually the solution that we were proposing wouldn’t be able to support the kinds of customization as they would need when they moved to 20 different supply chain partners and just having those conversations at a, higher level
[00:16:57] It was very eye-opening when I walked out of a few of those meetings. Understanding that 90 percent of our assumptions were just incorrect. It’s like, Oh my God, what are we doing? And why are we having this entire team of engineers building these features for, I think it was Portugal and Spain stores where, we were just expected to lift and shift that for Japan, and that we’re just not going to be possible said, okay,
[00:17:22] This made absolutely no sense. Let’s have deeper conversations about. The business what their goals are and how the technology is going to support that both now in the very short term, and we’re applying a very short term kind of mentality. But also long term also in 4 to 5 years, assuming the business is successful and they meet their objectives.
[00:17:44] How can we make sure we’re enabling their long term growth?
[00:17:48] Dr Genevieve Hayes: So it sounds like if one of our listeners wanted to follow your lead and move from technical specialist into a business leadership role, one of the first steps that they should take is to understand the objectives and goals of their organization and how their work can feed into achieving those goals and objectives.
[00:18:09] Andrei Oprisan: Absolutely. I think it’s just having those simple questions answered around. What is the business? What is it doing? Why is it doing it? Why are they in this specific sector now? How has this evolved? And then being able to answer, how are they actually able to do that? Is it people?
[00:18:28] Is it process? Is that technology is probably a combination of all of those different factors, but technology can have a multiplying effect, right? And I think it’s asking those questions in terms of where they are now and looking at different ways of expanding different ways of providing. Goods and services and using technology to more efficient.
[00:18:49] And , it’s just looking at the business, but I would call it. A common sense approach and asking the kinds of questions. Okay. Someone in on the business side, if they can’t answer things in a simple. Way ask more questions if you can understand them in the terms that.
[00:19:08] They’re giving back to you then then ask more clarifying questions. Don’t just assume. Right and it’s okay to not be an expert in those things. The challenge that I had in the beginning was getting frustrated with. My blind spots and my lack of really understanding I think it was.
[00:19:24] You know, 1 of the early examples was this around tax treatments and, how obviously. Different territories have different rules for when and how you collect taxes.
[00:19:34] It gets into a lot of complexity, but, it was very eyeopening. To ask more of those questions and to understand just how complex of an environment the business operates in, which allowed me to be a better developer, which allowed me to be a better team lead, which allowed me to then be a better partner, frankly, to those business folks who, you know, they have the same goals for the organization that we should have.
[00:19:59] The company is going to grow. And if the company grows and it does well, then it means good things for everybody on the team. And if they don’t, that’s going to lead to equally bad things for everybody on the team. And so I think part of it is having that ownership mindset of it’s not someone else’s problem.
[00:20:16] If we don’t understand this, it’s my problem. It’s my problem that we don’t understand how we’re going to need to customize this types engine. Because we might get hit with fines and we might need to retroactively as a severity one drop everything now. Anyways, kind of issue later than the line,
[00:20:34] Dr Genevieve Hayes: So what is the single most important change our listeners could make tomorrow, regardless of whether their role is purely technical or not, to accelerate their data science impact and results and increase their business exposure?
[00:20:47] Andrei Oprisan: I would say, ask, those deeper questions and figure out exactly the kind of work that they’re doing, how it’s having an impact on the bottom line. Whether it does or not, I think, understanding that very well understanding whether or not, the group that you’re in and the division is seen as a cost center or not or revenue center.
[00:21:05] I think that’s the biggest sort of eye opening question that you can get answered and figure out, what are the broader objectives? Well, there are technical objectives. That the team has or business objectives that the whole division has and figuring out, okay, am I playing a part in that today or not?
[00:21:26] Are we directly or indirectly? And how are my bosses or my bosses, bosses seeing the impact of the work that I’m doing in relation to the business success? And if there is no pathway for that, I think it’s the wrong kind of role in terms of long term growth. So again, if the work that you’re doing doesn’t have a measurable impact on that bottom line or on the growth of the organization, I think it’s worth asking deeper questions as to why that is or why it’s seen that way and how you can get into the kind of role that can help it.
[00:22:03] With the growth and resiliency of the business.
[00:22:06] Dr Genevieve Hayes: For listeners who want to get in contact with you, Andre, what can they do?
[00:22:10] Andrei Oprisan: Sure. Can email me at Andre at agent.ai. Can find me on the web at oprisan.com. My blog is linked there as well. I’m on LinkedIn and x and. All the social networks with the same handles but more importantly, just, find me on agent. ai where I spend most of my time building AI agents helping out in the community giving folks feedback on how to build better agents.
[00:22:35] And ultimately aiming to democratize AI and make it more accessible.
[00:22:40] Dr Genevieve Hayes: And there you have it, another value packed episode to help turn your data skills into serious clout, cash, and career freedom. If you enjoyed this episode, why not make it a double? Next week, catch Andre’s value boost, a five minute episode where he shares one powerful tip for getting real results real fast.
[00:23:01] Make sure you’re subscribed so you don’t miss it. Thank you for joining me today, Andre.
[00:23:05] Andrei Oprisan: Thank you. Great to be here.
[00:23:07] Dr Genevieve Hayes: And for those in the audience, thanks for listening. I’m Dr. Genevieve Hayes, and this has been Value Driven Data Science.
The post Episode 58: Why Great Data Scientists Ask ‘Why?’ (And How It Can Transform Your Career) first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 57: [Value Boost] 3 Game-Changing Questions to Save Your Data Science Presentations From Falling Flat
Every data scientist knows the sinking feeling: you’ve done brilliant technical work, but your presentation falls flat with stakeholders.
In this Value Boost episode, communications expert Lauren Lang and data analyst Dr Matt Hoffman join Dr Genevieve Hayes to share their go-to pre-presentation checklist to ensure that sinking feeling never happens again.
You’ll walk away knowing:
Lauren Lang is the Director of Content for Uplevel and is also a Content Strategy Coach for B2B marketers.
Dr Matt Hoffman is a Senior Data Analyst: Strategic Insights at Uplevel and holds a PhD in Physics from the University of Washington.
[00:00:00] Dr Genevieve Hayes: Hello and welcome to your value boost from value driven data science, the podcast that helps data scientists transform their technical expertise into tangible business value, career autonomy and financial reward. I’m Dr. Genevieve Hayes, and I’m here again with communications expert, Lauren Lang, director of content at Uplevel, and data analyst, Dr.
[00:00:24] Matt Hoffman, product manager at Uplevel, to turbo charge your data science career in less time than it takes to run a simple query. In today’s episode, we’re going to be discussing the most important questions data scientists can ask themselves before presenting any models or analysis to maximize the business impact of their work.
[00:00:47] So welcome back Lauren and Matt.
[00:00:50] Lauren Lang: Thank you. Glad to be back.
[00:00:52] Dr Matt Hoffman: Hello again.
[00:00:53] Dr Genevieve Hayes: In the longer conversation we had in our previous episode, we did a deep dive into a project the two of you recently collaborated on at Uplevel, where you combined And that’s data insights with Lauren’s communication expertise to produce significant business results.
[00:01:11] After hearing your story, it made me wish that I had had the benefit of working with communications experts in the various jobs I’ve had. Unfortunately, many of the organizations I’ve worked for didn’t even have an internal communications team, or if they did, it wasn’t in their job description to assist the data science team in crafting their message.
[00:01:34] Because of that, I’ve found myself creating a mental checklist of questions that I’d ask myself before presenting my work to increase my chances of success. So what I’d like to ask each of you today is what is one essential question that data scientists Can ask before delivering a presentation to transform how their analysis lands.
[00:02:04] So Lauren, as a communications expert, what would your one essential question be?
[00:02:10] Lauren Lang: One essential question would be, what are the business goals or OKRs that the company is currently facing? What is the single biggest Problem or initiative that the company is rallying around right now, and the reason for that is this is the world in which your audience is living in when you are doing a presentation
[00:02:37] you have to understand who you’re talking to and understand what their concerns are. And what is keeping them up at night? And what are they thinking about? And what are they prioritizing? And I think sometimes we present data, but we don’t connect it back to the actual business value that our work is bringing.
[00:02:56] And I think that that’s a. Negative, not only for the business, but for us as the presenters of information, it’s not showing our value in what we are contributing to the business. So, I think the more that you can contextualize how the work that you’re doing rolls up or connects to these larger business initiatives is just really important.
[00:03:18] And not only knowing that yourself, but making that very explicit, to the people that you’re communicating to do they know could be a secondary question. You asked for 1, I’m giving you 2. Do they know how relevant or important? My work is to the goals of the business. Because sometimes they don’t, sometimes when things get a little bit abstract or a little bit technical or out of the general wheelhouse of what executives understand, they may not understand what goal.
[00:03:49] You are trying to roll the work up into, so I think it’s really important to just make sure that you are communicating that clearly to them, even if you are very clear about what business value you’re bringing.
[00:04:01] Dr Genevieve Hayes: Yeah. From my experience, I’ve seen a lot of data scientists give a presentation, which is, Hey, look at this cool model I’ve built, but they never say, and if you use this cool model I’ve built, this will save you a hundred thousand dollars per year in this expense or whatever the key metric is. They always.
[00:04:22] Forget that final step.
[00:04:24] Lauren Lang: Right, and that’s the one step. You never want to drop because that’s the one that gets people really excited about what you do and helps to show your value that you’re bringing to the organization.
[00:04:35] Dr Genevieve Hayes: So turning to you, Matt, from your experience as a data analyst, what would your one essential question be?
[00:04:42] Dr Matt Hoffman: My question would be, what’s the life cycle of whatever artifact I’m using to present on? So I’ll give you an example. If it’s going to be a presentation that I’m going to go do, and I’m standing side by side, I can have slides that are Very minimally supported because I am the voice that’s going to be sharing the information that users need to have to understand the context of the work and all of that.
[00:05:11] If that deck is going to get shared out to other people asynchronously, now it’s insufficient. It doesn’t have enough explanation. It can’t be used later. Similarly, if I write a paper. It needs to be concise enough that an executive audience could read it, skim it in a minute, and be able to get some of the takeaways.
[00:05:31] So really, what is the life cycle of whatever we’re creating? Who’s going to be using it? What’s their experience? What’s their technical expertise with the subject matter? By really empathizing and understanding the scope of your entire audiences really helps you make much more impactful presentations and artifacts that can support your work.
[00:05:54] Setting aside how hard the work it was to do itself. I would also add that understanding that at the very beginning of your project as you’re even building your models, building your data analyst, really understanding the business problem, the business context and your users doing that throughout really helps you make more impactful data science work as well as presented out.
[00:06:19] Dr Genevieve Hayes: So basically know your audience.
[00:06:21] Dr Matt Hoffman: Know your audience and know the context that matters to them. And I would extremely advise that data scientists get involved in the conversations themselves and are in the rooms where some of these decisions happen to really understand their users and their audience the best.
[00:06:39] Dr Genevieve Hayes: For me, the one essential question would have to be so what, which is basically building on what both of you have just said. That’s something a former boss of mine used to always ask about my work. So, I would be the data scientist who went to him with, Hey, look at this cool model that I built.
[00:06:57] And before I even got to presenting to the board or the executive, he would always say to me, Hmm, that’s really nice. So what? And so that got me into that habit of, just taking the final step and saying, So. This is a cool model and so it will save you lots and lots of money,
[00:07:18] and yeah, big surprise once I started asking myself that the presentations that I was giving ended up being more successful. So, that is my one essential question so, in summary, the three questions we’ve got that every data scientist should ask before , every presentation what are the key metrics that your stakeholders are focused on Who are your stakeholders that will be reading whatever you produce from your work and so what?
[00:07:52] How do you connect your work back to those key metrics for those stakeholders that you’ve identified? And sounds like if you do that, then you’re on a path to a winning, successful deliverable.
[00:08:08] Lauren Lang: I like it. Yeah.
[00:08:11] Dr Genevieve Hayes: Okay, so that’s a wrap for today’s value boost, but if you want more insights from Lauren and Matt, you’re in luck. We’ve got a longer episode with them where we dive deeper into their strategies for transforming complex technical findings into compelling business narratives. And it’s packed with no nonsense advice for turning your data skills into serious cloud cash and career freedom.
[00:08:38] You can find it now wherever you found this episode or on your favorite podcast platform. Thanks for joining me again, Lauren and Matt.
[00:08:48] Lauren Lang: Thank you so much.
[00:08:49] Dr Matt Hoffman: Thanks for having us.
[00:08:50] Dr Genevieve Hayes: And for those in the audience, thanks for listening. I’m Dr. Genevieve Hayes, and this has been Value Driven Data Science.
The post Episode 57: [Value Boost] 3 Game-Changing Questions to Save Your Data Science Presentations From Falling Flat first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 56: How a Data Scientist and a Content Expert Turned Disappointing Results into Viral Research
It’s known as the “last mile problem” of data science and you’ve probably already encountered it in your career – the results of your sophisticated analysis mean nothing if you can’t get business adoption.
In this episode, data analyst Dr Matt Hoffman and content expert Lauren Lang join Dr Genevieve Hayes to share how they cracked the “last mile problem” by teaming up to pool their expertise.
Their surprising findings about Gen AI’s impact on developer productivity went viral across 75 global media outlets – not because of complex statistics, but because of how they told the story.
Here’s what you’ll learn:
Dr Matt Hoffman is a Senior Data Analyst: Strategic Insights at Uplevel and holds a PhD in Physics from the University of Washington.
Lauren Lang is the Director of Content for Uplevel and is also a Content Strategy Coach for B2B marketers.
[00:00:00] Dr Genevieve Hayes: Hello, and welcome to Value Driven Data Science, the podcast that helps data scientists transform their technical expertise into tangible business value, career autonomy, and financial reward. I’m Dr. Genevieve Hayes, and today I’m joined by Lauren Lang and Dr. Matt Hoffman. Lauren is the Director of Content for Uplevel and is also a Content Strategy Coach for B2B marketers.
[00:00:26] Matt is a Data Analyst and Product Manager at Uplevel and holds a PhD in Physics from the University of Washington. In this episode, we’ll uncover proven strategies for transforming complex technical findings into compelling business narratives that drive real organizational change. So get ready to boost your impact, earn what you’re worth, and rewrite your career algorithm. Lauren, Matt, welcome to the show.
[00:00:55] Lauren Lang: Hi Genevieve, thank you so much.
[00:00:57] Dr Matt Hoffman: Thanks for having us. Excited to be here.
[00:01:00] Dr Genevieve Hayes: In logistics, there’s a concept known as the last mile problem. Which refers to the fact that the last stage of the delivery process of people or goods is typically the most complex and expensive while also being the most essential. For example, it’s typically easier and cheaper to fly a plane full of packages from Australia to the U.
[00:01:22] S. than it is to transport those packages by road to their final destinations within the U. S. Yet if you can’t distribute those packages once they arrive in the U. S., they may as well have never left Australia. It’s for this reason that supply chain managers typically focus a disproportionate amount of effort on planning those final miles.
[00:01:43] Data scientists also face their own last mile problem. Despite many data science projects requiring sophisticated modelling and analysis techniques, the most difficult part of data science is often communicating the results of those projects to senior management and gaining adoption of the project from the business.
[00:02:04] That is the final stage. Yet, unlike in logistics, This is also the stage where data scientists typically focus the least amount of effort, much to the detriment of their work and their careers. Lauren and Matt, the reason why we’ve got both of you as guests in today’s episode is because you’ve recently backed this trend and pooled your combined experience in communications and data science with outstanding results.
[00:02:33] And this is actually the first time I’ve come across a data scientist working directly with the communications expert to address the data science last mile problem. Although, it probably should be far more common. So to begin with, Matt, can you give us an overview of the data science project you were working on and how you came to team up with Lauren when delivering the results?
[00:02:57] Dr Matt Hoffman: So we work at Uplevel and Uplevel is a company that pulls in data about software engineers and we help tell those data stories to our customers. Senior leaders of engineering, like software engineering firms so that they can make data driven decisions and drive change within their organizations.
[00:03:17] One of the things that’s really come up in the past year is this full topic of gen. AI software engineers being able to talk to an AI assistant to help them write code and the thinking was, oh, this is a silver bullet. We’re just going to be able to. Turn on this system. Our developers are going to be more productive.
[00:03:36] Instantly. The code is going to get better. There’s going to be nothing but greenfield. If we just turn this on, it’s a no brainer, we heard those questions and we don’t develop our own gen AI tool. But what we do have is data about software engineers and how they spend their time, the effectiveness of their work.
[00:03:54] Are they able to deliver more? Are they getting more things done? How’s the bug rate of their code? So it was natural for us to go explore that problem and really try to understand what is the impact of Gen AI on software engineers. That’s the problem that we were facing. So I work with our data science team.
[00:04:13] I’m not actually on our data science team, but worked with them to go do this analysis to really try to understand how do people compare to themselves and what changes do we see within this. And then we pulled in Lauren to go start showing off what we found. And that’s where that story kicked off.
[00:04:32] Dr Genevieve Hayes: Prior to working with Lauren, what are some of the challenges you encountered in communicating the results of your analysis?
[00:04:38] Dr Matt Hoffman: Well, it’s always a tricky one when the answer is complicated. The real fundamental place that we at Uplevel are at is that this is human data. While we may be able to measure timestamps to a millisecond, This is all still predicated that this is people data and people do weird things. And the data is messy and the data is muddy.
[00:05:03] So there’s the constant battle of, well, what can we trust? We’re looking for correlations and, you know, you squint to see if like, there’s something there you peel back a layer and then there’s something more, but people data is hard to work with. So that’s really a skill of our data science team to help pull that back.
[00:05:20] But we were. Kind of struggling to make heads and tails of what were the real conclusions. And Lauren really helped clarify that story for us and get that communication there.
[00:05:30] Dr Genevieve Hayes: People are irrational. I mean that’s the big problem with us. Before you did this, had you ever made some massive mistake because you just assumed people were rational when they worked?
[00:05:44] Dr Matt Hoffman: It’s funny stuff so sometime when some work’s becoming delayed and you go ask for the root cause and it’s like, oh, someone’s saying, I thought I did that and I forgot. Like, I never hit the button. That’s the kind of, people data that we see is that, like, yeah, that happened.
[00:05:59] It was late, but that was just because you forgot to hit the button. People’s behavior is really funny. So yeah, we just have to kind of take that into account that everybody’s different. That’s okay. And we need to bake that into our analysis, that people work differently and not try to over fit one model that applies to everybody .
[00:06:18] Dr Genevieve Hayes: Yeah, I actually wrote a LinkedIn post a while ago saying, people are a problem with data and wouldn’t it be nice to just be dealing with mechanical processes? And I had someone reply to that post who works at a water agency where they don’t deal with people, it’s, water going through pipes, and they said, well actually mechanical processes are just as annoying, they just are annoying in different ways because you have the sensors malfunctioning and all this.
[00:06:44] You can dream about not dealing with people but Machines cause problems too .
[00:06:48] Dr Matt Hoffman: Yeah, that’s exactly right. So you just have to know that going in and know that it’s going to be messy. And plan for that.
[00:06:56] Dr Genevieve Hayes: So Lauren, in your content strategy coaching work you’ve done a lot of work with software as a service companies. And as Matt said, Up Level itself is a company that Works with engineers and probably has a lot of engineers as its employees. So, I’d imagine you’ve worked with a lot of very technical people throughout your career.
[00:07:20] Lauren Lang: I have. Yes.
[00:07:21] Dr Genevieve Hayes: What are some of the biggest issues you’ve noticed in how technically minded people, especially data scientists and data analysts, present their findings to business stakeholders?
[00:07:33] Lauren Lang: It’s very funny because I think that there is a lot of similarities actually between how data scientists might present their findings and how a lot of marketers present their findings. And you would think like, Oh, marketing is so much more. We have our thumb on the pulse of the business.
[00:07:48] And, marketers are so much more business driven, but I think, anyone who is looking at data as marketers, we look at data too. We are. Not data scientists, but there’s a fair amount of data science, sometimes in marketing. And there’s a lot of data analysis that happens. And I think there is just this tendency sometimes to.
[00:08:07] Get very myopic and get very focused on your own specific context in looking at the data and forgetting that there is probably a larger story that the data existed to tell. I see this a lot. 1 of the. Challenges that I see a lot is, marketers will go into a meeting with a CEO and they will have dashboard after dashboard and chart after chart.
[00:08:31] And there is a very sort of distinct look on an executive space when. You’ve shown them three charts in a row or three dashboards and it’s like a completely blank look and you know that they are literally anywhere else. but in the conversation and it’s a little bit of like a death now.
[00:08:51] And so I think for anyone who likes to geek out on data, whatever part of the business you’re in, you have to remember that there is this larger value story that you need to be telling, and you need to be showing that data and be mindful of the context in which you’re showing that data.
[00:09:08] To what end? Rather than just taking people down the rabbit hole with you. I think sometimes there’s an assumption that everyone should be as interested about all of the nuances and slight, variances in the data as you are, and that’s not always the case.
[00:09:24] Dr Genevieve Hayes: Yeah the way you’re describing that death knell face, yeah, I’ve seen that before. And worse than that is when the people you’re presenting to start playing with their phones. Then you definitely know that you’ve failed.
[00:09:35] Lauren Lang: Might as well call it right there.
[00:09:37] Dr Genevieve Hayes: Yeah, , just pack up and walk out of the room at that point.
[00:09:39] Lauren Lang: That’s right. That’s right.
[00:09:42] Dr Genevieve Hayes: So, I assume you’ve pointed out these issues to technical people who you’ve worked with. How do they typically respond when you say, hey, not everyone’s as geeky as you?
[00:09:53] Lauren Lang: I think there’s a way to couch that in a way, because I have a lot of empathy for it. Geeky people are excited about what we do. I mean, there’s a passion there. And so you don’t want to not communicate that passion.
[00:10:05] I think that’s really important. And, there’s some exciting results or, even. Not exciting results that you didn’t think were going to pan out, but there’s always a story to tell, but it’s just, can you tell it maybe at a slightly more abstract level of specificity, maybe? Or can you tell it with an understanding of the context in which your audience exists
[00:10:28] I think there’s just a lot of tendency to Just forget that not everyone brings the same experiences and the same understanding and the same depth of knowledge to the table. And so the best way that the stories we tell with data can be impactful is to tell them in context and to be able to pull out the important parts that really can bring the message home.
[00:10:50] Dr Genevieve Hayes: So, put yourself in the shoes of your audience,
[00:10:53] Lauren Lang: absolutely. You should always have empathy with the person you’re trying to communicate to. I think it was Kim Scott said that communication happens at the listener’s ear and not the speaker’s mouth. That’s where meaning is made. It’s really important to keep that in mind as you are stepping into the shoes.
[00:11:09] Of the communicator,
[00:11:11] Dr Genevieve Hayes: so, I’d like to now take a deep dive into the project that the two of you collaborated on so Matt, how did you determine which insights from your analysis were most relevant for communicating with management? Are
[00:11:24] Dr Matt Hoffman: So we have a set of measures at up level that are kind of part of our standard suite of analysis. So 1st, because if you can’t go explore the data for yourself and understand where your team’s at, then that’s a really unsatisfying experiment. So we knew that we wanted to look at some of these measures.
[00:11:43] We’ve also been doing this for a few years now, so we do have a pretty good grasp on. You know, what are appropriate measures to look at for software engineers? And then what is completely inappropriate? That’s like, this is just not a good measure. You shouldn’t use it. It’s problematic for 1 reason or another.
[00:12:01] So choosing those measures that we think. Are kind of universally applicable, are good proxies of how this experience may look, and then really trying to see what’s going to move and shift when we look at these. Those were kind of the criteria. We had a few hypotheses that we went in for how we thought things were going to move once you introduced Gen AI to the mix.
[00:12:22] And we were surprised by our hypotheses, and we had to reject some of them, which was really fun. And it makes you really challenged that you’re doing it right. And then finding that this actually does go against what we thought would happen.
[00:12:36] Dr Genevieve Hayes: you able to share any examples of these?
[00:12:39] Dr Matt Hoffman: One of the things that we wrote about and we can share the link to our study was the general thinking was, hey, if you’re going to use Gen AI, you’re going to be able to ask questions and Jenny is going to help you write better code. So one of the things we looked at was. What’s the defect rate of code that gets merged and then it needs to get fixed later?
[00:13:02] So how often does that happen? You would think that that would go down if the code is going to be of higher quality because Gen AI is helping you. Now what we found was that actually the defect rate went up. Another organization seemed to find the same thing, saying that the result of Gen AI was that there’s larger changes to code.
[00:13:23] And then more things are going to get missed because the batch size is getting larger. So you might find things. four bugs, but there’s five because you’re writing bigger and bigger code changes. So we saw that the defect rate for the cohort that was using Gen AI went up by 40 percent compared to themselves, which is a pretty market change.
[00:13:43] So that was one that , we were very surprised to see and are really interested to see what happens next with that as all these tools get better and better and better.
[00:13:53] Dr Genevieve Hayes: insight you just described, that doesn’t surprise me because my own personal experience I’ve found with writing code using Gen AI, you can produce the code really, really fast. You’re spending. twice as long or three or four times as long debugging it, because there are all these bugs in it that would not be in there if you’d written it yourself.
[00:14:14] And you’re just not used to having that many bugs to fix.
[00:14:19] Dr Matt Hoffman: Yeah, and it might be not stylistic, like, the way that you think that you should write your code it might pull some solution that looks reasonable at first pass, but it’s pretty hard to debug if it’s the right thing when it, looks right, smells right, but then under the hood, there’s something wrong with it.
[00:14:36] Also, Jenna, I doesn’t understand the context of the problem that you’re trying to go write code for. You have that in your head, you know where you’re at and where the destination is, and it’s going to help you write some code. But you have that.
[00:14:49] Dr Genevieve Hayes: Yeah. And I’ve found it creates. Non existent Python packages and non existent Python functions, which is fun, because then you spend half an hour trying to find this package that doesn’t even exist.
[00:15:02] Dr Matt Hoffman: It’s tricky. It really is. The other one that I would just briefly say that we looked at is we thought people would write code faster. That’s the statement that you just said. How quickly does it take to get from commit to merge? Does that really pick up? Because you’re using Gen AI.
[00:15:16] And we found that it didn’t make much of a tangible impact. That there’s still a lot of time that’s spent when you’re trying to understand the problem of what you’re trying to solve, how you might approach it, the architecture of it. None of those things are going to go away.
[00:15:31] Bottlenecks of having another human review your code, that doesn’t change whether they both have Gen AI or not. You’re still working with other people. So those structural factors do tend to be very important in this problem. And those are ones that you need to pursue and kind of conventional means of understanding how your teams work and doing better.
[00:15:51] So that one didn’t move at all. And we thought that that would speed up. That was our hypothesis.
[00:15:56] Dr Genevieve Hayes: Yeah, doesn’t surprise me. So, Lauren, how did you take these insights and structure them into a narrative that maximized their impact?
[00:16:04] Lauren Lang: well, it was funny because even before we had done the research, we knew we wanted to do this research and we wanted to publish it. And looking from a content marketing perspective, I think original research right now is one of the most, potentially impactful formats for creating content.
[00:16:23] And some of that is that, there is so much out there. That is just really bland. And I is not helping. Jenna is not helping with that. There’s a lot of content. That is just not special. It’s not differentiated. It’s not helping to educate or inform anybody or share anything new. And so when you have the opportunity to sort of lend something new to the conversation, that’s an important opportunity.
[00:16:46] So we knew going in that we were going to do it. What we were not expecting were the results that we got. And I laughed a little bit when we got these results. I had a meeting with our data science team and with Matt, and., we all are sitting down and I’m like, lay it on me tell me what the results were and they were a little bit disappointed and they said, it’s kind of we’re not seeing, a big thing from Impact perspective or a data perspective, like, it’s just not that exciting.
[00:17:15] And I said, oh, no, actually, this is very exciting because there were a number of factors. I think that really made this a really impactful report. 1st was just having some new original research on this topic. That is maybe the hot topic of the decade.
[00:17:31] I think was really exciting. So it was like, listen, we know that people are very interested in this. We know that this is the question that they are asking, especially engineers and engineering leaders, the people who we serve from a business standpoint. They want to know is gen AI actually helping my developers be more productive.
[00:17:48] And we have like some. Things that we can show around that. And then also the fact that we were able to then bring a little bit of a spiky and contrarian point of view about this because a lot of the research that’s been published already is either survey based. So, a lot of developers reporting whether or not they feel more productive.
[00:18:11] Which is data as well, but, this is we’re bringing some quantitative data to bear or some of the other data was published by the. AI tools themselves, so you have to take that with a grain of salt. So, we came in
[00:18:27] with this sort of interesting and different point of view. And that really, really took off for folks. And we found that some people were surprised. We found a lot of developers and engineers like you, Genevieve, who are not who said, I have been saying this all along. And this feels very validating because I think there is some anxiety among engineers that, Hey, like leadership just thinks that can be replaced.
[00:18:50] But it really kicked off a really big conversation in the industry where we just said, Hey, you know, there’s a little bit of a hype cycle right now. We don’t know for sure. , we have results from one sample. There’s no big claims that we can make about the efficacy in the long run.
[00:19:06] And things change very quickly. Gen AI is improving all the time, but. We do have some data points that we think are interesting to share and it really took off and it was great for us from a business perspective. It really helped take the work that we do into that last mile. And it helped make the work that we do feel very tangible and accessible for folks.
[00:19:29] Dr Genevieve Hayes: So it sounds like, rather than taking a whole bunch of statistics and graphs, which would have been the output of Matt’s work. You translated those statistics and graphs into a narrative that could be understood by a person who wasn’t a data scientist or wasn’t a data analyst. Is that right?
[00:19:49] Lauren Lang: Yes, we did. And our audience is primarily engineering leaders, engineering leaders are not data scientists, but they’re technical. So we identified three main takeaways. And we presented that we shared a little bit about our methodology.
[00:20:03] And we shared essentially Some thoughts about what does this mean, what is the larger significance of what we found? What does this mean for you as an engineering leader does this mean that we think that you should stop adopting AI?
[00:20:17] Does it mean that, right?, you should be more controlling of how your engineers are experimenting with AI. And, we don’t believe that’s the case at all. But it allowed us to sort of share some of our perspective about, how you build effective engineering organizations and what role we think I may have to play in that.
[00:20:35] And, that is the larger story where data becomes very interesting because there’s sharing the data and then they’re sharing the so what around the data. So, what does this mean for me as an engineering leader? And so we really tried to bring those 2 elements together in the report.
[00:20:51] Dr Genevieve Hayes: How was this report ultimately received by the audience?
[00:20:55] Lauren Lang: Very well. We issued a press release around it. And I think we were picked up globally by somewhere between 50 and 75 media outlets, which. For a small engineering analytics platform, I’m pretty happy about that. It was in some engineering forums, it really became a big topic of discussion. We went sort of medium level viral. And it felt really good. It’s like, this is a really interesting topic. We accept that it’s an interesting topic.
[00:21:22] We think that we have something that is very interesting to add to the conversation. So, yeah, it was good and some folks to it was great, you know, because engineering leaders are naturally skeptical. This is 1 of the most fun parts about marketing to engineering leaders that engineering leaders hate marketing.
[00:21:38] So we got a few emails of folks who are like, tell us more about your methodology. And they really sort of wanted to, see behind the scenes and really, really dig in. And, that is par for the course. And we would expect nothing less
[00:21:51] It was a really positive impact. I’m really glad we did it.
[00:21:53] Dr Genevieve Hayes: So with all that in mind, I’d like to ask this of each of you. What is the single most important change our listeners could make tomorrow to accelerate their data science impact and results?
[00:22:05] Dr Matt Hoffman: I. am very fortunate to have Lauren as an editor even when we collaborate on writing, an article I think having someone who can help you clarify and simplify your story is so important. You really do want to edit and bounce back and forth and try to distill down the most important bits of what you’re doing.
[00:22:28] I tend to want to share, like, Everything, all of the details, all the gritty stuff, the exact perfect chart and it’s like, let’s simplify, simplify, simplify. And part of that conversation is also, who’s going to be receiving this? And what’s their persona? At what level are we going to explain this work?
[00:22:47] Are they going to be familiar with, the methodology that we’re using? Or do we need to explain that too? So, how do we write everything at the most appropriate level and understand the life cycle of? This report that we’re doing. So having an editor would be my big one and understanding your audience would be the other.
[00:23:06] Lauren Lang: I absolutely agree with everything Matt said. I think that the more that you make Sharing the results of your research, a team effort and a team sport, the more you’re likely going to succeed at it. But I think probably, and I’ll just come at it from, more of a technical perspective.
[00:23:23] When you are presenting information, 1 of the things that could be very helpful is to present it at various levels of detail. So, making sure that you are presenting key takeaways or abstracts at 1 level and then. People can always double click into things and dive deeper and, you can include appendices or include links to , more of the detailed research.
[00:23:47] But I think sort of having these executive summaries and really sort of being able to come at things from a very high level Can help sort of get that initial interest so that people understand quickly. what did the research find? What is the impact? And what is the context that this research was performed in?
[00:24:06] Where is the business value, so, being able to connect the dots for your audience in terms of not only did we find this, but here’s what it means. And that thing that it means is actually very impactful to you and the job that you are trying to accomplish .
[00:24:19] Dr Genevieve Hayes: So for listeners who want to get in contact with each of you, what can they do?
[00:24:23] Lauren Lang: I live on LinkedIn. So they can look me up on LinkedIn. I think my little handle there is ask Lauren Lang.
[00:24:31] Dr Matt Hoffman: Likewise, I don’t know what my LinkedIn handle is, but I’m on there. That would be the easiest way to get a hold of me on that.
[00:24:39] Lauren Lang: You obviously need to spend more time on LinkedIn than Matt.
[00:24:42] Dr Genevieve Hayes: Yes. And there you have it. Another value packed episode to help turn your data skills into serious clout, cash, and career freedom. And if you enjoyed this episode, why not make it a double? Next week, catch Lauren and Matt’s Value Boost, a five minute episode where they share one powerful tip for getting real results real fast.
[00:25:08] Make sure you’re subscribed so you don’t miss it. Thanks for joining me today, Lauren and Matt.
[00:25:12] Lauren Lang: Thank you so much for having us.
[00:25:14] Dr Matt Hoffman: Thank you. It was really lovely.
[00:25:16] Dr Genevieve Hayes: And for those in the audience, thanks for listening. I’m Dr. Genevieve Hayes, and this has been value driven data science.
The post Episode 56: How a Data Scientist and a Content Expert Turned Disappointing Results into Viral Research first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 55: [Value Boost] Why Data Scientists are Focus-Poor (and the Software Developer’s Solution to Fix It)
Have you ever noticed that software developers are frequently more productive than data scientists? The reason has nothing to do with coding ability.
Software developers have known for decades that the real key to productivity lies somewhere else.
In this quick Value Boost episode, software developer turned CEO Ben Johnson joins Dr Genevieve Hayes to discuss the focus management techniques that transformed his 20-year development career – which you can use to transform your data science productivity right now.
Get ready to discover:
Ben Johnson is the CEO and Founder of Particle 41, a development firm that helps businesses accelerate their application development, data science and DevOps projects.
[00:00:00] Dr Genevieve Hayes: Hello and welcome to your value boost from value driven data science. The podcast that helps data scientists transform their technical expertise into tangible business value, career autonomy, and financial reward. I’m Dr. Genevieve Hayes, and I’m here with Ben Johnson, CEO and founder of Particle 41 to turbocharge your data science career in less time than it takes to run a simple query.
[00:00:29] In today’s episode, we’re going to be discussing techniques from software development that data scientists can use to increase their productivity and efficiency. Welcome back, Ben.
[00:00:42] Ben Johnson: Hey, nice to be here.
[00:00:44] Dr Genevieve Hayes: As long time listeners of this show are probably already aware, before becoming a data scientist, my background was as an actuary and statistician.
[00:00:53] And then when I decided to make the move to data science, I did a master’s in computer science to upskill on machine learning and AI. And one of the things I loved most about my master’s was that my classmates were predominantly software developers and engineers. And I found that Just by being in the same classes as them and associating with them on the class online forums, I learned just as much, if not more, about what it takes to be an effective data scientist as I did from the lectures themselves.
[00:01:32] And this is because the software engineers had a very different perspective on data problem solving from what I’d developed as a statistician and actuary. Ben, in addition to being a serial entrepreneur, you yourself are a software developer with over 20 years of experience. In that time, you must have come across a whole range of techniques for boosting your productivity and efficiency as a developer.
[00:02:02] Are there any techniques among those that, you’re surprised, data scientists don’t also use?
[00:02:09] Ben Johnson: It kind of swirls together. So focus is a currency as kind of the tagline here. So the book, the one thing has been really inspirational for me. And I’m a bullet journaler. And so I kind of take my 90 day goals and break them down into months and then the weeks, you know,
[00:02:26] what’s the one thing or the finer sets of things? I find a lot of digital professionals, including data scientists are kind of multitasking and we’ve kind of even created This kind of interruption culture in the way that we work. So I find it interesting when data scientists don’t have like the Kanban board or the flow of work and they’re just kind of operating by slack messages and emails.
[00:02:50] And I think then you have Low currency of focus like you’re poor in focus. And so the overarching thing here is to be rich in focus. And that means creating systems and work environment and a personal organization strategy. That makes you richer in focus.
[00:03:07] Dr Genevieve Hayes: And how would you go about doing that?
[00:03:09] Ben Johnson: So I think it starts with like some level of personal ceremony.
[00:03:14] And some adherence to routine. So it may seem confining, but I actually find it gives me a lot of freedom. So spend a lot of time around the quarter. Thinking like, what do I want to accomplish in the next 90 days and documenting that and then breaking that out in a month and not just doing it professionally, but doing it personally as well.
[00:03:34] So that then when I go to my week, I’ve kind of planned my week. I know what my focuses are for at least some of the time. I don’t like knock it all down in stone. I leave some flex time in there for. Emails and slack messages, but I definitely know what needs to be true by the end of the week for me to feel accomplished and confident.
[00:03:57] And in the end, the biggest enemy is the imposter syndrome, right? So I have to have to put challenges in front of me that I’m accomplishing. Because the last thing I want anybody on my team to feel is that imposter syndrome. And the only way we were get through that is by. Proving to ourselves that we can accomplish the goals that we put in front of ourselves.
[00:04:19] Dr Genevieve Hayes: What you’ve described there is very similar to the approach that I take in my work. I read Cal Newport’s deep work about, three years ago. Yeah, and one of the things I find, you know, as a data scientist, often I do have multiple projects on the go. But I try and work in deep work blocks, so I schedule three two hour blocks per day, and I actually have a kitchen timer, and for that two hour block, I will only work on one particular task, and even if I’m working on multiple topics within a day.
[00:04:55] I try and only have one task per day, but just having those two hour focus blocks really helps me to accomplish a lot.
[00:05:03] Ben Johnson: Yeah, I think so. And what you’re talking about there is this time compression and I think time compression is very, very powerful. And I would say most people don’t. Incorporate an element of time compression, like your timer is time compression and incorporate environment. We kind of used to be.
[00:05:23] We planned the year and we give very little cadence to the quarter and the month. And then we kind of realized. You know, Q3 we’re falling behind and then that would make for these awful Q4 experiences, right? People working right up into the last day of the year kind of thing. I think we’re seeing that improve and I think time compression, EOS is really big on the quarterly planning, the monthly planning.
[00:05:50] And then you mentioned like the Pomodoro technique. These things are getting really popular, but those things are awarded by an increase. Like when you’re rich in focus, those things happen, right? Or you do those things to become more rich in focus.
[00:06:06] Dr Genevieve Hayes: And my experience is the days when I do manage to have those focus blocks, I’m happier at the end of the day.
[00:06:12] Ben Johnson: Yep. Yeah, because you created a scoreboard and you won the day, right? You know, you won the day. Yeah. In my bullet journal, I have a habit tracker and I put so many habits on there that if I do about half of them, like I’m good, and that works for me, you know, kind of always be solving.
[00:06:28] You know salespeople, they always be closing and I’m kind of like always be doing something to make my life better, even if it’s just like drinking water, right? Remembering to drink water that’s a thing on my tracker.
[00:06:42] Dr Genevieve Hayes: And that’s a wrap for today’s Value Boost. But if you want more insights from Ben, you’re in luck. We’ve got a longer episode with Ben where we discuss strategies for accelerating your data science impact and results. And it’s packed with no nonsense advice for turning your data skills into serious clout, cash, and career freedom.
[00:07:04] You can find it now, wherever you found this episode, or at your favorite podcast platform. Well, thank you for joining me again, Ben.
[00:07:12] Ben Johnson: Oh, my pleasure.
[00:07:14] Dr Genevieve Hayes: And for those in the audience, thanks for listening. I’m Dr. Genevieve Hayes, and this has been Value Driven Data Science.
The post Episode 55: [Value Boost] Why Data Scientists are Focus-Poor (and the Software Developer’s Solution to Fix It) first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 54: The Hidden Productivity Killer Most Data Scientists Miss
Why do some data scientists produce results at a rate 10X that of their peers?
Many data scientists believe that better technologies and faster tools are the key to accelerating their impact. But the highest-performing data scientists often succeed through a different approach entirely.
In this episode, Ben Johnson joins Dr Genevieve Hayes to discuss how productivity acts as a hidden multiplier for data science careers, and shares proven strategies to dramatically accelerate your results.
This episode reveals:
Ben Johnson is the CEO and Founder of Particle 41, a development firm that helps businesses accelerate their application development, data science and DevOps projects.
[00:00:00] Dr Genevieve Hayes: Hello and welcome to Value Driven Data Science, the podcast that helps data scientists transform their technical expertise into tangible business value, career autonomy, and financial reward. I’m Dr. Genevieve Hayes, and today I’m joined by Ben Johnson, CEO and founder of Particle 41, a development firm that helps businesses accelerate their application development, data science, and DevOps projects.
[00:00:30] In this episode, we’ll discuss strategies for accelerating your data science impact and results without sacrificing technical robustness. So get ready to boost your impact. Earn what you’re worth and rewrite your career algorithm. Ben, welcome to the show.
[00:00:48] Ben Johnson: Yeah, thank you for having me.
[00:00:50] Dr Genevieve Hayes: One of the most common misconceptions I see about data scientists is the mistaken belief that their worth within a business is directly linked to the technical complexity of the solutions they can produce.
[00:01:04] And to a certain extent, this is true. I mean, if you can’t program, fit a model, or perform even the most basic statistical analysis, realistically, your days as a data scientist are probably numbered. However, while technical skills are certainly necessary to land a data science job, The data scientists I see making the biggest impact are the ones who are not necessarily producing the most complex solutions, but who can produce solutions to the most pressing business problems in the shortest possible time.
[00:01:41] So in that sense, productivity can be seen as a hidden multiplier for data science careers. Ben, as the founder of a company that helps businesses accelerate their data science initiatives, it’s unsurprising that one of your areas of interest is personal productivity. Based on your experience, What are some of the biggest productivity killers holding data scientists back?
[00:02:11] Ben Johnson: I don’t know for others. I know for myself that what kills my productivity is not having an intention or a goal or a direct target that I’m trying to go for. So when we solve the science problems, we’re really trying to figure out, like, what is that hunt statement or that question that key answer you know, the question that will bring the answer.
[00:02:33] And also, what is the right level of information that would handle that at the asker’s level? So the ask is coming from a context or a person. And so we can know a lot. If that person is a fellow data scientist, then obviously we want to give them data. We want to answer them with data. But if that’s a results oriented business leader, then we need to make sure that we’re giving them information.
[00:02:57] And we. Are the managers of the data, but to answer your question, I think that the biggest killer to productivity is not being clear on what question are we trying to answer?
[00:03:08] Dr Genevieve Hayes: That, resonates with my own experience. One of the things I encountered early in my data science career was well, to take a step back. I originally trained as an actuary and worked as an actuary, and I was used to the situation where your boss would effectively tell you what to do. So, go calculate, calculate.
[00:03:28] premiums for a particular product. So when I moved into data science, I think I expected the same from my managers. And so I would ask my boss, okay, what do you want me to do? And his answer would be something like, Oh here’s some data, go do something with it. And you can probably imagine the sorts of solutions that we got myself and my team would come up with something that was a model that looks like a fun fit
[00:03:59] and those solutions tended to go down like a lead balloon. And it was only after several failures along those lines that it occurred to me, , maybe we should look at these problems from a different, point of view and figure out what is it that the senior management actually want to do with this data before starting to build a particular model from it.
[00:04:24] Ben Johnson: Yeah. What decision are you trying to make? Just kind of starting with like the end in mind or the result in mind, I find in any kind of digital execution there are people who speak results language and there are people who speak solutions language. And when we intermix those two conversations,
[00:04:41] it’s frustrating, it’s frustrating for the solution people to be like, okay, great. When are you going to give it to me? And it’s frustrating for the business folks, like hey, when am I going to get that answer when we want to talk about the solution? So I found like bifurcating like, okay, let’s have a results or planning discussion separate from a solution and asking for that right to proceed.
[00:05:02] In the way that we communicate is super helpful., what your share reminds me of is some of the playbooks that we have around data QA, because in those playbooks, we’re doing analysis just for analysis sake. I feel like we’re looking for the outliers.
[00:05:18] Okay. So if we look at this metric, these are the outliers. And really what we’re doing is we’re going back to the, originators of the data and say, like, sanity, check this for us. We want to run through a whole set of sanity checks to make sure that the pipeline that we’re about to analyze makes sense.
[00:05:34] Are there any other exterior references that we can compare this to? And I do know that the first time we were participating in this concept of data QA, not having that playbook Was a problem, right? Like, well, okay. Yeah, the data is there. It’s good. It’s coming in, but you know, to really grind on that and make sure that it was reflective of the real world was an important step.
[00:05:57] Dr Genevieve Hayes: So QA, I take your meaning quality assurance here? Is that right?
[00:06:02] Ben Johnson: Yes. That’s the acronym quality assurance, but testing and doing QA around your data pipelines.
[00:06:09] Dr Genevieve Hayes: Okay, so I get it. So actually making sure the pipelines work. And if you don’t understand what is it that you’re looking for with regard to performance, then you can end up going off in the wrong direction. Is that correct?
[00:06:23] Ben Johnson: So if you were analyzing sales data, you would want to make sure that your totals reflected the financial reports. You just want to make sure that what you’ve. Accumulated in your analysis environment is reflective of the real world. There’s nothing missing. It generally makes sense. We just haven’t introduced any problem in just the organizing and collection of the data.
[00:06:45] Dr Genevieve Hayes: Yeah, yeah. From my background in the insurance industry, those were all the sorts of checks that we used to have to do with the data as well.
[00:06:52] Ben Johnson: Well, and oftentimes the folks that are asking these hard questions, they’re not asking the questions because they have any idea how clean the data they’ve collected. They just think there might be a chance. It’s like the dumb and dumber, you know, okay, so we think we have a chance, you know anyways awful movie reference, but they think that there might be a possibility that the answer to all of their questions or this hard decision that they need to make regularly is somewhere in that pile of stuff.
[00:07:21] What we call a QA analysis Also is checking the data’s integrity if it’s even capable to solve the problem. So I think that’s a great first step and that sometimes that’s just kind of analysis for analysis sake or feels that way.
[00:07:37] Dr Genevieve Hayes: One of the things you’ve touched on several times is the idea of the results oriented people and the solutions oriented people and I take it with the solutions oriented people, you’re talking about people like the data scientists. When the data scientists are talking to those results oriented people, Is there a framework that they can follow for identifying what sorts of results those results oriented people are looking for?
[00:08:08] Ben Johnson: It’s very similar in the way that you approach like a UI UX design. We’ve taken kind of a storyboard approach, storyboard approach to what they want to see. Like, okay, What is the question? What are you expecting the answer to be? Like, what do you think would happen?
[00:08:25] And then what kind of decisions are you going to do as a result of that? And you had some of those things as well. But kind of storyboarded out what’s the journey that they’re going to take, even if it’s just a logical journey through this data to go affect some change.
[00:08:41] Dr Genevieve Hayes: So do you actually map this out on a whiteboard or with post it notes or something? So literally building a storyboard?
[00:08:48] Ben Johnson: Most of the time , it’s bullets. It’s more of like written requirements. But when we think of it, we think of it , in a storyboard and often it’ll turn into like a PowerPoint deck or something because we’re also helping them with their understanding of the funding of the data science project, like connecting ROI and what they’re trying to do.
[00:09:10] So yeah. Yeah, our firm isn’t just staff augmentation. We want to take a larger holistic ownership approach of the mission that we’re being attached to. So this is critical to like, okay, well, we’re going to be in a data science project together. Let’s make sure that we know what we’re trying to accomplish and what it’s for.
[00:09:29] Because, you know, if you’re working on a complex project and six months in everybody forgets Why they’ve done this, like why they’re spending this money oftentimes you need to remind them and, show them where you are in the roadmap to solving those problems.
[00:09:44] Dr Genevieve Hayes: With the storyboard approach, can you give me an example of that? Cause I’m still having a bit of trouble visualizing it.
[00:09:51] Ben Johnson: Yeah, it’s really just a set of questions. What are you trying to accomplish? What do you expect to have happen? Where are you getting this data? It’s , just a discovery survey that we are thinking about when we’re establishing the ground rules of the particular initiative.
[00:10:08] Dr Genevieve Hayes: And how do you go from that storyboard to the solution?
[00:10:12] Ben Johnson: That’s a great question. So the solution will end up resolving in whatever kind of framework we’re using data bricks or whatever it’ll talk about the collection, the organization and the analysis. So we’ll break down how are we going to get this data is the data already in a place where we can start messing with it.
[00:10:32] What we’re seeing is that a lot of. And I kind of going deep on the collection piece because that’s I feel like that’s like 60 percent of the work. We prefer a kind of a lake house type of environment where we’ll just leave a good portion of the data in its raw original format, analyze it.
[00:10:52] Bring it into the analysis. And then, of course, we’re usually comparing that to some relational data. But all that collection, making sure we have access to all of that. And it’s in a in a methodology and pipelines that we can start to analyze it is kind of the critical first step. So we want to get our hands around that.
[00:11:10] And then the organization. So is there, you know, anything we need to organize or is a little bit messy? And then what are those analysis? Like, what are those reports that are going to be needed or the visibility, the visualizations that would then be needed on top of that? And then what kind of decisions are trying to be made?
[00:11:28] So that’s where the ML and the predictive analytics could come in to try to help assist with the decisions. And we find that most data projects. Follow those, centralized steps that we need to have answers for those.
[00:11:43] Dr Genevieve Hayes: So a question that might need to be answered is, how much inventory should we have in a particular shop at a particular time? So that you can satisfy Christmas demand. And then you’d go and get the data about
[00:11:59] Ben Johnson: Yeah. The purchase orders or yeah. Where’s the data for your purchase orders? Do you need to collect that from all your stores or do you already have that sitting in some place? Oh, yeah. It’s in all these, you know, disparate CSVs all over the place. We just did a. project for a leading hearing aid manufacturer.
[00:12:18] And most of the data that they wanted to use was on a PC in the clinics. So we had to devise a collection mechanism in the software that the clinics were using to go collect all that and regularly import that into a place where We could analyze it, see if it was standardized enough to go into a warehouse or a lake.
[00:12:39] And there were a lot of standardization problems, oddly, some of the clinics had kind of taken matters into their own hands and started to add custom fields and whatnot. So to rationalize all of that. So collection, I feel like is a 60 percent of the problem.
[00:12:54] Dr Genevieve Hayes: So, we’ve got a framework for increasing productivity by identifying the right problem to solve, but the other half of this equation is how do you actually deliver results in a rapid fashion. because, as you know, A result today is worth far more than a result next year. What’s your advice around getting to those final results faster?
[00:13:19] Ben Johnson: So That’s why I like the lake house architecture. We’re also finding new mechanisms and methodology. Some, I can’t talk about where they’re rather than taking this time to take some of the raw data and kind of continuously summarize it. So maybe you’re summarizing it and data warehousing it, but we like the raw data to stay there and just ask it the questions, but it takes more time and more processing power.
[00:13:47] So what I’m seeing is we’re often taking that and organizing it into like a vector database or something that’s kind of right for the analysis. We’re also using vector databases in conjunction with AI solutions. So we’re asking the, we’re putting, we’re designing the vector database around the taxonomy, assuming that the user queries are going to match up with that taxonomy, and then using the LLM to help us make queries out of the vector database, and then passing that back to the LLM to test.
[00:14:15] Talk about it to make rational sense about the story that’s being told from the data. So one way that we’re accelerating the answer is just to ask questions of the raw data and pay for the processing cost. That’s fast, and that also allows us to say, okay, do we have it?
[00:14:32] Like, are we getting closer to having something that looks like the answer to your question? So we can be iterative that way, but at some point we’re starting to get some wins. In that process. And now we need to make those things more performant. And I think there’s a lot of innovation still happening in the middle of the problem.
[00:14:51] Dr Genevieve Hayes: Okay, so you’re starting by questioning the raw data. Once you realize that you’re asking the right question and getting something that the results oriented people are looking for, would you then productionize this and start creating pipelines and asking questions of process data? Yeah.
[00:15:11] Ben Johnson: Yeah. And we’d start figuring out how to summarize it so that the end user wasn’t waiting forever for an answer.
[00:15:17] Dr Genevieve Hayes: Okay, so by starting with the raw data, you’re getting them answers sooner, but then you can make it more robust.
[00:15:26] Ben Johnson: That’s right. Yes. More robust. More performant and then, of course, you could then have a wider group of users on the other side consuming that it wouldn’t just be a spreadsheet. It would be a working tool.
[00:15:37] Dr Genevieve Hayes: Yeah, it’s one of the things that I was thinking about. I used to have a boss who would always say fast, cheap and good, pick two. Meaning that, you can have a solution now and it can be cheap, but it’s going to come at the cost of And it sounds like you focus on Fast and cheap first, with some sacrifice of quality because you are dealing with raw data.
[00:16:00] But then, once you’ve got something locked in, you improve the quality of it, so then technical robustness doesn’t take a hit.
[00:16:09] Ben Johnson: Yeah, for sure. I would actually say in the early stage, you’re probably sacrificing the cheap for good and fast because you’re trying to get data right off the logs, right off your raw data, whatever it is. And to get an answer really quickly on that without having to set up a whole lot of pipeline is fast.
[00:16:28] And it’s it can be very good. It can be very powerful. We’ve seen many times where it like answers the question. You know, the question of, is that data worth? Mining further and summarizing and keeping around for a long time. So in that way, I think we addressed the ROI of it on the failures, right.
[00:16:46] Being able to fail faster. Oh yeah. That data is not going to answer the question that we have. So we don’t waste all the time of what it would have been to process that.
[00:16:55] Dr Genevieve Hayes: And what’s been the impact of taking this approach for the businesses and for the data scientists within your organisation who are taking this approach?
[00:17:05] Ben Johnson: I think it’s the feeling of like. of partnership with us around their data where we’re taking ownership of the question and they’re giving us access to whatever they have. And there’s a feeling of partnership and the kind of like immediate value. So we’re just as curious about their business as they are.
[00:17:27] And then we’re working shoulder to shoulder to help them determine the best way to answer those questions.
[00:17:32] Dr Genevieve Hayes: And what’s been the change in those businesses between, before you came on board and after you came on board?
[00:17:39] Ben Johnson: Well, I appreciate that question. So with many of the clients, they see that, oh, this is the value of the data. It has unlocked this realization that I, in the case of the hearing aid manufacturer that we work with, they really started finding that they could convert more clients and have a better brand relationship by having a better understanding of their data.
[00:18:03] And they were really happy that they kept it. You know, 10 years worth of hearing test data around to be able to understand, their audience better and then turn that into. So they’ve seen a tremendous growth in brand awareness and that’s resulted in making a significant dent in maintaining and continuing to grow their market share.
[00:18:26] Dr Genevieve Hayes: So they actually realize the true value of their data.
[00:18:30] Ben Johnson: That’s right. And then they saw when they would take action on their data they were able to increase market share because they were able to affect people that truly needed to know about their brand. And like we’re seeing after a couple of years, their brand is like, you don’t think hearing aids unless you think of this brand.
[00:18:48] So it’s really cool that they’ve been able to turn that data by really, Talking to the right people and sending their brand message to the right people.
[00:18:56] Dr Genevieve Hayes: Yeah, because what this made me think of was one of the things I kept encountering in the early days of data science was a lot of Senior decision makers would bring in data scientists and see data science as a magic bullet. And then because the data scientists didn’t know what questions to answer, they would not be able to create the value that had been promised in the organization.
[00:19:25] And the consequence after a year or two of this would be the senior decision makers would come to the conclusion that data science is just a scam. But it seems like by doing it right, you’ve managed to demonstrate to organizations such as this hearing aid manufacturer, that data science isn’t a scam and it can actually create value.
[00:19:48] Ben Johnson: Absolutely. I see data sciences anytime that that loop works, right? Where you have questions. So even I have a small client, small business, he owns a glass manufacturing shop. And. The software vendor he uses doesn’t give him a inexpensive way to mark refer like who his salespeople are,
[00:20:09] so he needs a kind of a salesperson dashboard. What’s really cool is that his software gives them, they get full access to a read only database. So putting a dashboard on top of. His data to answer this salesperson activities and commissions and just something like that. That’s data science.
[00:20:28] And now he can monitor his business. He’s able to scale using his data. He’s able to make decisions on how many salespeople should I hire, which ones are performing, which ones are not performing. How should I pay them? That’s a lot of value to us as data scientists. It just seems like we just put a dashboard together.
[00:20:46] But for that business, that’s a significant capability that they wouldn’t have otherwise had.
[00:20:52] Dr Genevieve Hayes: So with all that in mind, what is the single most important change our listeners could make tomorrow? to accelerate their data science impact and results.
[00:21:02] Ben Johnson: I would just say, be asking that question, Like what question am I trying to answer? What do you expect the outcome to be? Or what do you think the outcome is going to be? So that I’m not biased by that, but I’m sanity checking around that. And then what decisions are you going to make as a result?
[00:21:19] I think always having that like in the front of your mind would help you be more consultative and help you work according to an intention. And I think that’s super helpful. Like don’t let the client Or the customer in your case, whether that be an internal person give you that assignment, like, just tell me what’s there.
[00:21:38] Right. I just want insights. I think the have to push our leaders to give us a little more than that.
[00:21:46] Dr Genevieve Hayes: the way I look at it is, don’t treat your job as though you’re someone in a restaurant who’s just taking an order from someone.
[00:21:53] Ben Johnson: Sure.
[00:21:54] Dr Genevieve Hayes: Look at it as though you’re a doctor who’s diagnosing a problem.
[00:21:58] Ben Johnson: Yeah. And the data scientists that I worked with that have that like in their DNA, like they just can’t move forward unless they understand why they’re doing what they’re doing have been really impactful. In the organization, they just ask great questions and they quickly become an essential part of the team.
[00:22:14] Dr Genevieve Hayes: So for listeners who want to get in contact with you, Ben, or to learn more about Particle 41, what can they do?
[00:22:21] Ben Johnson: Yeah, I’m on LinkedIn. In fact I love talking to people about data science and DevOps and software development. And so I have a book appointment link on my LinkedIn profile itself. So I’m really easy to get into a call with, and we can discuss whatever is on your mind. I also offer fractional CTO services.
[00:22:42] And I would love to help you with a digital problem.
[00:22:45] Dr Genevieve Hayes: And there you have it. Another value packed episode to help turn your data science skills into serious clout, cash, and career freedom. If you enjoyed this episode, why not make it a double? Next week, catch Ben’s value boost, a quick five minute episode where he shares one powerful tip for getting real results real fast.
[00:23:10] Make sure you’re subscribed so you don’t miss it. Thanks for joining me today, Ben.
[00:23:16] Ben Johnson: Thank you. It was great being here. I enjoyed it
[00:23:19] Dr Genevieve Hayes: And for those in the audience, thank you for listening. I’m Dr. Genevieve Hayes, and this has been value driven data science.
The post Episode 54: The Hidden Productivity Killer Most Data Scientists Miss first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 53: A Wake-Up Call from 3 Tech Leaders on Why You’re Failing as a Data Scientist
Are your data science projects failing to deliver real business value?
What if the problem isn’t the technology or the organization, but your approach as a data scientist?
With only 11% of data science models making it to deployment and close to 85% of big data projects failing, something clearly isn’t working.
In this episode, three globally recognised analytics leaders, Bill Schmarzo, Mark Stouse and John Thompson, join Dr Genevieve Hayes to deliver a tough love wake-up call on why data scientists struggle to create business impact, and more importantly, how to fix it.
This episode reveals:
Bill Schmarzo, also known as “The Dean of Big Data,” is the AI and Data Customer Innovation Strategist for Dell Technologies’ AI SPEAR team, and is the author of six books on blending data science, design thinking, and data economics from a value creation and delivery perspective. He is an avid blogger and is ranked as the #4 influencer worldwide in data science and big data by Onalytica and is also an adjunct professor at Iowa State University, where he teaches the “AI-Driven Innovation” class.
Mark Stouse is the CEO of ProofAnalytics.ai, a causal AI company that helps companies understand and optimize their operational investments in light of their targeted objectives, time lag, and external factors. Known for his ability to bridge multiple business disciplines, he has successfully operationalized data science at scale across large enterprises, driven by his belief that data science’s primary purpose is enabling better business decisions.
John Thompson is EY’s Global Head of AI and is the author of four books on AI, data and analytics teams. He was named one of dataIQ’s 100 most influential people in data in 2023 and is also an Adjunct Professor at the University of Michigan, where he teaches a course based on his book “Building Analytics Teams”.
[00:00:00] Dr Genevieve Hayes: Hello, and welcome to Value Driven Data Science, the podcast that helps data scientists transform their technical expertise into tangible business value, career autonomy, and financial reward. I’m Dr. Genevieve Hayes, and today I’m joined by three globally recognized innovators and leaders in AI, analytics, and data science.
[00:00:24] Bill Schmarzo, Mark Stouse, and John Thompson. Bill? Also known as the Dean of Big Data, is the AI and Data Customer Innovation Strategist for Dell Technologies AI Spear Team, and is the author of six books on blending data science, design thinking, and data economics from a value creation and delivery perspective.
[00:00:49] He is an avid blogger and is ranked as the number four influencer worldwide in data science and big data Analytica. And he’s also an adjunct professor at Iowa State University, where he teaches AI driven innovation. Mark is the CEO of proofanalytics. ai, a causal AI company that helps organizations understand and optimize their operational investments in light of their targeted objectives, time lag and external factors.
[00:01:23] Known for his ability to bridge multiple business disciplines, he has successfully operationalized data science at scale across large enterprises. Driven by his belief that data science’s primary purpose is enabling better business decisions. And John is EY’s global head of AI and is the author of four books on AI data and analytics teams.
[00:01:49] He was named one of DataIQ’s 100 most influential people in data in 2023. and is also an adjunct professor at the University of Michigan, where he teaches a course based on his book, Building Analytics Teams. Today’s episode will be a tough love wake up call for data scientists on why you are failing to deliver real business value and more importantly, what you can do about it.
[00:02:17] So get ready to boost your impact. Earn what you’re worth and rewrite your career algorithm. Bill, Mark, John, welcome to the show.
[00:02:25] Mark Stouse: Thank
[00:02:26] Bill Schmarzo: Thanks for having us.
[00:02:27] John Thompson: to be here.
[00:02:28] Dr Genevieve Hayes: Only 11 percent of data scientists say their models always deploy. Only 10 percent of companies obtain significant financial benefits from AI technologies and close to 85 percent of big data projects fail. These statistics, taken from research conducted by Rexa Analytics, the Boston Consulting Group and Gartner respectively, paint a grim view of what it’s like working as a data scientist.
[00:02:57] The reality is, you’re probably going to fail. And when that reality occurs, it’s not uncommon for data scientists to blame either the executive for not understanding the brilliance of their work, or the corporate culture for not being ready for data science. And maybe this is true for some organizations.
[00:03:20] Particularly those relatively new to the AI adoption path. But it’s now been almost 25 years since William Cleveland first coined the term data science. And as the explosive uptake of generative AI tools, such as chat GPT demonstrate with the right use case. People are very willing to take on AI technologies.
[00:03:42] So perhaps it’s finally time to look in the mirror and face the truth. Perhaps the problem is you, the data scientist. But if this is the case, then don’t despair. In many organizations, the leadership just don’t have the time to provide data scientists with the feedback necessary to improve. But today, I’m sitting here with three of the world’s best to provide that advice just for you.
[00:04:09] So, let’s cut to the chase what are the biggest mistakes you see data scientists making when it comes to demonstrating their value?
[00:04:18] Mark Stouse: I think that you have to start with the fact that they’re not demonstrating their value, right? I mean, if you’re a CEO, a CFO, head of sales really doesn’t matter if you’re trying to make better business decisions over and over and over again. As Bill talks about a lot, the whole idea here is economic,
[00:04:39] and it is. About engaging, triggering the laws of compounding you’ve got to be able to do stuff that makes that happen. Data management, for example, even though we all agree that it’s really necessary, particularly if you’re launching, you know, big data solutions. You can’t do this sequentially and be successful.
[00:05:04] You’re going to have to find some areas probably using, you know, old fashioned math around causal analytics, multivariable linear regression, things like that, to at least get the ball rolling. In terms of delivering better value, the kind of value that business leaders actually see as valuable
[00:05:29] I mean, one of the things that I feel like I say a lot is, you have to have an understanding of your mission, the mission of data science. As somebody who, as a business leader champions it. Is to help people make those better and better and better decisions. And if you’re not doing that, you’re not creating value.
[00:05:52] Full stop.
[00:05:53] Bill Schmarzo: Totally agree with Mark. I think you’re going to find that all three of us are in violent agreement on a lot of this stuff. What I find interesting is it isn’t just a data scientist fault. Genevieve, you made a comment that leadership lacks the time to provide guidance to data scientists. So if leadership Is it treating data and analytics as an economics conversation if they think it’s a technology conversation is something that should be handled by the CIO, you’ve already lost, you’ve already failed, you already know you failed,
[00:06:24] Mark mentioned the fact that this requires the blending of both sides of the aisle. It requires a data scientist to have the right mindset to ask questions like what it is that we’re trying to achieve. How do we create value? What are our desired outcomes? What are the KPIs metrics around which are going to make your success?
[00:06:39] Who are our key stakeholders? There’s a series of questions that the data scientist must be empowered to ask and the business Leadership needs to provide the time and people and resources to understand what we’re trying to accomplish. It means we can go back old school with Stephen Covey, begin with an end in mind.
[00:07:01] What is it we’re trying to do? Are we trying to improve customer retention? We try to do, you know, reduce unplanned operational downtime or improve patient outcomes. What is it we’re trying to accomplish? The conversation must, must start there. And it has to start with business leadership, setting the direction, setting the charter, putting the posts out where we want to go, and then the data science team collaborating with the stakeholders to unleash that organizational tribal knowledge to actually solve
[00:07:32] Dr Genevieve Hayes: think a lot of the problem comes with the fact that many business leaders see data science as being like an IT project. So, if you’ve got your Windows upgrade, the leadership It gives the financing to IT, IT goes along and does it. And then one morning you’re told, when you come into work, your computer will magically upgrade to the latest version of Windows.
[00:07:55] So no one really gets bothered by it. And I think many business leaders treat data science as just another IT project like that. They think they can just Give the funding, the data scientists will go away and then they’ll come in one morning and the data science will magically be on their computer.
[00:08:15] Bill Schmarzo: Yeah, magic happens, right? No, no, magic doesn’t happen, it doesn’t happen. There has to be that leadership commitment to be at the forefront, not just on the boat, but at the front of the boat saying this is the direction we’re going to go.
[00:08:29] John Thompson: That’s the whole reason this book was written. The whole point is that, analytics projects are not tech projects. Analytics projects are cultural transformation projects, is what they are. And if you’re expecting the CEO, CFO, CIO, COO, whoever it is, to go out there and set the vision.
[00:08:50] That’s never going to happen because they don’t understand technology, and they don’t understand data. They’d rather be working on building the next factory or buying another company or something like that. What really has to happen is the analytics team has to provide leadership to the leadership for them to understand what they’re going to do.
[00:09:12] So when I have a project that we’re trying to do, my team is trying to do, and if we’re working for, let’s say, marketing, I go to the CMO and I say, look, you have to dedicate and commit. that your subject matter experts are going to be in all the meetings. Not just the kickoff meetings, not just the quarterly business review, the weekly meetings.
[00:09:36] Because when we go off as an analytics professionals and do things on our own, we have no idea what the business runs like. , we did analytics at one company that I work for. We brought it back and we showed it to the they said, the numbers are wildly wrong. And we said, well, why? And they said, well, you probably don’t understand that what we do is illegal in 10 US states.
[00:10:00] So you probably have the data from all those 10 states in the analysis. And we did. So, we took it all out and they look down there and go, you got it right. It’s kind of surprising. You didn’t know what you were doing and you got it right. So, it has to be a marriage of the subject matter experts in the business.
[00:10:17] And the data scientists, you can’t go to the leadership and say, tell us what you want. They don’t know what they want. They’d want another horse in Henry Ford’s time, or they glue a, a Walkman onto a radio or something in Steve Jobs time. They don’t know what they want. So you have to come together.
[00:10:36] And define it together and you have to work through the entire project together.
[00:10:42] Mark Stouse: Yeah, I would add to that, okay, that a lot of times the SMEs also have major holes in their knowledge that the analytics are going to challenge and give them new information. And so I totally agree. I mean, this is an iterative learning exchange. That has profound cultural implications.
[00:11:11] One of the things that AI is doing right now is it is introducing a level of transparency and accountability into operations, corporate operations, my operations, your operations, that honestly, none of us are really prepared for. None of us are really prepared for the level of learning that we’re going to have to do.
[00:11:36] And very few of us are aware of how polymathic. Most of our challenges, our problems, our objectives really are one of the things that I love to talk about in this regard is analytics made me a much better person. That I once was because it showed me the extent of my ignorance.
[00:12:01] And when I kind of came to grips with that and I started to use really the modicum of knowledge that I have as a way of curating my ignorance. And I got humble about it made a big difference
[00:12:16] John Thompson: Well, that’s the same when I was working shoulder to shoulder with Bill, I just realized how stupid I was. So, then I just, really had to, come back and, say, oh, God nowhere near the summit, I have a long way to go.
[00:12:31] Bill Schmarzo: Hey, hey, Genevie. Let me throw something out there at you and it builds on what John has said and really takes off on what Mark is talking about is that there is a cultural preparation. It needs to take place across organizations in order to learn to master the economies of learning,
[00:12:48] the economies of learning, because you could argue in knowledge based industries that what you are learning is more important than what you know. And so if what you know has declining value, and what you’re learning has increasing value, then what Mark talked about, and John as well, both city presenting data and people saying, I didn’t know that was going on, right?
[00:13:09] They had a certain impression. And if they have the wrong cultural mindset. They’re going to fight that knowledge. They’re going to fight that learning, oh, I’m going to get fired. I’m going to get punished. No, we need to create cultures that says that we are trying to master the economies and learning and you can’t learn if you’re not willing to fail.
[00:13:29] And that is what is powerful about what AI can do for us. And I like to talk about how I’m a big fan of design thinking. I integrate design thinking into all my workshops and all my training because it’s designed to. Cultivate that human learning aspect. AI models are great at cultivating algorithmic learning.
[00:13:50] And when you bring those two things together around a learning culture that says you’re going to try things, you’re going to fail, you’re going to learn, those are the organizations that are going to win.
[00:13:59] John Thompson: Yeah, you know, to tie together what Mark and Bill are saying there is that, you need people to understand that they’re working from an outmoded view of the business. Now, it’s hard for them to hear that. It’s hard for them to realize it. And what I ask data scientists to do that work for me is when we get a project and we have an operational area, sales, marketing, logistics, finance, manufacturing, whatever it is.
[00:14:26] They agreed that they’re going to go on the journey with us. We do something really simple. We do an exploratory data analysis. We look at means and modes and distributions and things like that. And we come back and we say, this is what the business looks like today. And most of the time they go, I had no idea.
[00:14:44] You know, I didn’t know that our customers were all, for the most part, between 70 and 50. I had no idea that our price point was really 299. I thought it was 3, 299. So you then end up coming together. You end up with a shared understanding of the business. Now one of two things is generally going to happen.
[00:15:05] The business is going to freak out and leave the project and say, I don’t want anything to do with this, or they’re going to lean into it and say, I was working from something that was, as Bill said, declining value. Okay. Now, if they’re open, like a AI model that’s being trained, if they’re open to learning, they can learn what the business looks like today, and we can help them predict what the business should look like tomorrow.
[00:15:31] So we have a real issue here that the three of us have talked about it from three different perspectives. We’ve all seen it. We’ve all experienced it. It’s a real issue, we know how people can come together. The question is, will they?
[00:15:46] Dr Genevieve Hayes: think part of the issue is that, particularly in the area of data science, there’s a marked lack of leadership because I think a lot of people don’t understand how to lead these projects. So you’ve got Many data scientists who are trained heavily in the whole technical aspect of data science, and one thing I’ve come across is, you know, data scientists who’ll say to me, my job is to do the technical work, tell me what to do.
[00:16:23] I’ll go away and do it. Give it to you. And then you manager can go and do whatever you like with it.
[00:16:29] Mark Stouse: Model fitment.
[00:16:31] Dr Genevieve Hayes: Yeah. And then one thing I’ve experienced is many managers in data science are, you know, It’s often the area that they find difficult to find managers for, so we’ll often get people who have no data science experience whatsoever
[00:16:46] and so I think part of the solution is teaching the data scientists that they have to start managing up because they’re the ones who understand what they’re doing the best, but no one’s telling them that because the people above them often don’t know that they should be telling the data
[00:17:08] John Thompson: Well, if that’s the situation, they should just fire everybody and save the money. Because it’s never going to go anywhere. But Bill, you were going to say something. Go ahead.
[00:17:16] Bill Schmarzo: Yeah, I was going to say, what’s interesting about Genevieve, what you’re saying is that I see this a lot in not just data scientists, but in a lot of people who are scared to show their ignorance in new situations. I think Mark talked about this, is it because they’re, you think about if you’re a data scientist, you probably have a math background. And in math, there’s always a right answer. In data science, there isn’t. There’s all kinds of potential answers, depending on the situation and the circumstances. I see this all the time, by the way, with our sales folks. Who are afraid we’re selling technology. We’re afraid to talk to the line of business because I don’t understand their business Well, you don’t need to understand their business, but you do need to become like socrates and start asking questions What are you trying to accomplish?
[00:18:04] What are your goals? What are your desired outcomes? How do you measure success? Who are your stakeholders ? You have to be genuinely interested In their success and ask those kind of questions if you’re doing it to just kind of check a box off Then just get chad gpt to rattle it off But if you’re genuinely trying to understand what they’re trying to accomplish And then thinking about all these marvelous different tools you have because they’re only tools And how you can weave them together to help solve that now you’ve got That collaboration that john’s book talks about about bringing these teams together Yeah
[00:18:39] Mark Stouse: is, famously paraphrased probably did actually say something like this, . But he’s famously paraphrased as saying that he would rather have a really smart question than the best answer in the world. And. I actually experienced that two days ago,
[00:18:57] in a conversation with a prospect where I literally, I mean, totally knew nothing about their business. Zero, but I asked evidently really good questions. And so his impression of me at the end of the meeting was, golly, you know, so much about our business. And I wanted to say, yeah, cause you just educated me.
[00:19:21] Right. You know, I do now. And so I think there’s actually a pattern here that’s really worth elevating. So what we are seeing right now with regard to data science teams is scary similar to what happened with it after Y2K, the business turned around and looked at him and said, seriously, we spend all that money,
[00:19:45] I mean, what the heck? And so what happened? The CIO got, demoted organizationally pretty far down in the company wasn’t a true C suite member anymore. Typically the whole thing reported up into finance. The issue was not. Finance, believing that they knew it better than the it people,
[00:20:09] it was, we are going to transform this profession from being a technology first profession to a business outcomes. First profession, a money first profession, an economics organization, that has more oftentimes than not been the outcome in the last 25 years. But I think that that’s exactly what’s going on right now with a lot of data science teams.
[00:20:39] You know, I used to sit in technology briefing rooms, listening to CIOs and other people talk about their problems. And. This one CIO said, you know, what I did is I asked every single person in my organization around the world to go take a finance for non financial managers course at their local university.
[00:21:06] They want credit for it. We’ll pay the bill. If they just want to audit it, they can do that. And they started really cross pollinating. These teams to give them more perspective about the business. I totally ripped that off because it just struck me as a CMO as being like, so many of these problems, you could just do a search and replace and get to marketing.
[00:21:32] And so I started doing the same thing and I’ve made that suggestion to different CDOs, some of whom have actually done it. So it’s just kind of one of those things where you have to say, I need to know more. So this whole culture of being a specialist is changing from.
[00:21:53] This, which, this is enough, this is okay , I’m making a vertical sign with my hand, to a T shaped thing, where the T is all about context. It’s all about everything. That’s not part of your. Profession
[00:22:09] John Thompson: Yeah, well, I’m going to say that here’s another book that you should have your hands on. This is Aristotle. We can forget about Socrates. Aristotle’s the name. But you know. But , Bill’s always talking about Socrates. I’m an Aristotle guy myself. So, you
[00:22:23] Bill Schmarzo: Okay, well I Socrates had a better jump shot. I’m sorry. He could really nail that
[00:22:28] John Thompson: true. It’s true. Absolutely. Well, getting back , to the theme of the discussion, in 1 of the teams that I had at CSL bearing, which is an Australian company there in Melbourne, I took my data science team and I brought in speech coaches.
[00:22:45] Presentation coaches people who understand business, people who understood how to talk about different things. And I ran them through a battery of classes. And I told them, you’re going to be in front of the CEO, you’re going to be in front of the EVP of finance, you’re going to be in front of all these different people, and you need to have the confidence to speak their language.
[00:23:07] Whenever we had meetings, we talk data science talk, we talk data and integration and vectors and, algorithms and all that kind of stuff. But when we were in the finance meeting, we talked finance. That’s all we talked. And whenever we talked to anybody, we denominated all our conversations in money.
[00:23:25] Whether it was drachma, yen, euros, pounds, whatever it was, we never talked about speeds and feeds and accuracy and results. We always talked about money. And if it didn’t make money, we didn’t do it. So, the other thing that we did that really made a difference was that when the data scientists and data scientists hate this, When they went into a meeting, and I was there, and even if I wasn’t there, they were giving the end users and executives recommendations.
[00:23:57] They weren’t going in and showing a model and a result and walking out the door and go, well, you’re smart enough to interpret it. No, they’re not smart enough to interpret it. They actually told the marketing people. These are the 3 things you should do. And if your data scientists are not being predictive and recommending actions, they’re not doing their job.
[00:24:18] Dr Genevieve Hayes: What’s the, so what test At the end of everything, you have to be able to say, so what does this mean to whoever your audience is?
[00:24:25] Mark Stouse: That’s right. I mean, you have to be able to say well, if the business team can’t look at your output, your data science output, and know what to do with it, and know how to make a better decision, it’s like everything else that you did didn’t happen. I mean it, early in proof, we were working on. UX, because it became really clear that what was good for a data scientist wasn’t working. For like everybody else. And so we did a lot of research into it. Would you believe that business teams are okay with charts? Most of them, if they see a graph, they just totally freeze and it’s not because they’re stupid.
[00:25:08] It’s because so many people had a bad experience in school with math. This is a psychological, this is an intellectual and they freeze. So in causal analytics, one of the challenges is that, I mean, this is pretty much functioning most of the time anyway, on time series data, so there is a graph,
[00:25:31] this is kind of like a non negotiable, but we had a customer that was feeding data so fast into proof that the automatic recalc of the model was happening like lickety split. And that graph all of a sudden looked exactly like a GPS. It worked like a GPS. In fact, it really is a GPS. And so as soon as we stylized.
[00:26:01] That graph to look more like a GPS track, all of a sudden everybody went, Oh,
[00:26:10] Dr Genevieve Hayes: So I got rid of all the PTSD from high school maths and made it something familiar.
[00:26:16] Mark Stouse: right. And so it’s very interesting. Totally,
[00:26:21] Bill Schmarzo: very much mirrors what mark talked about So when I was the new vice president of advertiser analytics at yahoo we were trying to solve a problem to help our advertisers optimize their spend across the yahoo ad network and because I didn’t know anything about that industry We went out and my team went out and interviewed all these advertisers and their agencies.
[00:26:41] And I was given two UEX people and zero data. Well, I did have one data scientist. But I had mostly UX people on this project. My boss there said, you’re going to want UX people. I was like, no, no, I need analytics. He said, trust me in UX people and the process we went through and I could spend an hour talking about the grand failure of the start and the reclamation of how it was saved at a bar after too many drinks at the Waldorf there in New York.
[00:27:07] But what we’ve realized is that. For us to be effective for our target audience was which was media planners and buyers and campaign managers. That was our stakeholders. It wasn’t the analysts, it was our stakeholders. Like Mark said, the last thing they wanted to see was a chart. And like John said, what they wanted the application to do was to tell them what to do.
[00:27:27] So we designed this user interface that on one side, think of it as a newspaper, said, this is what’s going on with your campaign. This audience is responding. These sites are this, these keywords are doing this. And the right hand side gave recommendations. We think you should move spend from this to this.
[00:27:42] We think you should do this. And it had three buttons on this thing. You could accept it and it would kick into our advertising network and kick in. And we’d measure how effective that was. They could reject it. They didn’t think I was confident and we’d measure effectiveness or they could change it. And we found through our research by putting that change button in there that they had control, that adoption went through the roof.
[00:28:08] When it was either yes or no, adoption was really hard, they hardly ever used it. Give them a chance to actually change it. That adoption went through the roof of the technology. So what John was saying about, you have to be able to really deliver recommendations, but you can’t have the system feel like it’s your overlord.
[00:28:27] You’ve got to be like it’s your Yoda on your shoulder whispering to your saying, Hey, I think you should do this. And you’re going, eh, I like that. No, I don’t like this. I want to do that instead. And when you give them control, then the adoption process happens much smoother. But for us to deliver those kinds of results, we had to know in detail, what decisions are they trying to make?
[00:28:45] How are they going to measure success? We had to really understand their business. And then the data and the analytics stuff was really easy because we knew what we had to do, but we also knew what we didn’t have to do. We didn’t have to boil the ocean. We were trying to answer basically 21 questions.
[00:29:01] The media planners and buyers and the campaign managers had 21 decisions to make and we built analytics and recommendations for each Of those 21
[00:29:10] John Thompson: We did the same thing, you know, it blends the two stories from Mark and Bill, we were working at CSL and we were trying to give the people tools to find the best next location for plasma donation centers. And, like you said, there were 50, 60 different salient factors they had, and when we presented to them in charts and graphs, Information overload.
[00:29:34] They melted down. You can just see their brains coming out of their ears. But once we put it on a map and hit it all and put little dials that they could fiddle with, they ran with it.
[00:29:49] Bill Schmarzo: brilliant
[00:29:50] Mark Stouse: totally, totally agree with that. 100% you have to know what to give people and you have to know how to give them, control over some of it, nobody wants to be an automaton. And yet also they will totally lock up if you just give them the keys to the kingdom. Yeah.
[00:30:09] Dr Genevieve Hayes: on what you’ve been saying in the discussion so far, what I’m hearing is that the critical difference between what data scientists think their role is and what business leaders actually need is the data scientists is. Well, the ones who aren’t performing well think their role is to just sit there in a back room and do technical work like they would have done in their university assignments.
[00:30:33] What the business leaders need is someone who can work with them, ask the right questions in order to understand the needs of the business. make recommendations that answer those questions. But in answering those questions, we’re taking a data informed approach rather than a data driven approach. So you need to deliver the answers to those questions in such a way that you’re informing the business leaders and you’re delivering it in a way that Delivers the right user experience for them, rather than the user experience that the data scientists might want, which would be your high school maths graphs.
[00:31:17] Is that a good summary?
[00:31:20] John Thompson: Yeah, I think that’s a really good summary. You know, one of the things that Bill and I, and I believe Mark understands is we’re all working to change, you know, Bill and I are teaching at universities in the United States. I’m on the advisory board of about five. Major universities. And whenever I go in and talk to these universities and they say, Oh, well, we teach them, these algorithms and these mathematical techniques and these data science and this statistics.
[00:31:48] And I’m like, you are setting these people up for failure. You need to have them have presentation skills, communication skills, collaboration. You need to take about a third of these credits out and change them out for soft skills because you said it Genevieve, the way we train people, young people in undergraduate and graduate is that they have a belief that they’re going to go sit in a room and fiddle with numbers.
[00:32:13] That’s not going to be successful.
[00:32:16] Mark Stouse: I would give one more point of dimensionality to this, which is a little more human, in some respects, and that is that I think that a lot of data scientists love the fact that they are seen as Merlin’s as shamans. And the problem that I personally witnessed this about two years ago is when you let business leaders persist in seeing you in those terms.
[00:32:46] And when all of a sudden there was a major meltdown of some kind, in this case, it was interest rates, and they turn around and they say, as this one CEO said in this meeting Hey, I know you’ve been doing all kinds of really cool stuff back there with AI and everything else. And now I need help.
[00:33:08] Okay. And the clear expectation was. I need it now, I need some brilliant insight now. And the answer that he got was, we’re not ready yet. We’re still doing the data management piece. And this CEO dropped the loudest F bomb. That I think I have ever heard from anybody in almost any situation,
[00:33:36] and that guy, that data science leader was gone the very next day. Now, was that fair? No. Was it stupid? For the data science leader to say what he said. Yeah, it was really dumb.
[00:33:52] Bill Schmarzo: Don’t you call that the tyranny of perfection mark? Is that your term that you always use? is that There’s this idea that I gotta get the data all right first before I can start doing analysis And I think it’s you I hear you say the tyranny of perfection is what hurts You Progress over perfection, learning over absolutes, and that’s part of the challenge is it’s never going to be perfect.
[00:34:13] Your data is never going to be perfect, you got to use good enough data
[00:34:17] Mark Stouse: It’s like the ultimate negative version of the waterfall.
[00:34:22] John Thompson: Yeah,
[00:34:23] Mark Stouse: yet we’re all supposedly living in agile paradise. And yet very few people actually operate
[00:34:30] John Thompson: that’s 1 thing. I want to make sure that we get in the recording is that I’ve been on record for years and I’ve gone in front of audiences and said this over and over again. Agile and analytics don’t mix that is. There’s no way that those 2 go together. Agile is a babysitting methodology. Data scientists don’t do well with it.
[00:34:50] So, you know, I’ll get hate mail for that, but I will die on that hill. But, the 1 thing that, Mark, I agree with 100 percent of what you said, but the answer itself or the clue itself is in the title. We’ve been talking about. It’s data science. It’s not magic. I get people coming and asking me to do magical things all the time.
[00:35:11] And I’m like. Well, have you chipped all the people? Do you have all their brain waves? If you have that data set, I can probably analyze it. But, given that you don’t understand what’s going on inside their cranium, that’s magic. I can’t do that. We had the same situation when COVID hit, people weren’t leaving their house.
[00:35:29] So they’re not donating plasma. It’s kind of obvious, so, people came to us and said, Hey, the world’s gone to hell in a handbasket in the last two weeks. The models aren’t working and I’m like, yeah, the world’s changed, give us four weeks to get a little bit of data.
[00:35:43] We’ll start to give you a glimmer of what this world’s going to look like two months later. We had the models working back in single digit error terms, but when the world goes haywire, you’re not going to have any data, and then when the executives are yelling at you, you just have to say, look, this is modeling.
[00:36:01] This is analytics. We have no precedent here.
[00:36:05] Bill Schmarzo: to build on what John was just saying that the challenge that I’ve always seen with data science organizations is if they’re led by somebody with a software development background, getting back to the agile analytics thing, the problem with software development. is that software development defines the requirements for success.
[00:36:23] Data science discovers them. It’s hard to make that a linear process. And so, if you came to me and said, Hey, Schmarz, you got a big, giant data science team. I had a great data science team at Hitachi. Holy cow, they were great. You said, hey, we need to solve this problem. When can you have it done?
[00:36:38] I would say, I need to look at the problem. I need to start exploring it. I can’t give you a hard date. And that drove software development folks nuts. I need a date for when I, I don’t know, cause I’ve got to explore. I’m going to try lots of things. I’m going to fail a lot.
[00:36:51] I’m going to try things that I know are going to fail because I can learn when I fail. And so, when you have an organization that has a software development mindset, , like John was talking about, they don’t understand the discovery and learning process that the data science process has to go through to discover the criteria for success.
[00:37:09] Mark Stouse: right. It’s the difference between science and engineering.
[00:37:13] John Thompson: Yes, exactly. And 1 of the things, 1 of the things that I’ve created, it’s, you know, everybody does it, but I have a term for it. It’s a personal project portfolio for data scientists. And every time I’ve done this and every team. Every data scientist has come to me individually and said, this is too much work.
[00:37:32] It’s too hard. I can’t
[00:37:34] Bill Schmarzo: Ha, ha, ha,
[00:37:35] John Thompson: three months later, they go, this is the only way I want to work. And what you do is you give them enough work so when they run into roadblocks, they can stop working on that project. They can go out and take a swim or work on something else or go walk their dog or whatever.
[00:37:53] It’s not the end of the world because the only project they’re working on can’t go forward. if they’ve got a bunch of projects to time slice on. And this happens all the time. You’re in, team meetings and you’re talking and all of a sudden the data scientist isn’t talking about that forecasting problem.
[00:38:09] It’s like they ran into a roadblock. They hit a wall. Then a week later, they come in and they’re like, Oh, my God, when I was in the shower, I figured it out. You have to make time for cogitation, introspection, and eureka moments. That has to happen in data science.
[00:38:28] Bill Schmarzo: That is great, John. I love that. That is wonderful.
[00:38:30] Mark Stouse: And of course the problem is. Yeah. Is that you can’t predict any of that, that’s the part of this. There’s so much we can predict. Can’t predict that.
[00:38:42] Bill Schmarzo: you know what you could do though? You could do Mark, you could prescribe that your data science team takes multiple showers every day to have more of those shower moments. See, that’s the problem. I see a correlation. If showers drive eureka moments, dang it.
[00:38:54] Let’s give him more showers.
[00:38:56] John Thompson: Yep. Just like firemen cause fires
[00:38:59] Mark Stouse: Yeah, that’s an interesting correlation there, man.
[00:39:05] Dr Genevieve Hayes: So, if businesses need something different from what the data scientists are offering, why don’t they just articulate that in the data scientist’s role description?
[00:39:16] John Thompson: because they don’t know they need it.
[00:39:17] Mark Stouse: Yeah. And I think also you gotta really remember who you’re dealing with here. I mean, the background of the average C suite member is not highly intellectual. That’s not an insult, that’s just they’re not deep thinkers. They don’t think a lot. They don’t
[00:39:37] John Thompson: that with tech phobia.
[00:39:38] Mark Stouse: tech phobia and a short termism perspective.
[00:39:43] That arguably is kind of the worst of all the pieces.
[00:39:48] John Thompson: storm. It’s a
[00:39:49] Mark Stouse: It is, it is a
[00:39:50] John Thompson: know, I, I had, I’ve had CEOs come to me and say, we’re in a real crisis here and you guys aren’t helping. I was like, well, how do you know we’re not helping? You never talked to us. And, in this situation, we had to actually analyze the entire problem and we’re a week away from making recommendations.
[00:40:08] And I said that I said, we have an answer in 7 days. He goes, I need an answer today. I said, well, then you should go talk to someone else because in 7 days, I’ll have it. But now I don’t. So, I met with him a week later. I showed them all the data, all the analytics, all the recommendations. And they said to me, we don’t really think you understand the business well enough.
[00:40:27] We in the C suite have looked at it and we don’t think that this will solve it. And I’m like, okay, fine, cool. No problem. So I left, and 2 weeks later, they called me in and said, well, we don’t have a better idea. So, what was that you said? And I said, well, we’ve coded it all into the operational systems.
[00:40:43] All you have to do is say yes. And we’ll turn it on and it was 1 of the 1st times and only times in my life when the chart was going like this, we made all the changes and it went like that. It was a perfect fit. It worked like a charm and then, a month later, I guess it was about 6 months later, the CEO came around and said, wow, you guys really knew your stuff.
[00:41:07] You really were able to help us. Turn this around and make it a benefit and we turned it around faster than any of the competitors did. And then he said, well, what would you like to do next? And I said, well, I resigned last week. So, , I’m going to go do it somewhere else.
[00:41:22] And he’s like, what? You just made a huge difference in the business. And I said, yeah, you didn’t pay me anymore. You didn’t recognize me. And I’ve been here for nearly 4 years, and I’ve had to fight you tooth and nail for everything. I’m tired of it.
[00:41:34] Mark Stouse: Yeah. That’s what’s called knowing your value. One of the things that I think is so ironic about this entire conversation is that if any function has the skillsets necessary to forecast and demonstrate their value as multipliers. Of business decisions, decision quality, decision outcomes it’s data science.
[00:42:05] And yet they just kind of. It’s like not there. And when you say that to them, they kind of look at you kind of like, did you really just say that, and so it is, one of the things that I’ve learned from analytics is that in the average corporation, you have linear functions that are by definition, linear value creators.
[00:42:32] Sales would be a great example. And then you have others that are non linear multipliers. Marketing is one, data science is another, the list is long, it’s always the non linear multipliers that get into trouble because they don’t know how to show their value. In the same way that a linear creator can show it
[00:42:55] John Thompson: And I think that’s absolutely true, Mark. And what I’ve been saying, and Bill’s heard this until he’s sick of it. Is that, , data science always has to be denominated in currency. Always, if you can’t tell them in 6 months, you’re going to double the sales or in 3 months, you’re going to cut cost or in, , 5 months, you’re going to have double the customers.
[00:43:17] If you’re not denominating that in currency and whatever currency they care about, you’re wasting your time.
[00:43:23] Dr Genevieve Hayes: The problem is, every single data science book tells you that the metrics to evaluate models by are, precision, recall, accuracy, et
[00:43:31] John Thompson: Yeah, but that’s technology. That’s not business.
[00:43:34] Dr Genevieve Hayes: exactly. I’ve only ever seen one textbook where they say, those are technical metrics, but the metrics that really count are the business metrics, which are basically dollars and cents.
[00:43:44] John Thompson: well, here’s the second one that says it.
[00:43:46] Dr Genevieve Hayes: I will read that. For the audience it’s Business Analytics Teams by John Thompson.
[00:43:51] John Thompson: building analytics
[00:43:52] Dr Genevieve Hayes: Oh, sorry, Building
[00:43:54] Mark Stouse: But, but I got to tell you seriously, the book that John wrote that everybody needs to read in business. Okay. Not just data scientists, but pretty much everybody. Is about causal AI. And it’s because almost all of the questions. In business are about, why did that happen? How did it happen? How long did it take for that to happen?
[00:44:20] It’s causal. And so, I mean, when you really look at it that way and you start to say, well, what effects am I causing? What effects is my function causing, all of a sudden the scales kind of have a way of falling away from your eyes and you see things. Differently.
[00:44:43] John Thompson: of you to say that about that book. I appreciate that.
[00:44:46] Mark Stouse: That kick ass book, kick
[00:44:48] John Thompson: Well, thank you. But, most people don’t understand that we’ve had analytical or foundational AI for 70 years. We’ve had generative AI for two, and we’ve had causal for a while, but only people understand it are the people on this call and Judea Pearl and maybe 10 others in the world, but we’re moving in a direction where those 3 families of AI are going to be working together in what I’m calling composite AI, which is the path to artificial, or as Bill says, average general intelligence or AGI.
[00:45:24] But there are lots of eight eyes people talk about it as if it’s one thing and it’s
[00:45:29] Mark Stouse: Yeah, correct. That’s right.
[00:45:31] Dr Genevieve Hayes: I think part of the problem with causal AI is it’s just not taught in data science courses.
[00:45:37] John Thompson: it was not taught anywhere. The only place it’s taught is UCLA.
[00:45:40] Mark Stouse: But the other problem, which I think is where you’re going with it Genevieve is even 10 years ago, they weren’t even teaching multivariable linear regression as a cornerstone element of a data science program. So , they basically over rotated and again, I’m not knocking it.
[00:46:01] I’m not knocking machine learning or anything like that. Okay. But they over rotated it and they turned it into some sort of Omni tool, that could do it all. And it can’t do it all.
[00:46:15] Dr Genevieve Hayes: think part of the problem is the technical side of data science is the amalgamation of statistics and computer science . But many data science university courses arose out of the computer science departments. So they focused on the machine learning courses whereas many of those things like.
[00:46:34] multivariable linear analysis and hypothesis testing, which leads to things like causal AI. They’re taught in the statistics courses that just don’t pop up in the data science programs.
[00:46:46] Mark Stouse: Well, that’s certainly my experience. I teach at USC in the grad school and that’s the problem in a nutshell right there. In fact, we’re getting ready to have kind of a little convocation in LA about this very thing in a couple of months because it’s not sustainable.
[00:47:05] Bill Schmarzo: Well, if you don’t mind, I’m going to go back a second. We talked about, measuring success as currency. I’m going to challenge that a little bit. We certainly need to think about how we create value, and value isn’t just currency. John held up a book earlier, and I’m going to hold up one now, Wealth of Nations,
[00:47:23] John Thompson: Oh yeah.
[00:47:25] Bill Schmarzo: Page 28, Adam Smith talks about value he talks about value creation, and it isn’t just about ROI or net present value. Value is a broad category. You got customer value, employee value, a partner stakeholder. You have society value, community value of environmental value.
[00:47:43] We have ethical value. And as we look at the models that we are building, that were guided or data science teams to build, we need to broaden the definition of value. It isn’t sufficient if we can drive ROI, if it’s destroying our environment and putting people out of work. We need to think more holistically.
[00:48:04] Adam Smith talks about this. Yeah, 1776. Good year, by the way, it’s ultimate old school, but it’s important when we are As a data science team working with the business that we’re broadening their discussions, I’ve had conversations with hospitals and banks recently. We run these workshops and one of the things I always do, I end up pausing about halfway through the workshop and say, what are your desired outcomes from a community perspective?
[00:48:27] You sit inside a community or hospital. You have a community around you, a bank, you have a community around you. What are your desired outcomes for that community? How are you going to measure success? What are those KPIs and metrics? And they look at me like I got lobsters crawling out of my ears.
[00:48:40] The thing is is that it’s critical if we’re going to Be in champion data science, especially with these tools like these new ai tools causal predictive generative autonomous, these tools allow us to deliver a much broader range of what value is And so I really rail against when somebody says, you know, and not trying to really somebody here but You know, we gotta deliver a better ROI.
[00:49:05] How do you codify environmental and community impact into an ROI? Because ROI and a lot of financial metrics tend to be lagging indicators. And if you’re going to build AI models, you want to build them on leading indicators.
[00:49:22] Mark Stouse: It’s a lagging efficiency metric,
[00:49:24] Bill Schmarzo: Yeah, exactly. And AI doesn’t do a very good job of optimizing what’s already happened.
[00:49:29] That’s not what it does.
[00:49:30] John Thompson: sure.
[00:49:31] Bill Schmarzo: I think part of the challenge, you’re going to hear this from John and from Mark as well, is that we broaden this conversation. We open our eyes because AI doesn’t need to just deliver on what’s happened in the past, looks at the historical data and just replicates that going forward.
[00:49:45] That leads to confirmation bias of other things. We have a chance in AI through the AI utility function to define what it is we want our AI models to do. from environmental, society, community, ethical perspective. That is the huge opportunity, and Adam Smith says that so.
[00:50:03] John Thompson: There you go. Adam Smith. I love it. Socrates, Aristotle, Adam
[00:50:08] Bill Schmarzo: By the way, Adam Smith motivated this book that I wrote called The Economics of Data Analytics and Digital Transformation I wrote this book because I got sick and tired of walking into a business conversation and saying, Data, that’s technology. No, data, that’s economics.
[00:50:25] Mark Stouse: and I’ll tell you what, you know what, Genevieve, I’m so cognizant of the fact in this conversation that the summer can’t come fast enough when I too will have a book,
[00:50:39] John Thompson: yay.
[00:50:41] Mark Stouse: yeah, I will say this, One of the things that if you use proof, you’ll see this, is that there’s a place where you can monetize in and out of a model, but money itself is not causal. It’s what you spend it on. That’s either causal or in some cases, not
[00:51:01] That’s a really, really important nuance. It’s not in conflict with what John was saying about monetizing it. And it’s also not in conflict with what. My friend Schmarrs was saying about, ROI is so misused as a term in business. It’s just kind of nuts.
[00:51:25] It’s more like a shorthand way of conveying, did we get value
[00:51:31] John Thompson: yeah. And the reason I say that we denominated everything in currency is that’s generally one of the only ways. to get executives interested. If you go in and say, Oh, we’re going to improve this. We’re going to improve that. They’re like, I don’t care. If I say this project is going to take 6 months and it’s going to give you 42 million and it’s going to cost you nothing, then they’re like, tell me more, and going back to what Bill had said earlier, we need to open our aperture on what we do with these projects when we were at Dell or Bill and I swapped our times at Dell, we actually did a project with a hospital system in the United States and over 2 years.
[00:52:11] We knocked down the incidence of post surgical sepsis by 72%. We saved a number of lives. We saved a lot of money, too, but we saves people’s lives. So analytics can do a lot. Most of the people are focused on. Oh, how fast can we optimize the search engine algorithm? Or, how can we get the advertisers more yield or more money?
[00:52:32] There’s a lot of things we can do to make this world better. We just have to do it.
[00:52:36] Mark Stouse: The fastest way to be more efficient is to be more effective, right? I mean, and so when I hear. CEOs and CFOs, because those are the people who use this language a lot. Talk about efficiency. I say, whoa, whoa, hold on. You’re not really talking about efficiency. You’re talking about cost cutting.
[00:52:58] Those two things are very different. And it’s not that you shouldn’t cut costs if you need to, but it’s not efficiency. And ultimately you’re not going to cut your way into better effectiveness. It’s just not the way things go.
[00:53:14] John Thompson: Amen.
[00:53:15] Mark Stouse: And so, this is kind of like the old statement about physicists,
[00:53:18] if they’re physicists long enough, they turn into philosophers. I think all three of us, have that going on. Because we have seen reality through a analytical lens for so long that you do actually get a philosophy of things.
[00:53:38] Dr Genevieve Hayes: So what I’m hearing from all of you is that for data scientists to create value for the businesses that they’re working for, they need to start shifting their approach to basically look at how can we make the businesses needs. And how can we do that in a way that can be expressed in the business’s language, which is dollars and cents, but also, as Bill pointed out value in terms of the community environment.
[00:54:08] So less financially tangible points of view.
[00:54:11] Bill Schmarzo: And if I could just slightly add to that, I would say first thing that they need to do is to understand how does our organization create value for our constituents and stakeholders.
[00:54:22] Start there. Great conversation. What are our desired outcomes? What are the key decisions? How do we measure success? If we have that conversation, by the way, it isn’t unusual to have that conversation with the business stakeholders and they go I’m not exactly sure.
[00:54:37] John Thompson: I don’t know how that works.
[00:54:38] Bill Schmarzo: Yeah. So you need to find what are you trying to improve customer retention? You’re trying to increase market share. What are you trying to accomplish and why and how are you going to measure success? So the fact that the data science team is asking that question, because like John said, data science can solve a whole myriad of problems.
[00:54:54] It isn’t that it can’t solve. It can solve all kinds. That’s kind of the challenge. So understanding what problems we want to solve starts by understanding how does your organization create value. If you’re a hospital, like John said, reducing hospital acquired infections, reducing long term stay, whatever it might be.
[00:55:09] There are some clear goals. Processes initiatives around which organizations are trying to create value
[00:55:18] Dr Genevieve Hayes: So on that note, what is the single most important change our listeners could make tomorrow to accelerate their data science impact and results?
[00:55:28] John Thompson: I’ll go first. And it’s to take your data science teams and not merge them into operational teams, but to introduce the executives that are in charge of these areas and have them have an agreement that they’re going to work together. Start there.
[00:55:46] Bill Schmarzo: Start with how do you how does the organization create value? I mean understand that fundamentally ask those questions and keep asking until you find somebody in the organization who can say we’re trying to do this
[00:55:57] Mark Stouse: to which I would just only add, don’t forget the people are people and they all have egos and they all want to appear smarter and smarter and smarter. And so if you help them do that, you will be forever in there must have list, it’s a great truth that I have found if you want to kind of leverage bills construct, it’s the economies of ego.
[00:56:24] Bill Schmarzo: I like
[00:56:24] John Thompson: right, Mark, wrap this up. When’s your book coming out? What’s the title?
[00:56:28] Mark Stouse: It’s in July and I’ll be shot at dawn. But if I tell you the title, but so I interviewed several hundred fortune, 2000 CEOs and CFOs about how they see go to market. The changes that need to be made in go to market. The accountability for it all that kind of stuff. And so the purpose of this book really in 150, 160 pages is to say, Hey, they’re not all correct, but this is why they’re talking to you the way that they’re talking to you, and this is why they’re firing.
[00:57:05] People in go to market and particularly in B2B at an unprecedented rate. And you could, without too much deviation, do a search and replace on marketing and sales and replace it with data science and you’d get largely the same stuff. LinkedIn,
[00:57:25] Dr Genevieve Hayes: for listeners who want to get in contact with each of you, what can they do?
[00:57:29] John Thompson: LinkedIn. John Thompson. That’s where I’m at.
[00:57:32] Mark Stouse: Mark Stouse,
[00:57:34] Bill Schmarzo: And not only connect there, but we have conversations all the time. The three of us are part of an amazing community of people who have really bright by diverse perspectives. And we get into some really great conversations. So not only connect with us, but participate, jump in. Don’t be afraid.
[00:57:51] Dr Genevieve Hayes: And there you have it, another value packed episode to help you turn your data skills into serious clout, cash, and career freedom. If you found today’s episode useful and think others could benefit, please leave us a rating and review on your podcast platform of choice. That way we’ll be able to reach more data scientists just like you.
[00:58:11] Thanks for joining me today, Bill, Mark, and John.
[00:58:16] Mark Stouse: Great being with
[00:58:16] John Thompson: was fun.
[00:58:18] Dr Genevieve Hayes: And for those in the audience, thanks for listening. I’m Dr. Genevieve Hayes, and this has been value driven data science.
The post Episode 53: A Wake-Up Call from 3 Tech Leaders on Why You’re Failing as a Data Scientist first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 52: Automating the Automators – How AI and ML are Transforming Data Teams
In many organisations, data scientists and data engineers exist as support staff. Data engineers are there to make data accessible to data scientists and data analysts, and data scientists are there to make use of that data to support the rest of the business.
But in helping everyone else in the business, data professionals can often forget to help themselves.
However, just as AI and machine learning can be used to help others in the organisation perform their jobs more effectively, there’s no reason why they can’t also be used to help data professionals excel in their own jobs. And as experts in applying these techniques, data scientists are perfectly placed to leverage them.
In this episode, Prof Barzan Mozafari joins Dr Genevieve Hayes to discuss how AI and machine learning are helping data professionals do their jobs more effectively.
Prof. Barzan Mozafari is the co-founder and CEO of Keebo, a turn-key data learning platform for automating and accelerating enterprise analytics. He is also an Associate Professor of Computer Science at the University of Michigan and Prof. Barzan Mozafari is the co-founder and CEO of Keebo, a turn-key data learning platform for automating and accelerating enterprise analytics. He is also an Associate Professor of Computer Science at the University of Michigan and has won several awards for his research at the intersection of machine learning and database systems.
The post Episode 52: Automating the Automators – How AI and ML are Transforming Data Teams first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 51: Data Storytelling in Virtual Reality
In the 2002 movie, Minority Report, the future of data interaction is depicted as Tom Cruise standing in front of a computer monitor and literally grabbing data points with his hands. Data interaction is shown to be as easy as interacting with physical objects in the real world.
This vision of a world where data is accessible to all was considered to be science fiction when Minority Report was first released. But over 20 years later, we are now at a point where technology has become good enough for this to soon become fact. And its data science that’s making this possible.
Or more accurately, it’s the intersection of data science and art.
In this episode, Michela Ledwidge joins Dr Genevieve Hayes to discuss how virtual reality and data science can be combined to create interactive data storytelling experiences.
Michela Ledwidge is the co-founder and CEO of Mod, a studio specialising in real-time and virtual production, and the creator of Grapho, a VR platform that lets non-technical users examine and manipulate graph data. She is also the writer and director of A Clever Label, a world-first interactive documentary.
The post Episode 51: Data Storytelling in Virtual Reality first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
When it comes to awareness and understanding, what we know and don’t know can be split into four categories: known knowns; unknown knowns; known unknowns; and unknown unknowns. And to quote former US Secretary of Defence Donald Rumsfeld: “If one looks throughout the history of our country and other free countries, it is the latter category that tends to be the difficult ones.”
When Rumsfeld made his famous “unknown unknowns” speech, he was referring to military intelligence. But the concept of “unknown unknowns” is just as relevant to data and data science. Those data dark spots, or data gaps, can be a real issue when it comes to data-driven decision making.
In this episode, Matt O'Mara joins Dr Genevieve Hayes to discuss the challenges and risks data gaps present to businesses and the community, and what data scientists can do to help address this issue.
Matt O'Mara is the Managing Director of information and insights company Analysis Paralysis and is the founder and Director of i3, which helps organisations use an information lens to realise significant value, increase productivity and achieve business outcomes. He is also an international speaker, facilitator and strategist and is the first and only New Zealander to attain Records and Information Management Practitioners Alliance (RIMPA) Global certified Fellow status.
Genevieve Hayes Consulting Episode 49: AI-Generated Advertising and the Future of Content Creation
The idea of targeted marketing is nothing new. Even before the advent of computers and data science, businesses have always tried to optimise their advertising campaigns by tailoring their advertisements to their ideal buyers.
Data science allowed businesses to become more effective at this targeting. However, it was still necessary for businesses to manually create the advertising content they wanted to share with their target buyers. That is, until recently.
In this episode, Hikari Senju joins Dr Genevieve Hayes to discuss how advances in AI technology have made it possible to generate personalised advertising content, optimised to produce the best results, and what that means for content creators.
Hikari Senju is the founder and CEO of Omneky, an AI platform that generates, analyzes and optimizes personalised advertising content at scale. He is a Harvard computer science graduate and also co-founded tutoring app Quickhelp, which he later sold to Yup.com.
The post Episode 49: AI-Generated Advertising and the Future of Content Creation first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 48: Overcoming the Machine Learning Deployment Challenge
It’s been 12 years since Thomas H Davenport and DJ Patil first declared data science to be “the sexiest job of the 21st century” and in that time a lot has changed. Universities have started offering data science degrees; the number of data scientists has grown exponentially; and generative AI technologies, such as Chat-GPT and Dall-E have transformed the world.
Yet, throughout that time, one thing has remained the same. Most machine learning projects still fail to deploy.
However, it’s not the technical capabilities of data scientists that let them down – those are now better than ever before. Rather, “it’s the lack of a well-established business practice that is almost always to blame.”
In this episode, Dr Eric Siegel joins Dr Genevieve Hayes to discuss bizML, the new “gold-standard”, six-step practice he has developed “for ushering machine learning projects from conception to deployment.”
Dr Eric Siegel is a leading machine learning consultant and the CEO and co-founder of Gooder AI. He is also the founder of the long-running Machine Learning Week conference series; author of the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die and the recently released The AI Playbook; and host of The Dr Data Show podcast.
The post Episode 48: Overcoming the Machine Learning Deployment Challenge first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 47: Leveraging Causal Inference to Drive Business Value in Data Science
For most people, data science is synonymous with machine learning, and many see the role of the data scientist as simply being to build predictive models. Yet, predictive analytics can only get you so far. Predicting what will happen next is great, but what good is knowing the future if you don’t know how to change it?
That’s where causal analytics can help. However, causal inference is rarely taught as part of traditional prediction-centric data science training. Where it is taught, though, is in the social sciences.
In this episode, Joanne Rodrigues joins Dr Genevieve Hayes to discuss how techniques drawn from the social sciences, in particular, causal inference, can be combined with data science techniques to give data scientists the ability to understand and change consumer behaviour at scale.
Joanne Rodrigues is an experienced data scientist with master’s degrees in mathematics, political science and demography. She is the author of Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights and the founder of health technology company ClinicPriceCheck.com.
The post Episode 47: Leveraging Causal Inference to Drive Business Value in Data Science first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 46: Empowering Democracy with LLMs
With all the reports about the spread of misinformation and disinformation on social media, sometimes it feels like one of the biggest threats to democracy is technology. But no technology is inherently good or bad. It’s how you use it that matters. And just as technology has the potential to harm democracy, it also has the potential to enhance it.
In this episode, Vikram Oberoi joins Dr Genevieve Hayes to discuss how he has been using generative AI and large language models (LLMs) to enhance people’s access to NYC council meetings through his work on citymeetings.nyc.
Vikram Oberoi is a software engineer, fractional CTO and co-owner of Baxter HQ, a boutique early-stage tech product development firm. He also built and operates citymeetings.nyc, an LLM powered tool to make New York City council meetings accessible.
The post Episode 46: Empowering Democracy with LLMs first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 45: AI-Powered Investment Insights
Succeeding in stock market investing is all about timing – buying low, selling high and being able to read the signs to determine when things are going to change. But as anyone who’s ever tried to get rich through stock trading can tell you, this is easier said than done.
Given the massive amounts of financial data published each day, for people who aren’t experts in the field, it can be too hard to spot the patterns and keep up with the constant change. As a result, many people are either investing in markets based on guesswork or not investing at all.
This is where AI can help, because there’s nothing that AI does better than finding patterns in large volumes of data. AI has the potential to democratize access to investment insights.
In this episode, Andrew Einhorn joins Dr Genevieve Hayes to discuss how AI can help ordinary investors find better investment opportunities than they could ever manage on their own.
Andrew Einhorn is the CEO and co-founder of Levelfields, an AI-driven fintech application that automates arduous investment research so investors can find opportunities faster and easier. Before moving into finance, Andrew started his career as an epidemiologist and helped build a pandemic monitoring system for Georgetown Hospital. He also previously co-founded tech company Synoptus, has consulted for NASA and served as an advisor to a $65 billion hedge fund.
The post Episode 45: AI-Powered Investment Insights first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 44: Designing Data Products People Actually Want to Use
As a data scientist, there’s nothing worse than devoting months of your time to building a data product that appears to meet your stakeholders’ every need, only to find it never gets used. It’s depressing, demotivating and can be devastating for your career.
But as the old saying goes, “You can lead a horse to water, but you can’t make it drink”. Or can you?
In this episode, Brian T O’Neill joins Dr Genevieve Hayes to discuss how you can apply the best techniques from software product management and UI/UX design to create ML and AI products your stakeholders will love.
Brian T O’Neill is the Founder and Principal of Designing for Analytics, an independent data product UI/UX design consultancy that helps data leaders turn ML & analytics into usable, valuable data products. He also advises on product and UI/UX design for startup founders in MIT’s Sandbox Innovation Fund; hosts the podcast Experiencing Data; founded The Data Product Leadership Community and maintains a career as a professional percussionist performing in Boston and internationally.
The post Episode 44: Designing Data Products People Actually Want to Use first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 43: Shaping the Future of AI
Two years ago, no one could imagine the impact generative AI would have on our world, and most of us can’t even begin to imagine the impact the next generation of AI will have on our world two years from now. The only thing that is certain is uncertainty.
But that uncertainty brings with it great opportunities and choices. We can choose to sit back and let the future of AI play out in front of us or engage with this new technology and shape the future of AI and the world as we know it.
In this episode, Dr Eric Daimler joins Dr Genevieve Hayes to discuss his extraordinary work in shaping the future of AI and what that future might look like.
Dr. Eric Daimler is the Chair, CEO and Co-Founder of Conexus AI and has previously co-founded five other companies in the technology space. He served under the Obama Administration as a Presidential Innovation Fellow for AI and Robotics in the Executive Office of President, as the sole authority driving the agenda for U.S. leadership in research, commercialization, and public adoption of AI & Robotics. He is also the author of the upcoming book The Future is Formal: The Roadmap for Using Technology to Solve Society’s Biggest Problems.
The post Episode 43: Shaping the Future of AI first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 42: Should You Outsource Your Data Team?
Chances are, you’re reading this summary on a device you didn’t build yourself. Why would you? Tech companies can build you a far better device for a much lower cost than you could ever manage alone. As with many other cases in life, this is an example of where it is better to buy than to build.
Yet, in building a data team, many organisations assume the only solution is to build from within. And although this may be the right solution for some organisations, building a solution isn’t right for all.
In this episode, Collin Graves joins Dr Genevieve Hayes to discuss what a bought solution might look like in the data science space, and whether it is right for you.
Collin Graves is the CEO of North Labs, a leading fractional cloud data analytics firm that helps growing companies become data-driven. Before founding North Labs, he served with distinction in NATO Special Operations during his tenure with the US Air Force. He is also the author of the upcoming Data Revolution: Leading with Analytics and Winning from Day One.
The post Episode 42: Should You Outsource Your Data Team? first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 41: Building Better AI Apps with Knowledge Graphs and RAG
When ChatGPT was first released, there was talk it would lead to traditional search engines, like Google, soon becoming obsolete. That was until users discovered generative AI’s one major drawback – it makes stuff up.
Because of the stochastic nature of ChatGPT, it is never going to be possible to completely eliminate hallucinations. However, there are ways to work around this issue. One such way is through leveraging knowledge graphs and retrieval augmented generation (or RAG).
In this episode, Kirk Marple joins Dr Genevieve Hayes to discuss how knowledge graphs and RAG can be leveraged to improve the quality of generative AI.
Kirk Marple is the CEO and Technical Founder of Graphlit, serverless, cloud-native platform that streamlines the development of AI apps by automating unstructured data workflows and leveraging retrieval augmented generation.
The post Episode 41: Building Better AI Apps with Knowledge Graphs and RAG first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 40: Making Data Science Teams Profitable
For many people, data science is synonymous with machine learning and many data science courses are little more than overviews of the most used machine learning algorithms and techniques.
Where the majority of data science courses fall short is they neglect to bridge the gap between data science theory and business reality, resulting in many data scientists who are technically strong but unable to create value from their work. However, this doesn’t necessarily have to be the case.
In this episode, Douglas Squirrel joins Dr Genevieve Hayes to discuss systems and techniques data scientists and their managers can use to make data science teams profitable.
Douglas Squirrel has been coding for forty-five years and has led software teams for twenty-five. He uses the power of conversations to create insane profits in technology organisations of all sizes. His experience includes growing software teams as a CTO in startups; consulting on product improvement; and coaching a wide variety of leaders in improving their conversations, aligning to business goals, and creating productive conflict.
The post Episode 40: Making Data Science Teams Profitable first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 39: The Impact of Data Science on Data Orchestration
One of the big promises of data science is its ability to combine multiple disparate datasets to produce value-creating insights. But this is only possible if you can get all those disparate datasets together, in the one location, to begin with. The has led to the rise of the data engineer and the data orchestration platform.
In this episode, Sandy Ryza joins Dr Genevieve Hayes to discuss the impact of the data scientist on the creation of the next generation of data orchestration tools.
Sandy Ryza is a data scientist turned data engineer who is currently the lead engineer on the Dagster project, an open-source data orchestration platform used in MLOps, data science, IOT and analytics. He is also the co-author of Advanced Analytics with Spark.
The post Episode 39: The Impact of Data Science on Data Orchestration first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 38 – The Art and Science of Survey Design
From BuzzFeed Quizzes to the national census, it’s impossible to get through life without encountering surveys. However, not all surveys are created equal. As with everything else in data science, garbage going in will inevitably lead to garbage coming out.
In this episode, Kyle Block joins Dr Genevieve Hayes to look at practical techniques for designing surveys to ensure they deliver value, as well as approaches to analysing survey results, to maximise that value.
Kyle Block is Head of Research at Gradient, an analytics agency that combines advanced statistical and machine learning techniques to answer difficult marketing challenges. He holds a Masters in Spatial Analysis from the University of Pennsylvania and has spent his career helping managers use data to make important decisions.
The post Episode 38 – The Art and Science of Survey Design first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 37: Data Privacy in the Age of AI
Most people have come to accept that the price of living in a technological world, and its associated convenience, is some loss of data privacy. However, few realise just how much privacy they are giving up.
In this episode, Dr Katharine Kemp joins Dr Genevieve Hayes to discuss data privacy challenges for consumers and data scientists in the age of AI.
Dr Katharine Kemp is an Associate Professor in UNSW’s Faculty of Law and Justice and Deputy Director of the Allens Hub for Technology, Law and Innovation. Her research focuses on competition, data privacy and consumer protection regulation, including their application to digital platforms.
The post Episode 37: Data Privacy in the Age of AI first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 36: Sequential Decision Problems
Decision-making is an essential part of everyday life and one of the main applications of data science is making the decision-making process easier.
However, mostly when data scientists build models, it’s to make a single decision. But in real life, decision-making is rarely that simple.
In this episode, Prof Warren Powell joins Dr Genevieve Hayes to discuss one way in which the decision-making process can become more complicated, in the form of sequential decision problems.
Warren Powell is the co-founder and Chief Innovation Officer of Optimal Dynamics and a Professor Emeritus after retiring from Princeton, where he was a faculty member in the Department of Operations Research and Financial Engineering. He is also the author of Sequential Decision Analytics and Modelling and Reinforcement Learning and Stochastic Optimization.
The post Episode 36: Sequential Decision Problems first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 35: Data-Driven Podcasting
According to the Interview Valet 2023 State of Podcast Guesting Annual Report, there are over 380,000 active podcasts in the world right now, with the average podcast episode receiving just 150 downloads within 30 days of its release.
So, for individuals and organisations looking to use podcast marketing to grow their business, just booking podcast guest appearances isn’t enough. It’s necessary to use a targeted strategy based on data.
In this episode, Tom Schwab joins Dr Genevieve Hayes to discuss how Interview Valet uses data to optimise business results in podcast interview marketing.
Tom Schwab is the founder and Chief Evangelist Officer of Interview Valet and the author of Podcast Guest Profits and One Conversation Away. He is also an engineer whose first job out of college involved running nuclear power plants in the US Navy.
The post Episode 35: Data-Driven Podcasting first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 34: Financial Modelling for Start-Up Founders
Start-ups and data science go hand in hand, but usually when people think about how data science can help start-ups, it’s with regard to product development and enhancement. However, it doesn’t matter how great a start-up’s product is, if the financials are a mess, the business is going to struggle.
This is where data science can also help start-ups, in the form of financial modelling and analysis.
In this episode, Lauren Pearl joins Dr Genevieve Hayes to discuss her work in helping start-up founders translate their business ideas into maths via financial models.
Lauren Pearl is a CEO-turned-CFO who helps start-up founders work better with financial data. She holds an MBA from NYU’s Stern School of Business and is the resident start-up finance expert at NYU’s Berkley Centre for Entrepreneurship.
The post Episode 34: Financial Modelling for Start-Up Founders first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 33: Making the Shift from Data Scientist to Datapreneur
Data science is among the most in-demand skills of the 21st century, with opportunities existing for data scientists to make a difference and earn good money as an employee in a range of industries. Yet there has also never been a better time to be a data science entrepreneur (or datapreneur).
But for data scientists who have never experienced the entrepreneurial life and who are used to the security of a steady pay check, making the transition from employee to entrepreneur may seem like an impossible leap, regardless of how desirable it may seem.
In this episode, David Shriner-Cahn joins Dr Genevieve Hayes to discuss how data scientists can escape the corporate world and make the transition from employee to datapreneur.
David Shriner-Cahn is the podcast host and community builder behind Smashing the Plateau, an online platform offering resources, accountability, and camaraderie to high-performing professionals who are making the leap from the corporate career track to entrepreneurial business ownership.
The post Episode 33: Making the Shift from Data Scientist to Datapreneur first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 32: Blockchain and Cryptocurrency for Data Science
Depending on who you speak to blockchain and cryptocurrency are either the way of the future or the scam of the century. But few would be able to tell you what either of them actually is – including among data scientists for whom data and technology are a way of life.
In this episode, Luke Willis joins Dr Genevieve Hayes to demystify blockchains, cryptocurrency and the data behind them.
Luke Willis is the dApp UX guy. He’s a web3 developer with extensive front end and UX experience. He’s also the founder of the Koin Press where he writes a regular newsletter, hosts the Koin Press podcast and helps others make their dApp ideas a reality.
The post Episode 32: Blockchain and Cryptocurrency for Data Science first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 31: The Business Leader as Data Consumer
When data science first became the must-have skill of the 21st century, organisations were fighting to recruit the best and brightest data science talent. But the glory of having a data scientist on staff was often short-lived, as many organisations soon found they didn’t know what to do with them.
Business leaders had been sold the dream of being able to turn their data into business gold but were unable to maximise the value of the data science expertise they had brought in because they couldn’t communicate effectively with their new data science teams.
In this episode, Dr Howard Friedman joins Dr Genevieve Hayes to discuss how adopting a customer mindset can help business leaders capitalise on the hidden value of data.
Dr Howard Steven Friedman is a data scientist, health economist, and writer with decades of experience leading data modelling teams in the private sector, public sector and academia. He is an adjunct professor, teaching data science, statistics, and program evaluation, at Columbia University, and has authored/co-authored over 100 scientific articles and book chapters in areas of applied statistics, health economics and politics. His previous books include Ultimate Price and Measure of a Nation, which Jared Diamond called the best book of 2012.
The post Episode 31: The Business Leader as Data Consumer first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 30: Cause and Effect Data Science
Correlation does not equal causation, as anyone who has studied statistics or data science would know. But understanding causality isn’t just important when you’re developing models.
If you’re working in business and want to be recognised for your work, it’s essential to be able to demonstrate causality between what you do and the benefit flowing through to the business.
In this episode, Mark Stouse joins Dr Genevieve Hayes to discuss how data science can be used to comprehend the underlying cause-and-effect relationships in business data.
Mark Stouse is the CEO of Proof Analytics, an AI-driven marketing analytics platform. Prior to becoming an analytics software CEO, Mark had a successful career in B2B marketing and in 2014 was named Innovator of the Year at the Holmes Report In2 SABRE Awards for his work in tying marketing and communication investment to key business performance metrics.
The post Episode 30: Cause and Effect Data Science first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 29: Creating Order From Data Chaos
The insurance sector owes its existence to data and insurers were some of the first companies to utilise data expertise. Yet, being an early adopter isn’t always as great as it seems. And many big insurers are now discovering the challenges of bringing their long-established data systems into the 21st century.
In this episode, Maria Ferrés joins Dr Genevieve Hayes to discuss the complexities of creating order from data chaos in the insurance industry.
Maria Ferrés is an actuary with extensive experience throughout Europe and Australia, who now specialises in establishing the enterprise data functions of multinational insurers. She is currently the Enterprise Data Officer at trade credit insurer Atradius and she also advises companies within the insurtech space on the use of data to comply with Data Protection laws.
The post Episode 29: Creating Order From Data Chaos first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 28: The Data Science Behind ChatGPT
ChatGPT was one of the best things to ever happen to data science – not so much because of what it can do, but because, virtually overnight, it made AI and data science mainstream.
However, while most data scientists now have experience with ChatGPT and other large language model (LLM)-based technologies as end users, few have had experience in building their own LLM-based tools.
In this episode, Dr Mudasser Iqbal joins Dr Genevieve Hayes to discuss the data science behind LLMs and how to go about doing just that.
Dr Mudasser Iqbal is the Founder and CEO of TeamSolve, a company dedicated to leveraging AI for digital transformation with a sustainable focus. He has extensive experience in Industrial AI, including multiple patents, and was recognised as an MIT Young Innovator. He also played a key role in the growth of his previous start-up, Visenti, and its subsequent acquisition by Xylem Inc.
The post Episode 28: The Data Science Behind ChatGPT first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 27: The Future of Technology in Financial Services
Despite its conservative reputation, the financial services industry has always been a big adopter of cutting-edge technologies. Dating back more than a century, it’s also been one of the biggest employers of people with technology and data-related skills. But what does the future hold for the use of tech in the financial services industry?
In this episode, Ben Shapira joins Dr Genevieve Hayes to discuss what this future might look like and how technology is being used right now to improve the lives of consumers.
Ben Shapira is a digital strategist and UX specialist turned tech entrepreneur. He is the founder and Chief Product Officer of Australian fintech start-up Dinero, as well as being a lecturer in the Master of Media and Communication program at Swinburne University.
The post Episode 27: The Future of Technology in Financial Services first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 26: Data Storytelling and Data-Informed Education
Data science is only useful if it can create value. And one way that value can be created is by using data to influence decision-making. Yet, to influence decisions, data scientists need to effectively communicate the outcomes of their work – which is something many struggle with. This is because effective data science communication is about more than just rattling off statistics and expecting your end users to piece them together.
In this episode, Dr Selena Fisk joins Dr Genevieve Hayes to discuss how data scientists can improve their communication by using those numbers to tell a story.
Dr Selena Fisk is a data storyteller and researcher, with a background in education, who now works with the corporate sector to develop data-informed strategies. She is also the author of a number of books, including I’m Not a Numbers Person: How to Make Good Decisions in a Data-Rich World and Data-Informed Learners: Engaging Students in their Data Story.
The post Episode 26: Data Storytelling and Data-Informed Education first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 25: The Risks of Applying Data Science to Financial Modelling
Pretty much everyone has a retirement plan, but those plans aren’t always robust enough to see you through to the finish line of life. And part of that is a direct consequence of incorrectly applying data science principles to financial modelling.
In this episode, Todd Tresidder joins Dr Genevieve Hayes to discuss the risks and limitations of using data science when planning for retirement.
Todd Tresidder is a former hedge fund manager who “retired” at age 35 to become a financial consumer advocate and money coach. He now runs the popular retirement planning website FinancialMentor.com and is the author of a range of books on retirement planning and investments including How Much Money Do I Need to Retire? and The Leverage Equation.
The post Episode 25: The Risks of Applying Data Science to Financial Modelling first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 24: AI and IP
If you look at the list of the greatest inventions of the 20th century, you’ll find they all have two things in common. From tea bags to toasters and from cell phones to cellophane, they all take the form of physical objects, and all are, or at least were, protected by patents.
Yet, since the turn of the century, the nature of inventions has changed significantly. And many of the greatest inventions of this century now take the form of computer code or models.
But how do you protect an invention you can’t physically touch?
In this episode, Helen McFadzean joins Dr Genevieve Hayes to discuss the intersection of artificial intelligence and intellectual property.
Helen McFadzean is a patent and trademark attorney, with a background in artificial intelligence and mechatronics engineering. She has successfully obtained patents, trademarks and designs for businesses in Australia and overseas in a large number of technology areas including machine learning and image classification, automation, smart devices, audio signal processing, embedded software, and control systems.
The post Episode 24: AI and IP first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 23: Reinforcement Learning – The Other Type of Machine Learning
Most Intro to Machine Learning courses cover supervised learning and unsupervised learning. But did you know there is also a third type of machine learning, which was used in the development of ChatGPT and is likely to become increasingly important in the not too distant future?
In this episode, Prof Michael Littman joins Dr Genevieve Hayes to discuss reinforcement learning – the other type of machine learning – as well as his new book, Code to Joy: Why Everyone Should Learn a Little Programming.
Prof. Michael Littman is an award-winning Professor of Computer Science at Brown University, specialising in reinforcement learning; is co-creator of the Machine Learning and Reinforcement Learning courses offered as part of Georgia Tech’s Online Master of Science in Computer Science (OMSCS) program; and is currently serving as Division Director for Information and Intelligent Systems at the (US) National Science Foundation. He is also the author of Code to Joy: Why Everyone Should Learn a Little Programming.
The post Episode 23: Reinforcement Learning – The Other Type of Machine Learning first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 22: Software Engineering for Data Science
Data science sits at the intersection of Computer Science and Statistics, so it comes as no surprise that many of the best data scientists have a computer science or software development background. And those that don’t? Well, there’s a lot they can learn from software developers.
In this episode, Ethan Garofolo joins Dr Genevieve Hayes to discuss techniques from software engineering and software development that you can use to become a better data scientist.
Ethan Garofolo is a software developer and software architect, specialising in microservice-based projects and using Lean and DevOps principles to make software development teams more effective. He is the author of Practical Microservices: Build Event-Driven Architectures with Event Sourcing and CQRS and runs the Utah Microservices Meetup group.
The post Episode 22: Software Engineering for Data Science first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 21: Responsible Data Sourcing for AI Model Building
The saying goes that if you’re not paying for the product, then you are the product. And every time you interact with the digital world, there’s a good chance your data is going to be harvested for some alternative use.
In this episode of Value Driven Data Science, Dr Kate Bower joins Dr Genevieve Hayes to discuss the data rights of consumers and what data scientists need to be aware of when using consumer data.
Dr Kate Bower is a consumer data advocate for Australian consumer advocacy group CHOICE, following a previous career in academia, where her focus was on qualitative health research.
The post Episode 21: Responsible Data Sourcing for AI Model Building first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 20: Using Data Science to Live Better for Longer
We all want to live long, happy and healthy lives, and in the age of technology, it comes as little surprise that people are turning to data for help.
Between smart watches, Oura rings and even just fitness apps like Strava, we’re all generating massive quantities of personal health and fitness data each day, sometimes literally in our sleep. But that data is only valuable if it can be converted into useful insights.
In this episode of Value Driven Data Science, Dr Torri Callan joins Dr Genevieve Hayes to discuss how health tech start-ups, such as UAre, are now looking to do just that.
This is the third part of a three-part special focussing on the use of data science in start-ups.
Dr Torri Callan is the Data Scientist at Australian health tech start-up UAre, as well as working as a data scientist with fintech start-up Spriggy. He has spent the past 5 years setting up AI and automated risk management for leading finance companies in Australia.
The post Episode 20: Using Data Science to Live Better for Longer first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 19: The Democratisation of AI and Data Science
Once upon a time, data scientists needed to develop programming skills to rival those of software engineers, and this limited the ability of people without such skills to make use of AI. But recently, this has changed, with the huge number of no-code and low-code tools entering the market.
In this episode, I’m joined by Geo George to discuss how start-ups are leading the way in leveraging such tools, and in the process, helping to make AI and data science available to all.
This is the second part of a three-part special focussing on the use of data science in start-ups.
Geo George is a director and co-founder of Mayfly Accelerator, a company that helps founders build, grow and scale disruptive start-ups. He is also a start-up founder in his own right and has experience as an executive in the Government sector, with a focus on strategy and risk management.
The post Episode 19: The Democratisation of AI and Data Science first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 18: Making AI Commercially Viable
Many data scientists dream of using their skills to develop ground-breaking AI technology. Yet, few manage to translate their dreams into commercially viable products – or even know where to begin.
In this episode, start-up founder Dr Jeroen Vendrig joins Dr Genevieve Hayes to discuss his experiences in developing AI-driven products, both in an academic setting and in a variety of organisations within the commercial world.
This is the first part of a three-part special focussing on the use of data science in start-ups.
Dr Jeroen Vendrig is the Chief Technology Officer of ProofTec, an Australian technology start-up specialising in the development of AI-driven software for damage detection and assessment of high value assets. He has over 20 years’ experience in video analytics with world leading R&D labs and has over 25 patents in force.
The post Episode 18: Making AI Commercially Viable first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 17: How to Avoid an AI Scandal
AI technology has now reached the point where it can potentially damage the reputation of an organisation, if improperly managed. As a result, many data scientists are now becoming very interested in understanding AI ethics and responsible AI.
In this episode of Value Driven Data Science, Chris Dolman joins Dr Genevieve Hayes to discuss strategies organisations and data scientists can apply to de-risk automated decisions, and in doing so, avoid an AI scandal.
Chris Dolman is the Executive Manager, Data and Algorithmic Ethics at Insurance Australia Group, a Gradiant Institute Fellow and regularly contributes to external research on responsible AI and AI ethics. In 2022, he was named the Australian Actuaries Institute’s Actuary of the Year, in recognition of his work around data ethics, and was also included in Corinium Global Intelligence – Business of Data’s list of the Top 100 Innovators in Data and Analytics.
The post Episode 17: How to Avoid an AI Scandal first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 16: Improving the Data Science Customer Experience
The launch of Chat-GPT turned the business world upside down and left many people wondering about the future of their careers. How do you compete against AI? One solution is by delivering a superior customer experience.
In this episode, Dasun Premadasa joins Dr Genevieve Hayes to discuss why technical people often trip up when it comes to customer experience and what data scientists can do to overcome these issues.
Dasun Premadasa is the founder of DASCX, an independent business analyst consultancy that helps businesses with their digital transformations and IT project delivery. He is also the host of the DASCX Show on YouTube.
The post Episode 16: Improving the Data Science Customer Experience first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 15: Graph-Powered Data Science
From social media to electricity grids and the internet itself, we live in a highly interconnected world. But traditional data science techniques don’t adequately allow for the relationships that can exist between data points in such networks. This is where graph data analysis comes into play.
In this episode, Dr Alessandro Negro joins Dr Genevieve Hayes to discuss how data scientists can exploit the natural relationships that exist within network datasets through the use of graph-powered machine learning.
Dr Alessandro Negro is the Chief Scientist at GraphAware, the world’s #1 Neo4j consultancy, and Managing Director at GraphAware Italy. He is also the author of Graph-Powered Machine Learning and the recently released Knowledge Graphs Applied.
The post Episode 15: Graph-Powered Data Science first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 14: Building Your Authority in Data Science
Data science is an in-demand skill. Yet, many data scientists find it challenging to get started in the industry and to differentiate themselves from other data scientists once they find a job.
In this episode, Jonathan Stark joins Dr Genevieve Hayes to discuss how data scientists can find their niche and build a reputation as a data science authority.
Jonathan Stark is a former software developer who now helps independent professionals make a living while increasing their impact on the world. He is the author of Hourly Billing Is Nuts, the host of the podcast Ditching Hourly and the co-host of The Business of Authority.
The post Episode 14: Building Your Authority in Data Science first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 13: Breeding Data Science Unicorns
“Data science unicorns” are those rare people who “understand the (data) problem they seek to resolve, have the mathematical expertise to analyse the problem and possess the computing skills to covert this knowledge into outcomes.” In fact, they are considered so rare that some people have suggested they don’t really exist. Yet, although nobody is born a data science unicorn, organisations with the right know-how can create them.
In this episode, Dr Peter Prevos joins Dr Genevieve Hayes to discuss his work in creating data science unicorns from water industry subject matter experts around the world.
Dr Peter Prevos is a civil engineer, social scientist (and amateur magician) who manages the data science function at Coliban Water in regional Australia and runs courses in data science for water professionals. He is also the author of a number of books including Principles of Strategic Data Science and the recently released Data Science for Water Utilities.
The post Episode 13: Breeding Data Science Unicorns first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 12: The Role of Data in Environmental Justice
Are you familiar with “environmental justice”? It’s all about equitable access to environmental amenities and the equitable distribution of pollution, and has its roots in the American Civil Rights movement of the 1960’s and 1970’s.
In this episode, Robin Rotman and Amber Spriggs join Dr Genevieve Hayes to discuss the environmental justice movement and how open access GIS-based tools are being used to achieve environmental justice in the USA today.
Robin Rotman is an Assistant Professor of Energy and Environmental Law and Policy at the University of Missouri-Columbia. She is also a qualified lawyer, focussing on energy, environmental, and natural resource issues, and is a Counsel at Van Ness Feldman, a law firm in Washington DC.Amber Spriggs is a civil engineering Masters student at the University of Missouri-Columbia with a research focus on hydrology, hydraulic engineering, GIS-based risk assessment, and flood insurance policy.
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Genevieve Hayes Consulting Episode 11: Better Workplace Conversations for Data Scientists
Data scientists are constantly being told of the importance of effective communication for their career success. But this advice typically translates to being able to communicate effectively the results of their work. One aspect of communication that is often overlooked is conversational communication.
In this episode, Julia Lessing joins Dr Genevieve Hayes to discuss the skills and techniques data scientists can combine to make their workplace conversations a lot easier.
Julia Lessing is the principal actuary and Director of Guardian Actuarial, which specialises in helping clients use data to solve complex people-oriented problems, and runs the Guardian Actuarial Leadership Program and the Easier Conversations course. She is also the host of the We Are Actuaries podcast and has trained and served as a Lifeline phone counsellor.
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Genevieve Hayes Consulting Episode 10: ChatGPT and the Future of Human Computer Interaction
In December 2022, OpenAI released ChatGPT for public testing and within a week of its launch, the user count exceeded 1 million. For many, ChatGPT provided a first glimpse at what an AI-powered future might look like.
In this episode, Dr Genevieve Hayes is joined once again by Dr David Joyner to discuss the implications of AI-driven technology, such as ChatGPT, for education, business and the world in general, and to finish their discussion of Georgia Tech’s OMSCS program.
This is the second part of a two-part conversation, which began in Episode 9.
Dr David Joyner is the Executive Director of Online Education and the Online Master of Science in Computer Science at Georgia Tech’s College of Computing. Between 2019 and 2021 he taught a total of 21,768 for-credit college students, more than any other person on the planet. He is also the author of the recently released Teaching at Scale, and co-author of The Distributed Classroom.
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Genevieve Hayes Consulting Episode 9: Learning Data Science at Scale with OMSCS
What if you could get a Masters degree in Machine Learning for under US$8000, from a top US university, without quitting your day job or moving location? Georgia Tech’s pioneering Online Master of Science in Computer Science (OMSCS) program offers just that. In this episode, Dr David Joyner joins Dr Genevieve Hayes to discuss OMSCS, the world’s first MOOC-based degree.
This is the first part of a two-part conversation, which is continued in Episode 10.
Dr David Joyner is the Executive Director of Online Education and the Online Master of Science in Computer Science at Georgia Tech’s College of Computing. Between 2019 and 2021 he taught a total of 21,768 for-credit college students, more than any other person on the planet. He is also the author of the recently released Teaching at Scale, and co-author of The Distributed Classroom.
The post Episode 9: Learning Data Science at Scale with OMSCS first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
Genevieve Hayes Consulting Episode 8: Data Science in the Metaverse
Ever since Facebook rebranded itself as Meta, the term “metaverse” has entered everyone’s vocabulary, but there’s still a lot of confusion about what it actually is and how it’s likely to affect our lives in the future. In this episode, Romeo Cabrera Arévalo, a data scientist working in the immersive technology space, joins Dr Genevieve Hayes to answer these questions and more.
Romeo Cabrera Arévalo is a senior AI and computer vision researcher and engineer at Immersed, “the world’s first professional metaverse.” He is also an AI and tech advisor to the Board of Laboratorio iA, and has lectured in the Masters of Data Science program at the Escuela Superior Politéchnica del Litoral.
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Genevieve Hayes Consulting Episode 7: Finding and Retaining the Best Data Talent
Over the past decade, demand for data talent has grown exponentially, and this has had a massive impact on talent acquision in the data space. Employers of data professionals frequently cite talent acquisition as one of the biggest challenges they face in building their internal data capabilties. In this episode, Dr Genevieve Hayes is joined by data recruiter Joel Robinstein to discuss the data science recruitment landscape, including practical advice for both data scientists and those looking to employ them.
Joel Robinstein is Head of Clients Services and Operations at Precision Sourcing Australia, where he has over 12 years’ experience working in the data recruitment space. He is also the co-host of the podcast Keeping Up With Data.
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Genevieve Hayes Consulting Episode 6: Bridging the Chasm Between Data Science and Engineering
The success of data science projects often depends on being able to get stakeholders, from a variety of backgrounds, to work well together. But what if the stakeholders involved come from very different backgrounds and struggle to understand each other – as can be the case with data scientists and engineers? In this episode, Dr Genevieve Hayes is joined by software engineer turned data scientist Hendrik Dreyer, who has carved a niche for himself by acting as a intermediary between Team Data Science and Team Engineering.
Hendrik Dreyer is both a qualified data scientist and a qualified engineer. He worked extensively in a range of senior software engineering roles, in both South Africa and Australia, prior to making the transition into data science. He is now the Manager of Analytics Capability at Australia’s largest superannuation fund, AustralianSuper.
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Genevieve Hayes Consulting Episode 5: Identifying Data Science Use Cases for your Business
Businesses rarely approach data scientists with well-defined problems to solve. Sometimes, the problems businesses devise aren’t appropriate for solving using data science at all. This makes it difficult for data science projects to succeed. In this episode, Dr Genevieve Hayes is joined by Rob Deutsch to discuss strategies businesses and data scientists can employ to identify data science use cases and maximise their probability of success.
Rob Deutsch is the Chief Operating Officer of AkuShaper, a company that uses advanced modelling algorithms and software to build better surfboards faster. He is also a data science consultant with Parity Analytic, and previously founded Boxer, which built software for creating better financial models.
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Genevieve Hayes Consulting Episode 4: The Role of the Board in Maximising the Value of Data
Have you ever wondered what your organisation’s Board are thinking, when it comes to data use? In this episode, Dr Genevieve Hayes is joined by Dr Stuart Black to discuss the attitudes of Boards to data use and their implications for the organisations they govern.
Dr Stuart Black is an Enterprise Fellow in data, analytics, disruption and innovation at the University of Melbourne. Prior to joining academia, Stuart spent 30 years in professional services and industry, at employers including Deloitte, where he was Senior Partner, National Australia Bank and AT Kearney. He is also a co-author of the recently released book Business Model Transformation – the AI and Cloud Technology Revolution.
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Genevieve Hayes Consulting Episode 3: Fairness and Anti-Discrimination in Machine Learning
We all know what it means for a human to discriminate against another human, but the concept of a predictive model or an artificial intelligence is relatively new. What does it mean for a model or an AI to discriminate against someone? In this episode of Value Driven Data Science, Dr Genevieve Hayes is joined by Dr Fei Huang to discuss the importance of considering fairness and avoiding discrimination when developing machine learning models for your business.
Dr Fei Huang is a senior lecturer in the School of Risk and Actuarial Studies at the University of New South Wales, who has won awards for both her teaching and her research. Her main research interest is predictive modelling and data analytics, and has recently been focussing on insurance discrimination and pricing fairness.
Fei’s papers on fairML and insurance pricing:
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Genevieve Hayes Consulting Episode 2: Leading a Technical Team – Transitioning from Individual Contributor to Manager and Beyond
The two most challenging transitions you can make in your career are transitioning from individual contributor to team lead, and moving from team lead to managing managers. This is true across all professions, but is particularly pronounced in technical fields, like data science.
In this episode, host Dr Genevieve Hayes is joined by guest Tim Davey to discuss the challenges faced by data scientists looking to climb the corporate ladder, and how employers of data professionals can support them in developing their careers.
Tim Davey has spent the majority of his career working in the organisational development and HR space where his work has focussed strongly on the development of leaders and working with individuals to understand and maximise their careers. This has included, among other things, providing executive coaching to senior management across a wide range of industries, including media, the performing arts, manufacturing, financial services, transport, education, insurance, legal, and not-for-profit sectors.
Yet, Tim also has a strong technical background himself, having completed a Science degree at the University of Melbourne, and starting his working career in the chemical manufacturing sector, so has first-hand understanding of the challenges faced by the members and leaders of technical teams.
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Genevieve Hayes Consulting Episode 1: Building Data Science Capability in Data-Focussed Teams
Data presents incredible opportunities for organisations to create value, but with the current skills and labour shortages that are affecting all businesses, finding and retaining data scientists and other data professionals can be hard. In this episode, host Dr Genevieve Hayes is joined by guest Amanda Aitken to discuss a practical way in which organisations can address the skills shortage, gain much needed data skills and increase staff retention – by upskilling their existing staff.
Amanda Aitken is a fully-qualified actuary who is currently an educator with the Actuaries Institute of Australia. She teaches data analytics and data science to actuaries through the Actuaries Institute’s Data Analytics Application course and is also a member of the Institute’s Data Analytics Practice Committee and Data Analytics Education Faculty.
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En liten tjänst av I'm With Friends. Finns även på engelska.