Value Driven Data Science

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:

1. Why lacking clear intention kills productivity — and how to ensure every analysis drives real decisions. [02:11]
2. A powerful “storyboarding” framework for turning vague requests into actionable projects. [09:51]
3. How to deliver results faster using modern data architectures and raw data analysis. [13:19]
4. The game-changing mindset shift that transforms data scientists from order-takers into trusted strategic partners. [17:05]

Guest Bio

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.

Links

• Connect with Ben on LinkedIn

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

Read Full Transcript

[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:

1. Why focusing purely on technical metrics like accuracy and precision is sabotaging your success — and what metrics actually matter to business leaders. [04:18]
2. The critical mindset shift needed to transform from a back-room technical specialist into a valued business partner. [30:33]
3. How to present data science insights in ways that drive action — and why your fancy graphs might be hurting rather than helping. [25:08]
4. Why “data driven” isn’t enough, and how to adopt a “data informed” approach that delivers real business outcomes. [54:08]

Guest Bio

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”.

Links

• Connect with Bill on LinkedIn
• Connect with Mark on LinkedIn
• Connect with John on LinkedIn

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

Read Full Transcript

[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.

Guest Bio

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.

Highlights

• (00:05) Meet Barzan Mozafari
• (00:50) The role of AI in data engineering
• (01:36) The birth of Keebo
• (02:34) Challenges in modern data pipelines
• (05:41) How Keebo optimizes data warehousing
• (07:35) AI and ML techniques behind Keebo
• (08:47) Reinforcement learning in practice
• (16:23) Guardrails and safeguards in AI systems
• (26:29) The build vs. buy dilemma
• (36:03) Future trends in data science and AI
• (39:36) Final advice for data scientists
• (40:50) Closing remarks and contact information

Links

• Keebo website
• Connect with Barzan on LinkedIn

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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.

Guest Bio

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.

Highlights

• (00:05) Meet Michela Ledwidge
• (02:04) Michela’s journey from Commodore 64 to interactive filmmaking
• (06:40) The birth of Mod and remixable films
• (14:48) Exploring graph databases and data science techniques
• (25:33) The future of data science and AI in creative industries
• (32:27) Grapho: Data science + storytelling in virtual reality
• (48:29) The future of data science and storytelling
• (49:37) Conclusion and contact information

Links

• Grapho website

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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.

Guest Bio

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.

Highlights

• (00:55) Understanding information gaps
• (02:33) Matt O'Mara's journey and insights
• (04:58) Real-world examples of information gaps
• (07:30) The impact of information gaps on society
• (11:54) Organizational challenges and solutions
• (25:55) Critical information sources and management
• (31:33) Developing an information lens
• (42:47) The role of data scientists in addressing information gaps
• (45:29) Conclusion and contact information

Links

• i3 website
• Connect with Matt on LinkedIn

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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.

Guest Bio

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.

Highlights

• (02:06) How OmneKey works
• (03:29) Personalisation in advertising
• (06:35) The role of human input in AI-generated content
• (10:45) Impact of AI on the advertising industry
• (15:09) Hikari Senju’s journey and insights
• (19:53) Technical deep dive into OmneKey
• (25:54) The competitive landscape of AI
• (32:10) The future of content and AI
• (40:26) Conclusion and final thoughts

Links

• Omnekey website
• Connect with Hikari on LinkedIn
• Follow Hikari on X

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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.”

Guest Bio

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.

Highlights

• (01:21) Challenges in machine learning deployment
• (05:00) The importance of business involvement in ML projects
• (15:39) Defining bizML and its steps
• (25:32) Understanding predictive analytics
• (26:52) Challenges in model deployment and MLOps
• (29:12) BizML for generative and causal AI
• (31:25) Exploring uplift modeling
• (35:45) Gooder AI: bridging the gap between data science and business value
• (45:45) Beta testing and future plans for Gooder AI
• (47:35) Final advice for data scientistsb

Links

• BizML website

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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.

Guest Bio

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.

Highlights

• (00:49) Combining social sciences with data science
• (02:01) Joanne’s journey from social sciences to data science
• (04:15) Understanding causal inference
• (07:40) Real-world applications of causal inference
• (12:22) Challenges in causal inference
• (19:41) Correlation vs. causation in data science
• (26:12) Operationalising randomness in experiments
• (27:16) Observational experiments vs. medical trials
• (27:47) Designing experiments with existing data
• (28:50) Challenges in natural experiments
• (29:55) Ethical considerations in experimentation
• (31:50) Qualitative frameworks in causal inference
• (35:58) Integrating causal inference with machine learning
• (38:59) Common techniques in causal inference
• (41:02) Marketing causal inference to management
• (43:48) Ethical implications of predictive modelling
• (48:08) Final advice for data scientists

Links

• Connect with Joanne on LinkedIn
• Joanne’s website

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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.

Guest Bio

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.

Highlights

• (00:00) Meet Vikram Oberoi
• (01:31) Overview of citymeetings.nyc
• (07:50) Vikram’s journey into local politics
• (12:05) Technical aspects of citymeetings.nyc
• (18:41) Dealing with AI hallucinations
• (25:00) Understanding the different types of AI errors
• (26:05) Case study: Honeycomb’s query feature
• (26:59) Reinforcement learning with human feedback
• (28:32) Choosing between Claude and GPT
• (31:42) The importance of context windows
• (40:31) Effective prompt engineering tips
• (46:11) Final advice for data scientists

Links

• citymeetings.nyc
• Vikram’s website
• Vikram’s talk at NYC School of Data about citymeetings.nyc
• Follow Vikram on X

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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.

Guest Bio

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.

Highlights

• (00:06) Meet Andrew Einhorn
• (02:54) Andrew’s journey from public health to data science
• (07:55) The birth of Levelfields
• (19:35) Event-driven investment insights explained
• (26:22) AI and data science behind Levelfields
• (36:36) User experience and customisation
• (41:03) Future developments and final advice

Links

• Levelfields website

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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.

Guest Bio

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.

Highlights

• Introducing Brian T. O’Neill (00:19)
• Brian’s journey from music to data product design (02:16)
• Understanding the real needs of stakeholders (06:45)
• The importance of user-centered design in data products (09:33)
• Gaining insights through direct user interaction (12:16)
• Focusing on business and user experience outcomes (17:48)
• Debunking the myths of self-serve analytics and dashboarding (22:46)
• Data platforms vs. data products (27:26)
• Defining a data product: the value exchange principle (29:08)
• Designing human-centered data products (32:56)
• The CED framework: conclusions, evidence, data (36:01)
• Final advice for data scientists (45:06)

Links

• Brian’s mailing list
• Designing for Analytics
• Data Product Leadership Community
• CED framework

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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.

Guest Bio

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.

Highlights

• (00:00) Meet Dr. Eric Daimler
• (01:46) Eric’s role in the Obama Administration
• (06:32) Challenges in government data integration
• (10:31) The importance of technical expertise in policy
• (16:06) Founding Connexus AI
• (18:09) Understanding category theory
• (20:51) Applications of Conexus AI
• (27:16) The future of AI: safe and symbolic
• (38:35) Insights from Eric’s upcoming book
• (47:49) Advice for data scientists and final thoughts

Links

• Connect with Eric on LinkedIn
• Conexus AI website

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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.

Guest Bio

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.

Highlights

• (01:43) Collin’s journey from the US Air Force to data science
•  (09:53) The birth of North Labs: a fractional data analytics firm
•  (12:02) Scaling a one-man operation to a thriving business
•  (13:58) The challenges of using data in the industrial and manufacturing sector
•  (28:41) The power of outsourcing data science
•  (34:09) The future of data teams and the role of in-house expertise
•  (41:44) Insights from Collin’s upcoming book
•  (46:17) Final thoughts and advice for data scientists

Links

• Connect with Collin on LinkedIn
• North Labs website

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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.

Guest Bio

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.

Highlights

• (00:19) Meet Kirk Marple
• (01:22) Leveraging knowledge graphs and RAG
• (06:08) Challenges with named entity extraction
• (09:16) Cost implications of LLMs
• (12:17) Deep dive into RAG
• (16:58) Vector search explained
• (20:49) Graph databases and RAG
• (38:58) Future of RAG and AI
• (43:08) Final thoughts

Links

• Connect with Kirk on LinkedIn
• Graphlit website
• Follow Graphlit on X

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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.

Guest Bio

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.

Highlights

• Douglas Squirrel’s journey: From CTO to profitability guru (00:00)
• Integrating data science with business goals (10:58)
• The surprising technological growth in Africa (17:38)
• Overcoming the Walled Garden: strategies for tech team success (19:14)
• The Lean Startup approach to data science (26:48)
• The importance of direct feedback in data science (32:50)
• Transforming data science with human empathy (33:39)
• Leveraging action science for effective communication (42:46)
• Elephant Carpaccio (47:41)
• Techniques for data scientists to create business value (51:22)
• Creating productive conflict for business innovation (53:43)
• Final thoughts and resources (01:00:28)

Links

• Douglas Squirrel’s website
• Squirrel Squadron

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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.

Guest Bio

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.

Highlights

• Welcome to Value Driven Data Science (00:00)
• Introducing Sandy Ryza and his journey from data scientist to data engineer (01:30)
• Navigating the challenges of creating consistent data definitions within teams (05:11)
• The birth and development of Dagster (11:32)
• Dagster: A tool designed for data scientists (20:54)
• Final thoughts and advice for data scientists (37:29)

Links

• Connect with Sandy on LinkedIn
• Follow Sandy on X
• Dagster

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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.

Guest Bio

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.

Talking Points

• What good survey design looks like.
• Advice on how to design effective surveys.
• How list experiments can be used to uncover true opinions around sensitive topics.
• How data science techniques can be applied to survey data analysis to maximise its value.
• What the future might hold for survey data analysis.

Links

• Connect with Kyle on LinkedIn
• Gradient Website

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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.

Guest Bio

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.

Talking Points

• What types of data are companies collecting about their customers?
• How companies currently de-identify customer data to ensure consumer privacy is protected.
• The effectiveness of data de-identification methods at truly protecting the privacy of individuals.
• The state of current consumer data privacy laws and how they are likely to evolve.
• The impact of generative AI tools, such as ChatGPT, on consumer data privacy.

Links

• UNSW Allens Hub for Technology, Law and Innovation
• Katharine’s Research (SSRN Page)
• Consumer Policy Research Centre
• Singled Out Report

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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.

Guest Bio

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.

Talking Points

• What is a sequential decision problem?
• Real-life examples of sequential decision problems and the disciplines in which they occur.
• The four main classes of techniques for solving sequential decision problems.
• How Warren’s approach to addressing sequential decision problems differs from the standard approach in this space.
• The challenges of implementing sequential decision analysis techniques in practice.

Links

• Connect with Warren on LinkedIn
• Warren’s website (SDA Links)

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

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.

Guest Bio

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.

Talking Points

• What is podcast interview marketing and how it differs from traditional digital marketing approaches?
• How Tom uses data to inform podcast guest marketing strategies at Interview Valet.
• The most important metrics for targeting podcast marketing and optimising return on investment.
• What makes a top podcast?
• How Tom’s use of data and analytics has evolved over time.

Links

• Interview Valet

• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE

The post Episode 35: Data-Driven Podcasting first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.