Automating Analytics Teams

Derek Knudsen (@dsknudsen, CTO at @Alteryx) talks about the differences between analytics and data science teams, critical analytics workflows, aligning culture and technologies, and best practices in presenting data. 

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SHOW NOTES:

• Alteryx Homepage - Automated, Self-Service Analytics
• Alteryx Analytics Platform (APA)

Topic 1 - Welcome to the show. Tell us a little bit about your background, and what makes you passionate about helping analytics teams improve their businesses? 

Topic 2 - Can we start by talking about how you think about Analytics teams vs. Data Science teams vs. AI/ML teams? Are these different only in name, or are their functional/skill differences, or places where one group is more appropriate than others? 

Topic 3 - Let’s talk about Analytics in the context of workflows. Are you seeing it still be mostly a business analyst “offline” function, or are more workflows and applications introducing more “real-time” analytics capabilities? 

Topic 4 - We talk a lot on this show about DevOps and Developer Productivity, in the context of more frequently changing applications. How does that apply to Analytics groups? Where do they have bottlenecks today? How do they get around those bottlenecks?

Topic 5 - How do platforms like the Alteryx Analytics Platform help teams improve their analytics velocity and productivity? And how much do you find that the right tools help improve how teams organize, or do they need to be well organized to best take advantage of the right tools? 

Topic 6 - Can you give us some examples of the types of results that companies often achieve when they better align their analytics teams to self-service and automated environments?

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