AI is transforming data teams - maybe you're anxious, excited, or kind of bummed. I'll share how I went from AI skeptic to enthusiast, how analytics engineering is evolving, and how data teams can finally deliver on promises we've been making for years.

We talked about:

Caitlin’s background The last mile in data The Pareto Principle Failing to use data Making sure data is used Communicating with decision-makers Working backwards from the last mile Understanding how data drives decisions Sketching and prototyping Showing the benefits of power data Measurability Driving change in data Asking high-leverage questions Resistance from users Understanding domain experts Linear projects vs circular projects Recommendations for data analyst students Finding Caitlin online

Links:

Emelie's talk https://locallyoptimistic.com/post/linear-and-circular-projects-part-1/ https://locallyoptimistic.com/post/linear-and-circular-projects-part-2/

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Your data team has to produce solid data. The pipelines have to run, the logic in your transformations has to be sound, and the report has to show accurate revenue. But if that’s all you’re doing, your team is probably bored and your organization definitely isn’t getting as much value as it could out of its data.

Open-ended creative work is a huge part of the appeal of working in this field – identifying opportunities to improve processes, appeal to new customers, or build better products adds value for the organization, but it is also incredibly satisfying. One of the fundamental challenges of managing a data team is balancing the need for rigor and reliability with the team’s desire to spend most of their time creating new knowledge. In this video, Caitlin Moorman, Head of Analytics with Trove Recommerce, discusses how we can manage those sometimes conflicting priorities, and create tools and processes that make the balance easier.