How AI is Changing Data Analytics || Convo with a VP of Data

Описание: Chris Byington has spent 15 years in analytics across consulting, startups, and tech companies, and the last decade leading teams. Recently, we sat down to chat about how analytics has evolved over this time, what he looks for when hiring, and how AI has impacted the role.

Here are some of the highlights.

AI’s biggest gift to analysts is headspace

Of course, we had to talk about AI.

Yes, AI can write your SQL, draft your weekly update, and build a visualization in minutes instead of hours. But Chris frames the real value differently: it’s not about saving time, it’s about protecting focus. “It gives you longer blocks to think deeply,” which is where the best analytical work actually happens. The clerical stuff was always getting in the way of that.

AI tools for analytics are useless without a semantic layer

AI-powered BI tools are overhyped, at least for the time being.

Chris’s view: the SQL has never been the hard part. The hard part is knowing what the data actually means: which filters matter, which users to exclude, which date ranges to trust.

Until that implicit knowledge is made explicit in a governed semantic model, natural-language queries will keep giving you plausible-looking wrong answers.

Data teams are no longer seen as magicians

15 years ago, analytics was treated like a mysterious field full of magic. Today, having an analytics function is expected, and stakeholders know how to work together.

The upside: clearer partnerships and better questions. The tradeoff is higher expectations than ever. This is true not just for hiring, but for the job itself.

When AI speeds up analysis, the bottleneck is asking the right questions

If a question that used to take a week can now be answered in 90 minutes, the constraint shifts entirely. Chris argues that data teams have historically been great at point problems like A/B tests and feature analysis, but now need to move upstream. “How much data actually factors into your executive team’s annual planning decisions? It scares me how little, sometimes.” That’s where the leverage is.

Chris looks for two things when hiring - and neither is technical

After 15 years of building teams, Chris has distilled his hiring filter to:

Batteries included: Genuine drive to move the business forward without needing a lot of handholding.

Coachability: The ability to receive feedback and actually grow from it.

Technical skills are testable and trainable. Whether someone cares about impact, can take initiative and solve problems on their own (relative to their level and experience), and can take honest feedback? That’s much more important and much harder to train.



Listen to the full episode for more on data governance, breaking into analytics in 2026, and how Chris thinks about goal-setting at a tech company.