49 Kellogg Insight function, which we built. It’s a small function, but it primarily focuses on a few things, including analytics training and education at every level of the organization, and pulling together analytics practitioners from dif - ferent functions to build a community. INSIGHT: Tell us more about that final point about building a community around analytics. I would imagine at a company like Vanguard, there is data science everywhere. There must be data scientists working through - out the organization on a huge range of problems, some of which prob - ably have nothing to do with customer analytics. So can you talk about the broader data-science ecosystem a little bit? WANG: When you say “data science,” it’s not a term that people have uni - form understanding of. Anybody who can manipulate data, try to gen - erate insights from data, produce models, build AI capabilities—they could all be data scientists, right? So to answer your question, Vanguard has traditionally had very strong analytic capabili - ties in the investment area. But they don’t call themselves data scientists. They call themselves investment analysts or finan - cial analysts. So part of the ana - lytics-enablement work is to get everybody together and agree - ing on a consistent set of levels and job grades and to align those with skill sets and competencies Based on insights from Jing Wang