Money, Python, and the Holy Grail: Designing Operational Data Models

Most analysts don’t become analysts to build dashboards. We don’t become analysts to do data pulls, or clean up messy data, or put together pitch decks. We become analysts to do impactful, strategic analysis. This is our calling; it’s the most valuable work that we do; and it’s why we put up with the rest of our job—for that afternoon with nothing but a big question, a clear calendar, and a trajectory-changing aha moment buried somewhere in our well-prepped datasets.

But the rapid rise of analytics engineering should make us question all of this. Is strategic analysis actually the holy grail of analytics? Is it the most valuable thing we could do? Is it even what we want to do?

In chasing this ambition, Benn Stancil (Mode) thinks we’ve lost sight of something even more important—and potentially, more interesting: Designing operational models. These frameworks, which are a natural extension of the semantic models built by analytics engineers, are often more valuable than any dashboard, any dataset, or any deep dive analysis.

In his talk, Benn will share what these models are, why they’re valuable, and why, in our eternal quest to both quantify our value and to find work we love, they could prove to be our holy grail we’ve always been looking for.