Data science for people who want decisions, not dashboards.
Most organizations have more dashboards than decisions. I started at AT&T Bell Labs before data science had a job title, and I've been working on the same problem ever since: the gap between what the analysis shows and what the organization actually does about it.
Michael Bagalman is a data science executive specializing in decision science, marketing analytics, machine learning, and AI strategy. He is VP of Business Intelligence and Data Science at Starz, Professor of Practice at the University of Oklahoma, and author of the Data Science Rabbit Hole publication, with applied work spanning marketing, product, content, finance, and consumer analytics.
What is Decision Science? Decision Science is the discipline of identifying which questions are worth answering with data and building systems that translate analysis into action.
When the factory turns the lights on, the science fair closes. A practical argument for why the next phase of AI in business is about operational discipline, not experimentation theater.
For Executives For PractitionersWhy tearing it down and rebuilding is rarely the answer. A framework for executives navigating legacy data stacks without halting the business.
For ExecutivesMoving past the "fairy dust" framing to understand what Netflix's content discovery overhaul actually means for how people find things to watch.
For PractitionersDecision Science is the discipline of identifying which questions are worth answering with data, and building the systems that translate analytical findings into better organizational decisions.
Data Science finds answers to questions. Decision Science finds questions worth answering. Most companies are very good at the first and structurally bad at the second, not because of technical failure, but because of the conceptual starting point.
These aren't motivational posters. They're convictions refined by three decades of watching smart organizations do preventable things.
If you cannot explain the model to the board, you do not understand the risk. Explainability isn't a concession to non-technical executives; it's a requirement for accountability.
Dashboards answer questions about last quarter. Strategy requires comparing what you could do differently. Most analytics teams are answering the wrong type of question.
The best data stack in the world cannot fix a company that's afraid of the truth. Change management is the hardest part of data science, and the least discussed.
More data doesn't eliminate uncertainty; it helps you manage it. Organizations that mistake data volume for decision clarity are confusing the map for the territory.
The problems that interest me most now are at larger scope: enterprise-level data strategy, AI governance, and the organizational design questions that determine whether advanced analytics creates durable competitive advantage. Most organizations that think they have a data science problem actually have a decision science problem, and no amount of model sophistication fixes it.
I'm looking for contexts where that distinction matters to the people at the top. If you're thinking at that scale, I'm reachable.