Original source: Profisee
This video from Profisee covered a lot of ground. 2 segments stood out as worth your time. Everything below links directly to the timestamp in the original video.
Most data scientists are optimizing for a standard of precision that their business colleagues neither need nor want — and the gap is now wide enough to trigger a corporate reorganization.
Data Science Teams Chase 95% Confidence While Executives Make Decisions at 20%
The structural tension between data science and business leadership is not a personality conflict — it is an architectural one. Data scientists, trained in academic traditions, treat a 95% confidence threshold as the floor of acceptable work. But when Stouse modeled actual executive decisions retrospectively at Honeywell, those decisions were running at roughly 20% confidence in driving desired outcomes. The implication is stark: doubling that figure to 40% does not merely improve decisions incrementally — it compounds across hundreds of repeated choices into returns that Stouse and collaborator Bill Schmarzo calculate at roughly 2,000% annualized value. The gap is not about rigor versus sloppiness; it is about two communities optimizing for completely different objectives.
When the pandemic hit and CEOs turned to their data science teams for guidance, many received the response that data management work was still ongoing. That moment — widely repeated across organizations — accelerated a structural consequence: data science teams are increasingly being consolidated under Finance, not because Finance can teach the discipline its craft, but to force the same business-alignment transformation that brought enterprise IT teams to heel twenty years ago. The direction of adaptation is non-negotiable. Data science will move toward the business, not the reverse.
"If you can get me in the 40s, I'm a hero. Business is all about the law of compounding — if you are one half of one percent better each day, the compound value annualized on that is bumping 2,000 percent."
Fortune 500 CEO Calls Data Science Department a 'Shell Game' as BI Market Contracts
The single most revealing detail in Stouse's analysis is not a model or a metric — it is a phrase. A Fortune 500 CEO, speaking candidly, described his data science department as operating a shell game: technically impressive motion that never quite reveals where the value is. Stouse offers a concrete corrective: sit with the C-suite and identify the twenty questions that, answered at even 40-50% confidence, would materially improve decisions made this week. That exercise, he argues, is the vodka in a distillery built around aging bourbon — the immediate cash crop that funds the long-horizon work. The parallel failure mode driving BI's decline is equally structural: dashboards are always about the past, and in periods of high volatility, the past provides diminishing guidance. Extrapolation from historical data is a known risk; what organizations need is dynamic, causal modeling.
The pattern Stouse identifies — data leaders responding to crisis moments with requests for more time to get infrastructure in order — repeated during the pandemic supply chain collapse and is now repeating again around AI adoption. The real question is whether the data science community will recognize that this cycle is not a resource problem or a technology problem, but a prioritization problem. The structural reality is that organizations will fund work that demonstrably moves decisions forward, and they will defund, reorganize, or simply route around functions that cannot show that connection within a credible timeframe.
"My data science department operates a shell game. Is that fair? Probably not. Is it understandable why they would feel that way? Probably yes."
Summarised from Profisee · 50:19. All credit belongs to the original creators. Streamed.News summarises publicly available video content.
