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From Data Sprinkling to Data-Driven Decision-Making · The Science of Leading

• 0:00
  Welcome back to Science of Leading, I'm Claire Monroe with Edwin Carrington.
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  Edwin, I want to open with a gap that stopped me cold.
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  Ninety-eight point six percent of executives say they aspire to a data-driven
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  culture.
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  Only thirty-two point four percent report actually getting there.
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  That gap is the entire episode, Claire.
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  Almost everyone wants it.
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  Almost nobody achieves it.
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  And the reason isn't ambition, it's that most companies confuse having a dashboard
• 0:25
  with being data driven.
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  There's a phrase in the piece for that exact confusion, "data sprinkling."
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  Which is precisely what it sounds like, bolting one dashboard onto an otherwise
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  intuition-based process and calling it transformation.
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  Real data-driven decision-making is consistent and repeatable across the business,
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  not a report someone pulls up once a quarter to feel better about a decision they
• 0:47
  already made.
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  So what does it actually look like in practice, day to day,
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  not in a strategy deck?
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  Concretely, it means hiring funnels get tracked all the way through,
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  applicant volume, interviews, offers, and first-year performance,
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  so you can see where quality actually drops off.
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  It means turnover gets segmented, voluntary versus involuntary,
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  by role, by manager, by tenure, so you know exactly where the company is bleeding
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  talent instead of guessing.
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  And it means behavioral profiles get compared against who actually succeeded in a
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  role, building real fit benchmarks over time instead of relying on a gut sense of
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  "we'll know it when we see it."
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  And crucially, none of that requires removing the human from the decision.
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  That's the misconception people default to.
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  Using data doesn't mean stripping out humanity, it means using evidence to reduce
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  bias, surface real patterns, and challenge assumptions about a candidate or an
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  employee that nobody's actually tested.
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  The data doesn't replace the manager.
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  It gives the manager something better to argue with.
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  Let's talk about why this took so long to reach mid-sized companies specifically,
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  because the piece traces this back decades.
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  Quantitative decision models go back to mid-century operations research,
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  then finance and manufacturing picked it up by the eighties.
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  People analytics stayed stuck in academic research and the largest enterprises for a
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  long time after that.
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  What actually changed recently is generative AI and better data integration tools,
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  which made this accessible to a hundred-person company without a data science team.
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  So the tools caught up to the ambition, even if the culture hasn't caught up to the
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  tools yet.
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  Exactly the mismatch, and it's why the culture question matters more than the
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  technology question.
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  Seventy percent of data modernization efforts fail specifically because of culture,
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  not because the software didn't work.
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  Which brings us to what I'd call the operating system underneath any of this,
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  the foundations.
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  Strategy, governance, data quality, literacy, culture, in that order,
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  I think.
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  Strategy first, because every metric you track needs to serve two or three clear
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  business outcomes, cutting ninety-day attrition by a defined amount,
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  improving first-year sales productivity.
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  Without that anchor, you end up measuring whatever's easy instead of what matters.
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  Governance next, because the moment you're handling behavioral profiles or anything
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  protected-class adjacent, consent, role-based access, and compliance aren't optional
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  add-ons, they're the floor.
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  And data quality is the one everyone underestimates, I think,
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  because it sounds boring compared to strategy or AI.
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  It's the least glamorous and most decisive piece.
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  Clean job titles, consistent exit dates, an actual agreed-upon definition of
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  "regrettable turnover."
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  Skip that, and every downstream analysis you run is suspect,
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  no matter how sophisticated the model on top of it looks.
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  And literacy and culture close the loop, because a great dashboard nobody can read
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  or trust doesn't help anyone.
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  Right, managers need to be able to read a basic turnover chart and interpret a
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  behavioral profile without a translator.
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  And leadership buy-in sets the tone for whether that becomes normal practice or a
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  slide that gets shown once and forgotten.
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  Democratize the access, or the insight stays trapped with whoever built the
• 4:00
  dashboard.
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  If you want a behavioral starting point for building that kind of benchmark on your
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  own team, OAD's free Top Performer Profile is about seven minutes,
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  results in twenty-four hours, no credit card.
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  That's O-A-D dot A-I.
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  Consistency is the whole test.
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  Not whether you have data, whether you use the same reliable data the same way,
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  every time a real decision comes up.
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  Perfect place to leave it.
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  Thanks, Edwin.
• 4:25
  Always, Claire.