wrote to the Smithsonian Establishment asking for every part that they had on human flight. He and his brother Orville devoured each paper, principle, and calculation from the main aeronautical researchers of their time. They studied Otto Lilienthal’s glider designs, Octave Chanute’s engineering ideas, and Samuel Langley’s tables on raise and drag.
Then they did one thing radical: they questioned all of it.
When their gliders didn’t carry out because the revealed knowledge predicted, the Wright brothers didn’t assume they had been at fault. They constructed their very own wind tunnel and examined over 200 wing designs themselves. What they discovered then shocked them. Lilienthal’s raise coefficients—the numbers the complete subject had relied on—had been flawed, and never just a bit bit. Completely, utterly flawed.
The Wright brothers didn’t query this out of conceitedness; they had been using first ideas considering. And it’s what made the distinction between their first flight in 1903 and Langley—who had extra funding, status, and sources—crashing his plane into the Potomac simply 9 days earlier.
Right this moment’s knowledge scientists face an identical problem. We’re surrounded by playbooks, frameworks, and greatest practices—A/B testing guides, metrics taxonomies, mannequin choice checklists. These instruments are invaluable. However similar to Langley’s tables, they will lead us astray when utilized blindly.
Probably the most strategic knowledge scientists I do know don’t select between frameworks and first ideas. They use each. Frameworks give pace. First ideas present readability. Collectively, they separate activity executors from strategic companions.
What Is First Rules Pondering?
First ideas considering means breaking an issue right down to its elementary truths and rebuilding from the bottom up. Aristotle described it as discovering “the primary foundation from which a factor is thought.”
In apply, it means asking:
- What do we all know to be completely true?
- What are we assuming?
- Which of these assumptions can we problem?
This doesn’t imply rejecting present information: the Wright brothers studied all of the analysis accessible; they simply didn’t deal with it as gospel.
For knowledge scientists, the identical applies. Frameworks are maps—confirmed shortcuts via acquainted terrain. First ideas are the compass, preserving us oriented when the map will get a bit blurry.
Why Knowledge Scientists Want This Now
Frameworks are in all places in knowledge science for good purpose. They assist us run experiments, outline metrics, and construct fashions rapidly. However they will additionally create false confidence.
I’ve seen groups run flawless A/B checks that answered the flawed query. I’ve seen customary metrics produce dashboards that appeared spectacular however measured nothing significant. These failures didn’t occur as a result of the frameworks had been flawed. They occurred as a result of nobody paused to ask the primary ideas questions: What resolution are we truly making an attempt to tell? What worth are we actually making an attempt to measure? Will we even want this degree of complexity?
This issues greater than ever as a result of AI is automating the execution aspect of knowledge science. Gen AI can question knowledge, generate visualizations, and apply frameworks brilliantly. Nevertheless it can’t resolve whether or not you’re asking the correct query.
First ideas considering is your differentiator. It’s the ability that retains frameworks grounded in actuality—and it’s changing into probably the most defensible functionality of a strategic knowledge scientist.
The place First Rules Adjustments The whole lot
Listed below are three examples the place the information science playbook is completely properly written, however strictly following it mindlessly would produce a poor final result.
Past Textbook A/B Exams
The framework says: outline your speculation, randomize customers, measure your major metric, test significance. That works completely—if you happen to’re asking the correct query.
However the first ideas query comes earlier: What resolution are we making an attempt to tell? What uncertainty are we making an attempt to resolve?
I as soon as suggested a staff who was testing a brand new suggestion algorithm. Framework considering stated: randomize customers, measure click-through charge, run for 2 weeks. Ship the winner.
However a primary ideas pause revealed one thing totally different. We weren’t unsure about clicks—early indicators recommended they’d go up. We had been unsure whether or not these clicks would drive true engagement or simply noise.
So we modified what we measured. As a substitute of clicks, we targeted on return visits, session depth, and long-term engagement. The end result? The brand new algorithm elevated clicks by 12% however decreased return visits by 8%. The usual framework would have stated “ship it.” The primary ideas method stated “not but.”
We determined that the brand new algorithm was too “click-baity”. Frameworks gave us the methodology. First ideas gave us the correct query.
What Are Our Metrics Actually Measuring?
Metrics frameworks—North Star, OKRs, HEART—are highly effective as a result of they offer construction. However they will additionally create the phantasm that we’ve measured what issues.
First ideas considering asks: What’s the elemental habits or worth we care about? Does this metric truly seize it?
Take into account engagement. Many frameworks recommend DAU, session size, or actions per session. Cheap proxies—however are they proper?
- For a meditation app, longer classes might look “higher,” however the elementary objective is a sustainable apply. Which may imply shorter classes over time.
- For an analytics software, extra queries per person may sign deeper utilization, or it may imply customers are struggling to seek out solutions. The true worth is quicker, extra focused insights.
I as soon as began a brand new job and inherited a dashboard that proudly reported weekly lively customers as the first success metric. However once I dug in, I spotted most “lively” customers had been simply logging in, trying round, and leaving with out finishing a single activity. From a primary ideas lens, I made a decision that the true worth was duties accomplished. And as soon as I shifted the metric, we found (as anticipated) that utilization was far decrease underneath the brand new definition, however the brand new framing supplied us readability on what to give attention to with a purpose to drive significant adoption.
The framework provides you a menu of metrics. First ideas considering tells you whether or not any of them truly mirror your product’s worth. Typically the usual metric is ideal, however typically it’s dangerously deceptive.
When First Rules Saved a Launch
One of many clearest examples I’ve skilled got here early in my profession when my staff was tasked with constructing a “person high quality rating” to assist gross sales prioritize leads.
The framework method was apparent: supervised studying, predict conversion chance, rank by rating. We had the information, the options, the methodology.
Two weeks in, as we had been struggling to eke out incrementally increased efficiency scores, somebody requested: What resolution will gross sales truly make with this rating?
We requested gross sales. The reply wasn’t “give me precise chances.” It was: Ought to I take the time and personalization to name this lead, or simply ship a fast standardized e-mail?
That shifted every part. We didn’t want a posh mannequin spanning the total chance vary. We wanted a easy, interpretable classifier optimized round one threshold.
By reframing, we moved from an ensemble mannequin to logistic regression, dropped half our options, shipped three weeks sooner—and delivered one thing gross sales truly used.
By going again to first ideas, we clarified the true drawback after which went again to the usual frameworks to construct an answer.
The Compass and the Map
Right here’s the lesson: strategic knowledge scientists don’t select between frameworks and first ideas. They mix them.
- Frameworks are the map—they allow you to transfer quick and leverage gathered information.
- First ideas are the compass—they preserve you aligned when the map isn’t clearly exhibiting your path.
The Wright brothers didn’t reject the analysis of their time. They constructed on it, however in addition they knew when to return to fundamentals.
That’s the mindset shift that separates strategic knowledge scientists from tactical ones. It’s not about realizing extra strategies or working tougher. It’s about realizing when to comply with the map and when to test your compass.
With the steering of AI, frameworks will preserve getting simpler to use. However the compass—that’s yours to construct. And it’s what is going to preserve you related, strategic, and indispensable within the years forward.
This is likely one of the core themes I discover in my new e-book, The Strategic Data Scientist: Level Up and Thrive in the Age of AI (Amazon affiliate hyperlink). It’s about pairing foundational considering with confirmed frameworks to create influence, affect roadmaps, and place your self as a strategic associate—not only a technical executor.
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