wrote to the Smithsonian Establishment asking for all the things that they had on human flight. He and his brother Orville devoured each paper, concept, and calculation from the main aeronautical researchers of their time. They studied Otto Lilienthal’s glider designs, Octave Chanute’s engineering rules, 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 have been at fault. They constructed their very own wind tunnel and examined over 200 wing designs themselves. What they discovered then surprised them. Lilienthal’s raise coefficients—the numbers all the discipline had relied on—have been improper, and never just a bit bit. Completely, utterly improper.
The Wright brothers didn’t query this out of vanity; they have been using first rules 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 identical to Langley’s tables, they’ll lead us astray when utilized blindly.
Probably the most strategic knowledge scientists I do know don’t select between frameworks and first rules. They use each. Frameworks give velocity. First rules present readability. Collectively, they separate process executors from strategic companions.
What Is First Ideas Considering?
First rules considering means breaking an issue all the way down to its basic truths and rebuilding from the bottom up. Aristotle described it as discovering “the primary foundation from which a factor is understood.”
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 current information: the Wright brothers studied all of the analysis out there; they simply didn’t deal with it as gospel.
For knowledge scientists, the identical applies. Frameworks are maps—confirmed shortcuts by acquainted terrain. First rules are the compass, retaining us oriented when the map will get a bit blurry.
Why Knowledge Scientists Want This Now
Frameworks are all over the place in knowledge science for good purpose. They assist us run experiments, outline metrics, and construct fashions shortly. However they’ll additionally create false confidence.
I’ve seen groups run flawless A/B exams that answered the improper query. I’ve seen normal metrics produce dashboards that seemed spectacular however measured nothing significant. These failures didn’t occur as a result of the frameworks have been flawed. They occurred as a result of nobody paused to ask the primary rules questions: What determination are we really 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 facet of information science. Gen AI can question knowledge, generate visualizations, and apply frameworks brilliantly. But it surely can not determine whether or not you’re asking the precise query.
First rules 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 Ideas Modifications All the things
Listed below are three examples the place the information science playbook is completely nicely written, however strictly following it mindlessly would produce a poor end result.
Past Textbook A/B Assessments
The framework says: outline your speculation, randomize customers, measure your major metric, test significance. That works completely—should you’re asking the precise query.
However the first rules query comes earlier: What determination are we making an attempt to tell? What uncertainty are we making an attempt to resolve?
I as soon as suggested a group who was testing a brand new advice algorithm. Framework considering mentioned: randomize customers, measure click-through fee, run for 2 weeks. Ship the winner.
However a primary rules pause revealed one thing completely different. We weren’t unsure about clicks—early alerts recommended they might go up. We have been unsure whether or not these clicks would drive true engagement or simply noise.
So we modified what we measured. As an alternative of clicks, we targeted on return visits, session depth, and long-term engagement. The consequence? The brand new algorithm elevated clicks by 12% however decreased return visits by 8%. The usual framework would have mentioned “ship it.” The primary rules strategy mentioned “not but.”
We determined that the brand new algorithm was too “click-baity”. Frameworks gave us the methodology. First rules gave us the precise 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’ll additionally create the phantasm that we’ve measured what issues.
First rules considering asks: What’s the basic habits or worth we care about? Does this metric really seize it?
Contemplate engagement. Many frameworks counsel DAU, session size, or actions per session. Cheap proxies—however are they proper?
- For a meditation app, longer classes might look “higher,” however the basic purpose is a sustainable apply. That may imply shorter classes over time.
- For an analytics software, extra queries per person might sign deeper utilization, or it might 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 have been simply logging in, wanting round, and leaving with out finishing a single process. From a primary rules lens, I made a decision that the actual 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 concentrate on to be able to drive significant adoption.
The framework offers you a menu of metrics. First rules considering tells you whether or not any of them really mirror your product’s worth. Typically the usual metric is ideal, however generally it’s dangerously deceptive.
When First Ideas Saved a Launch
One of many clearest examples I’ve skilled got here early in my profession when my group was tasked with constructing a “person high quality rating” to assist gross sales prioritize leads.
The framework strategy was apparent: supervised studying, predict conversion likelihood, rank by rating. We had the information, the options, the methodology.
Two weeks in, as we have been struggling to eke out incrementally larger efficiency scores, somebody requested: What determination will gross sales really 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 all the things. We didn’t want a posh mannequin spanning the complete likelihood 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 really used.
By going again to first rules, we clarified the actual downside 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 rules. They mix them.
- Frameworks are the map—they allow you to transfer quick and leverage accrued information.
- First rules 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 figuring out extra strategies or working more durable. It’s about figuring out when to comply with the map and when to test your compass.
With the steerage 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 Knowledge Scientist: Stage Up and Thrive within 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.
In case you’re questioning how your job will change as AI turns into extra succesful, or just seeking to have extra influence as a knowledge scientist and make progress in direction of a promotion, please take a look at the e-book on Amazon!
