A mistake thats been mainstreamed
From a linkedin post
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You’re about to make a $200k mistake on your first data hire. Your first hire shouldn’t be technical. It should be strategic.
The CEO says: “We need to be data-driven.”
The CTO agrees: “We should hire a Data Scientist.”
So they do.
PhD. Brilliant. Technical wizard.
They join, ask for data… and walk straight into chaos.
Three CRMs with conflicting definitions.
Revenue numbers that don’t match Finance.
Dashboards that contradict every meeting.
Six months later, they’re spending 100% of their time cleaning up the past instead of building the future.
By month nine, they quit.
You’re back at zero - with a bigger mess and a smaller budget.
Here’s the blunt truth:
You don’t have a data problem.
You have a sequencing problem.
You started by hiring technical people before defining strategic direction.
That’s like hiring construction workers before drawing the blueprint.
Here’s what you should’ve done instead:
1. Define the Business Problem Before the Role
Most leaders start with:
“We need dashboards.”
“We need a data warehouse.”
Wrong starting point.
The right question is:
“Where are we losing money, time, or credibility because of bad data?”
That’s the anchor.
Until you can quantify that cost, every technical hire is just busywork.
2. Hire the Strategist Before the Builders
Your first hire shouldn’t be a Data Engineer or someone Specialized.
It should be a Strategic Data Lead, or someone who can connect business goals to what the data team actually builds.
Their job isn’t to code.
It’s to define the map before you start driving.
In the first 90 days, they should:
Interview execs and department heads.
Quantify which data problems cost the most money.
Align on definitions.
Prioritzie clear next steps
Build a 90-day roadmap tied to ROI.
That roadmap will tell you who to hire next, and who not to.
3. Align Hires to ROI, Not Titles
If your biggest gap is broken pipelines → hire an Engineer.
If the problem is metric chaos → hire an Analyst or BI Developer.
If it’s governance → get an Architect.
But only after the roadmap exists.
Hiring a Data Scientist without strategyis like deploying AI on top of bad Excel sheets, expensive, slow, and guaranteed to fail.
4. Measure Data Team Success in Business Outcomes
Stop celebrating activity, “We built 10 dashboards.”
Change to "Reduced fuel cost per shipment by 8%"
5. Sit under where best
Hot take: It doesn't matter where your data team sits.
Seen it all work from IT, to COO to CEO.
Put it where it brings the biggest impac
Here’s the formula I’ve seen work repeatedly:
Strategy first → Tools second → Team third.
That order alone can save you 6–12 months and hundreds of thousands in cost.
If your first data hire is technical, you’ll get deliverables.
If your first data hire is strategic, you’ll get results.
Big difference.
