The New Data Leader Playbook: How to Win the First 90 Days

Hiring your first Head of Data? Their first 90 days will decide if they become a strategic partner or just another backlog manager. Here’s the playbook to make it work.

Ali Z.

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CEO @ aztela

If your new Head of Data spends their first 90 days building dashboards, you didn’t hire a leader. You hired an expensive analyst.

I see it again and again in mid-market firms:

A shiny new “Head of Data” starts.
They inherit a backlog of reporting requests.
They spend months firefighting pipelines and chasing ad hoc asks.
By month six, the CFO is asking: “What did we actually get for this hire?”

By then, it’s too late.

The first 90 days are make-or-break. Done right, your Head of Data becomes a strategic partner that drives revenue, compliance, and trust. Done wrong, they become a very expensive analyst with a permanent ticket queue.

This playbook will show you how to set them — and your organization — up for success.

Step 1: Align on Business Outcomes (Not Tools)

The first question every new data leader must ask:

“What outcomes are we driving with data?”

If your CEO, CFO, and COO can’t answer that, your Head of Data is doomed to chase tasks instead of delivering outcomes.

Examples of clear alignment:

CFO → Improve financial reporting accuracy for board audits.
COO → Streamline supply chain with better forecasting.
CRO → Increase sales productivity with cleaner pipeline data.

Without this clarity, the new data leader becomes a dashboard builder instead of a strategist.

👉 Tie this to governance foundations: if you don’t align on business outcomes early, you’ll later struggle with consistent definitions of “margin” or “customer” across teams.

Step 2: Deliver One Quick Win in 90 Days

You don’t prove value by boiling the ocean. You prove it with one critical win.

The mistake most Heads of Data make is trying to solve everything at once. Six months in, nothing is finished. Trust erodes.

Instead:

Pick one high-stakes problem. Revenue reconciliation, compliance reporting, or sales pipeline accuracy.
Deliver in 60–90 days. The speed matters as much as the outcome.
Show ROI. Don’t present dashboards — present outcomes. Example: “This reporting fix reduced manual reconciliation time by 50 hours per month.”

That win buys trust and runway. It’s the difference between being seen as overhead and being seen as essential.

Step 3: Kill the Backlog

Your new Head of Data isn’t a help desk.

If they spend their first 90 days drowning in ad hoc requests, they’ll never escape. The backlog will own them forever.

How to fix it:

Say “no” or “not now.” 80% of dashboard requests deliver little value.
Push to self-service. Give marketing their own sandbox for exploratory dashboards.
Enforce prioritization. Every request should be ranked against business outcomes.

This is where governance comes in. Without prioritization, even the best hire will drown.

Step 4: Establish Core Definitions

Here’s why board decks fall apart: Sales says one ARR number, Finance says another. Marketing has a third definition of “customer.”

This isn’t a tech problem. It’s a governance problem.

Your new Head of Data must:

Define and document 3–5 core metrics (revenue, margin, pipeline, churn).
Enforce ownership (Finance owns finance data, Sales owns sales data).
Communicate these definitions clearly across the company.

Without this step, every dashboard will fail adoption — because no one trusts the numbers.

👉 Internal Link: See also our post on Why BI Dashboards Fail Adoption

Step 5: Set Org Model & Ownership

Where the data leader sits matters.

If they report to IT, they’ll be ticket-takers.
If they report to Finance, they’ll be compliance-driven but too narrow.
If they report to the COO, they can align across business operations.

The best model for mid-market? A hybrid structure:

Business leaders own data accuracy (Sales owns pipeline, Finance owns margin).
Data leader + stewards enforce definitions, lineage, and governance.

This prevents endless finger-pointing and makes accountability real.

👉 Internal Link: See our post on Where Should the Data Team Report?