Booster Analytics

When I walked into the company for the first time, I could smell the fear of due diligence.

You know, the kind — half-built dashboards, duplicated datasets, undocumented pipelines, and a team that had somehow made it all work just enough to avoid a full meltdown. But the founders had a bigger goal now: get acquired. And they knew their current data story wouldn’t survive five minutes of scrutiny from a prospective buyer’s tech diligence team.

That’s when I got the call. A fractional CTO with one job: make the company look good — technically, operationally, and culturally — so it can be sold without the whole thing crumbling under due diligence.

Here’s what I walked into and how I tackled it.

The Data Stack Was a Frankenstein

Every startup builds a data stack like it builds furniture from IKEA — missing instructions, surplus screws, and the wrong tool for every job.

There were three BI tools: one for execs, one for sales, and one that no one admitted to owning. Data pipelines were stitched together with cron jobs and Slack alerts. Cloud spend was opaque, with compute and storage costs ballooning due to abandoned POCs and duplicated models.

Fix #1: We consolidated tools, deprecated vanity dashboards, implemented lineage tracking, and set up a clean data architecture using dbt and Airflow, all version-controlled and auditable.

I also made sure cost visibility and optimization were built into the process — not a luxury, but table stakes for M&A.

The Data Culture Was a Game of Telephone

Data was political. Engineering owned the pipelines, marketing had its analyst (who didn’t trust the product metrics), and finance had built its own Excel universe with logic that contradicted everything else.

Everyone had data, but nobody had the truth.

Fix #2: I introduced a single source of truth model, built a metrics layer with consistent definitions, instituted data SLAs, and, most importantly, made data literacy non-negotiable.

Every team had to explain their metrics, not just consume them. Transparency replaced trust issues.

The Team’s Data Skills Were Stuck in the Past

Here’s the part no one wants to say out loud: the talent that got the company to Series B was no longer good enough to get it to an exit.

Analysts were SQL-only and still manually exporting CSVs into PowerPoint. Engineers could write ETL in Python but couldn’t explain data lineage or debug a dbt model. There was zero understanding of modern data ops, observability tools, or even basic governance principles.

This wasn’t about blaming the team — it was about being honest. The data world had evolved, but the company hadn’t kept up.

Fix #3: I brought in external experts for short, high-impact upskilling sprints — think dbt modeling workshops, Git 101 for analysts, and cost-aware Snowflake querying. I also created a capability matrix to identify who could scale with the business and who, frankly, needed to be redeployed.

I wasn’t trying to turn everyone into a data engineer, but I had to raise the bar on data maturity across the board.

Reorganizing the Team for M&A Readiness

The organizational chart looked like a failed hackathon: data engineers reporting to the product head, analysts split across five departments, and no ownership over data governance.

Fix #4: I centralized the data team temporarily under a “virtual data office,” a structure I designed specifically for fast-track cleanup. We focused on:

– Role clarity (who owns what across ingestion, modeling, and analysis),

– Documenting the stack and policies (for diligence teams),

– Creating handover-ready artifacts (in case of a full acquisition or acquisition),

And grooming key team members for visibility in the acquisition process. This wasn’t just about making things work. It was about making things look like they’ve always worked that way.

Prepping for the Exit: What Buyers Want to See

When you’re selling a company, you’re not selling code — you’re selling confidence. Buyers want to know:

– That data is auditable and reliable.

– That reporting is consistent and compliant.

– That the tech stack won’t implode the day after the deal closes.

So I made sure:

– All data models had ownership and documentation.

– Every core metric has a history and a reason.

– The data stack had a future-proof roadmap (even if that future was uncertain).

Food for Thoughts

Cleaning up a data mess is one thing. Cleaning it up for acquisition is another beast. You’re not optimizing for long-term perfection — you’re optimizing for clarity, credibility, and continuity.

As a fractional CTO, my job wasn’t just to rebuild the stack. It was to rebuild trust in the stack. Internally, with execs. Externally, with buyers.

And when the deal went through? The diligence team had only one question: “Can we keep this setup after the acquisition?”

That’s when I knew I did my job.