Why 98% of Data Teams Are Overworked (and Still Deliver Nothing)

A few weeks ago, I had a call with a CEO who spent over $2M on data last year.

Their internal team was buried in work—dashboards, pipelines, requests, tools, “AI readiness”… you name it.

But when I asked what ROI they saw?

“Honestly? Nothing I can point to.”

That hit hard. But it’s not rare.

Why most data teams are busy—but useless

Every day, their data team would wake up to a flood of requests:

• “Can you add this metric?”

• “Can you pull that from Salesforce?”

• “Fix the BI dashboard for QBR?”

It never ends.

I call it Slack PTSD. And it leads nowhere.

The team spends weeks wiring pipelines, fixing broken reports, and debating definitions—but none of it moves the business.

What’s missing?

Strategic thinking.

Not just code and cleaning. But knowing exactly why you're building something—and what it’s meant to do.

Code means nothing if it doesn't move a metric

You can write the cleanest dbt model in the world. Or build the best Airflow DAG.

But if you can’t walk into a meeting and say:

“This saved the company $X”

“This feature grew NRR by Y%”

…then none of it matters.

Our fix: the Value vs Complexity Matrix

Here’s how we guide every data team we work with:

Before you write a line of code, use a Value vs Complexity matrix.

Ask:

• What’s the business impact? (Revenue, cost, risk)

• How hard is it to deliver? (Data quality, tools, team effort)

Only focus on high-value, low-complexity initiatives.

These become your core data roadmap—the 1–3 projects that actually matter this quarter.

Everything else?

It waits. Or gets cut.

Align every experiment to your quarterly goals

If you're doing 20 things, but none tie back to the core objectives of the business, you're not a data team.

You’re a reporting-as-a-service agency.

Every month, every experiment should align to the core 1–3 initiatives from your prioritization matrix.

That’s how you build traction.

And that’s how you say “no” with confidence.

Think like a product team, not a service team

Data teams that win treat every initiative like a product.

3 phases:

1. Strategy — Who’s the user? What decision will this enable? Why now?

2. Build — Fast MVP. Just enough to prove value.

3. Iterate — Feedback, improvement, adoption.

This mindset shifts your team from reactive → revenue-driving.

Suddenly, the dashboards get used. The models impact decisions. And the business starts trusting the data again.

Real tip: Less meetings, more deep work

AI requires deep, creative, focused work. Not a million status calls.

Most meetings are noise.

The only ones that matter?

The ones where you get user feedback. Everything else → async.

TL;DR

• Most data teams overwork on low-impact requests

• The real fix is strategic focus and product thinking

• Use a Value vs Complexity matrix every quarter

• Prioritize 1–3 key initiatives, and align every project to them

• Build in small pieces, iterate with feedback, ship faster

• Cut meetings, increase impact

Want help applying this to your team?

We’ll audit your current roadmap, assess your team’s ROI, and build a prioritized data plan—free.
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