“No more dashboards until we know what problem we’re solving.”

This week’s newsletter will be a bit different.

Instead of polished frameworks or spicy rants about dashboard factories, I’m giving you a peek under the hood.

I’ve decided that I want to share how I actually run data projects from scratch - in real time, with real clients. Building in public, so to speak.

Cool? Let’s get into it.

Why This Project (and Why Now)

Some of you know that I own a consulting business that helps VC-backed consumer scale-ups (and occasionally SMEs and PE-backed ventures) build their first real data infrastructure.

It’s a productized service: discovery, implementation, and fractional leadership until a full-time hire is in place.

But now I’m slowly shifting.

Less consulting, more products. I’m building for you - the outcome-driven data leader who wants to move from reactive dashboard pusher to proactive, strategic partner at the exec table.

I still take on select consulting projects mostly to:

• Stay sharp
• Supercharge my frameworks with AI
• And share my learnings with you - of course!

This newsletter is about my new project I just started in December.

The Setup: A Two-Sided Marketplace

The client is a two-sided marketplace in Germany, Switzerland and Austria: buyers on one side, sellers on the other. Classic case where growth depends on balancing supply and demand across user types.

Greenfield setup. They have a few Looker data studio reports sitting directly on the data sources but that's it. We’re building from scratch.

So how do we start?

With the only thing that really matters:

What business problem are we solving with data?

Step 1: Receive Company Goals + Planned Initiatives

This client came prepared. Business plan, company goals, major initiatives - all well documented.

If they hadn’t been, I’d collect that intel during stakeholder interviews. But in this case, we had a head start.

I went deep:

• Where are they today?
• Where do they want to be in 12 months?
• What levers are they already pulling to get there?
• What data do they REALLY need to pull those levers?

The point here is simple: understand the game before building tools.

You’d be shocked how many teams skip this step and start building pipelines no one needs.

Step 2: Identify Stakeholders

Once I had a grasp on the company’s strategic plan, the next step was mapping the data demand landscape:

• Who will be making decisions?
• What data will they need?
• What tools are they used to - or allergic to?

At this stage, I’m not just identifying “data consumers.” I’m getting a feel for their data literacy, biases, and expectations.

This isn’t just helpful - it’s critical.

If I know someone had a traumatic experience with Power BI in their last role, I’m not going to force-feed them another nightmare.

Step 3: Run Stakeholder Interviews

This is where the real work happens.

My goal: understand how each leader currently makes decisions and where they can’t.

Most importantly, I ask:

“What’s a decision you regularly make… where you wish you had better data support?”

In these convos, I’m looking for:

• Actual decisions being made
• Metrics that aren’t accessible
• Questions that lead to dead ends

I also filter ruthlessly. If a stakeholder can already self-serve in HubSpot or GA, I won’t waste time building a pipeline for it.

I only solve problems where data isn’t currently accessible and the business impact is real.

Running these interviews and asking the right questions is crucial. The biggest mistake I see people making is asking leading questions.

Those questions are often worse than not asking at all and almost force your interview partner to inadvertently lie to you .

I've written more about which types of questions to avoid here .

Step 4: Build the KPI-Dimension Map

Once interviews are done, I translate everything into a KPI-Dimension Map .

This is my go-to deliverable. It’s simple but surgical. It maps:

• Which KPIs matter
• For what decision(s)
• Using which dimensions

And it highlights:

• Gaps in data access
• Opportunities for value creation
• Dependencies and tracking needs

This map becomes the contract. The “spec” for what we’ll build and why.

It also makes it blindingly clear what we won’t build.

This is the actual KPI Dimension Map from the project. It's in German and some parts are redacted to protect the client's privacy.

If I were to summarize the use case in one sentence I would call it: Build a single-source of truth for Growth Marketing initiatives. The goal is to optimize Return on Marketing Investment.

Step 5: Run a Sign-Off Workshop

Last step in the discovery phase: get the map in front of stakeholders and lock it in.

By this point, alignment is easy. No endless debates. Why?

Because we’ve already validated:

• The use case is tied to business goals
• The data is inaccessible without new infra
• The stakeholders are ready to act on it

This isn’t some abstract data strategy. It’s a delivery spec.

Once it’s signed off, we move into implementation.

(And yes, I’ll walk you through that part too - if you like this format.)

Why This Approach Matters

Too many data teams still build for theory, not for impact.

They ship pipelines without demand.
They collect metrics no one uses.
They deploy tools their users hate.

This five-step approach flips that. It’s boringly effective. And it saves months of wasted effort.

This whole process took me less than 4 days.

It ensures:

• Every pipeline is tied to a repeatable decision
• Every stakeholder is onboard before a single line of code is written
• Every deliverable has a clear definition and owner

That’s how you go from dashboard factory to strategic partner.

Action Steps (if you're building your own)

If you’re doing something similar, here’s a simplified checklist:

• ✅ Ask for company goals and initiatives up front
• ✅ Interview stakeholders (correctly), focusing on real decision workflows
• ✅ Filter out anything that doesn’t require a pipeline
• ✅ Create your KPI-Dimension Map
• ✅ Get explicit sign-off before building

Want templates for all this? I walk through the exact process (and share my templates) in my Data Strategy Masterclass "From Dashboard Factory to Strategic Partner" . Check it out if you're ready to stop building dashboards no one uses.

The class also includes a community of outcome-driven data leaders who support each other implement the learnings.

See you next week (or see you inside the masterclass)

Cheers,

Sebastian