“Data doesn’t create value. Action does.”

If you’ve ever built a dashboard that no one used, answered questions no one asked, or fixed bugs no one cared about, you already know the pain.

And you’re not alone.

This week, instead of sharing just one practical tip or framework, I want to give you a primer on four of my favorite frameworks for building data teams and products that actually drive business impact.

I go deep into these in my ​masterclass From Dashboard Factory to Strategic Partner​ , but here's a quick overview to get you started, and to give you tools you can use immediately.

Let’s dive in.

Why This Newsletter Episode Matters

Too many data teams start with good intentions, but end up in the weeds:

• Building tools no one asked for
• Answering ad hoc requests endlessly
• Fixing the same dashboard 20 times for different people
• Wondering why nothing seems to stick

This happens because most teams still operate with the wrong mental model:

They start with data.

But that never works.

You don’t get buy-in from data. You get buy-in from impact. And impact starts with the business goal - not the data warehouse.

Framework #1: Start with Business Goals, Not Data

The Mistake:

Early in my data career, I believed the job was simple:

1. Start with data
2. Build tools
3. Deliver value

But that playbook never works. Here’s what usually happens:

• Dashboards no one uses
• Reports answering questions no one cares about
• Bug fixes for tools that don’t matter

The Realization:

Data alone does nothing. Only action creates value - and data teams don’t usually act on the data. Business teams do.

So, to create impact:

• You must help others take action
• That means getting their buy-in
• And to get buy-in, you need a compelling story
• Which only works if it’s tied to real problems
• And those problems must be linked to business goals

This framework is simple but powerful:

Start with the business goal → Understand the problem → Tell a compelling story → Get buy-in → Trigger Action

If you skip the first step, nothing else matters.

Framework #2: The Data Team Lean Canvas

This is a one-pager I created, inspired by the classic Lean Canvas used in startups.

The insight:

Building a data team is like building a startup. And most startups fail because they build something no one wants.

Sound familiar?

Many data teams make the same mistake: they fall in love with the solution and forget the problem.

So, I adapted the Lean Canvas to work for data teams.

Key elements of the Data Lean Canvas:

1. User Problem – Tied directly to business goals
2. User & Organizational Strategy – Who has the problem, Who solves it?
3. Unique Value Proposition – How do you solve it and why it matters
4. Solution – The data products, tools or processes
5. Distribution Strategy – How will you make users adopt your solution?
6. Systems Strategy – How do you create value systematically?
7. Outcomes – How do you measure impact?
8. Cost Structure – What does it cost, what's the ROI?
9. People Strategy – How do you find and grow talent?

This canvas keeps you honest.

It ensures you don’t build something just because you can, but because it solves a real problem.

Framework #3: The Key Result–Service–Product Framework

This framework connects the dots between high-level business goals and specific data products.

It’s split into two sides:

→ “What” to build 

→ “How” to build it

The “What” side:

1. Vision – Long-term aspiration
2. Mission – What you're doing to get there
3. Objectives – High-level business priorities
4. Key Results – Specific, measurable targets
5. Services – The things you need to achieve those targets

The “How” side:

This is where the data products come in:

• The architecture
• The models
• The tools
• The operational setup

Why this matters:

• Business owns the “what”
• Data owns the “how”
• This split removes ambiguity and aligns everyone

Framework #4: The Control–Agility Matrix

This one’s about navigating one of the most painful trade-offs in data:

How do you balance trust with speed?

The trade-off:

• Control → You get quality, trustworthiness, standardization
• Agility → You get speed, accessibility, experimentation

Most companies start in the Jungle:

• Everyone pulls data straight from source systems
• Lots of movement, zero consistency

Then someone decides: “Let’s build a perfect data stack and self-service!” They want to jump straight to the Promised Land!

But here’s the catch:

You can’t go from the Jungle to the Promised Land without going through the Fortress.

The right path:

1. Start by building control (Fortress)

• Define use cases
• Set up a data foundation
• Build trust

1. Then layer in agility (Promised Land)

• Own experimentation
• Enable self-serve
• Maintain consistency

Jumping straight to agility without control leads to data anarchy.

Summary: Use These 4 Frameworks to Build Better, Not More

Here’s a recap of the 4 frameworks that can save you years of trial-and-error:

1. Start with Business Goals, Not Data → Action is the outcome. Storytelling gets buy-in.
2. The Data Team Lean Canvas → Align data strategy to real business problems on one page.
3. Key Result–Service–Product Framework → Translate vision into measurable results and practical data products.
4. Control–Agility Matrix → Don’t chase agility without proper foundations.

Bottom Line

The biggest mistake data teams make is starting with data. But data is just a means to an end.

To make an impact, flip your mindset:

• Start with the problem, not the tool
• Start with the outcome, not the query
• Start with the business, not the warehouse

These frameworks are how I’ve seen teams go from being dashboard repair shops to strategic partners in growth.

Want to go deeper?

Check out ​my masterclass From Dashboard Factory to Strategic Partner​ where we walk through these frameworks in depth - with real-world examples and templates you can use right away.

Until then, pick one of these frameworks and try applying it this week.

Let me know which one resonates most with you.

Until next week!

Sebastian

P.S.: If you want to join a group of peers who work through my masterclass, build AI-ready data foundations and the data teams of the future, check out our ​10X Data Team Collective​ .