“There is no data strategy. There’s only business strategy, and data is part of it.”

That quote came up in one of the many heated discussion I’ve had with data leaders over the last weeks. And it reveals just how divided the industry is when it comes to defining what a data strategy actually is, or whether it even exists.

Some say it’s just tool migration. Others claim it’s nothing but an appendix to the business strategy. And still, others get visibly frustrated the moment the term comes up.

So why does data strategy stir up so much controversy?

Simple: most people approach it backwards.

Why You Should Care About Data Strategy — Done Right

The way you define and execute your data strategy determines:

• Whether the tools you build will ever be used
• Whether your team creates actual business value or just busywork
• Whether data has a seat at the leadership table or remains a dashboard factory

And right now, especially in the AI age, many teams are once again repeating the same mistake: thinking in tools before use cases, in outputs before outcomes.

That’s why I want to re-introduce a practical framework with you.

A framework that some of you already know and that has shaped the way I build data teams and data infrastructure.

A framework I developed to cut through the noise and align data strategy with what actually matters: users, value, and business goals.

The Problem: Strategy by Tooling, Not by Thinking

Too many data strategies start with the question:

“What stack should we use?”

But the real question should be:

“Whose problem are we solving and how will our work drive change?”

When data leaders focus only on tooling, they’re addressing product risk: the risk that we build something that doesn’t work.

But the bigger, more dangerous risk is market risk: the risk that no one wants or uses what we’ve built.

Unfortunately, market risk is rarely considered in data strategy conversations. And this is why so many dashboards go unused, pipelines get over-engineered, and teams stay stuck chasing stakeholder requests with unclear priorities.

The Solution: The Data Team-Adjusted Lean Canvas

To help data teams shift their mindset, I adapted the well-known Lean Canvas to our data world. It breaks data strategy into 9 practical building blocks, across two types of risk:

1. Market Risk: are we solving the right problem for the right user?
2. Product Risk: can we build a solution that works and delivers value?

Let’s walk through each component, in the order they should be tackled:

1. Users

Start by identifying who you’re serving:

• Who are the stakeholders with data touchpoints?
• What are they trying to achieve?
• What’s slowing them down today?

Understanding users and their problems is always the first step in any project. Without this, everything else risks irrelevance.

2. User Problems

Now that you know your users, understand their struggles:

• What decisions are they trying to make?
• What data challenges block them?
• What workarounds do they rely on?

This is the heartbeat of your strategy. No meaningful work can begin without it.

3. Unique Value Proposition

Now ask: Why should stakeholders trust your team to solve these problems?

This step is often overlooked, but critical. Think of it like a mini internal pitch:
→ How does your team solve stakeholder problems better than gut instinct or spreadsheets?
→ What do you enable that they can’t get today?

4. The Solution

This is the part we all love, but it’s only step four out of nine!

Define what you're building to solve stakeholder problems:

• Modern Data Stack on Databricks?
• dbt pipelines?
• Agentic Analysts?
• A Churn Prevention ML Model?
• Fancy Dashboards?

But don’t jump here too early. Tools and solutions come after understanding the problem and defining your edge.

5. Distribution Strategy

Here’s the piece that’s missing in most data projects:

How will we make people use what we’ve built?

To create value, you need action. To take action, you need buy-in. To get buy-in, you need a story.”

That story, and how you get it in front of the right people, is your distribution strategy.

This part of the framework was inspired by a great post by Christian Sassano and then extended. I recommend to check out his post .

6. Management & Systems Strategy

Here we define the systems and routines that ensure consistency:
→ How do we handle stakeholder requests?
→ What testing and QA processes do we have?
→ Are there SOPs or playbooks for recurring tasks?

This is where AI and automation can shine: bug alerting, documentation, request triage. But it doesn’t always require AI to be effective.

7. Outcomes

Forget output metrics like:

• Number of dashboards built
• Number of requests closed

Focus on business outcomes:
→ Did we help marketing to decrease churn?
→ Can we sell a data product to external customers on a subscription?
→ Is our data infrastructure increasing trust with important investors and helps raise the next funding round?

That’s where your value lives.

8. Cost Structure

Surprisingly, many data teams overestimate this.

Yes, cost matters. But often the value created by one smart project can cover multiple salaries.

As Sascha Urban (VP Data at Blinkist) shared: a single initiative saved the marketing team enough money to pay for two analysts for a whole year .

Don’t be scared of cost: just be clear on value.

9. People Strategy

Lastly, consider your team:

• How do you attract the right talent?
• How do you keep and grow them?

A strong data strategy includes a plan for hiring, career paths, and development and not just tech and tooling.

Bottom Line

Too many data strategies are just tooling plans in disguise.

The real strategy starts with people:
→ Understand your users
→ Solve real problems
→ Communicate the value
→ Build with purpose, not just with tech

The Data Team-Adjusted Lean Canvas helps you structure your thinking in a way that aligns with business value and finally puts an end to strategy debates that go nowhere.

If you want to go deeper into each of these 9 elements, I’ve built a full course around this framework .

And here's the heads-up: I’ll be increasing the price next week. I originally built this masterclass for aspiring data leaders but realized that it's also being used and praised by very senior leaders across 40+ countries. So, it's time to adjust the price up.

If you’re serious about building a data strategy that drives business, not just dashboards, check it out now while it’s still at the current price.

Let’s stop building tools that go unused and start building data strategies that matter.