"The biggest obstacle to advancing data and analytics isn’t technology - it’s mindset." 

 — Malcolm Hawker, The Data Hero Playbook

Why This Newsletter Matters to You

You may have been told that the secret to success lies in tools, tech and processes. 

But what if there's one thing that's even more important than all of the above?

I just finished reading Malcolm Hawker's The Data Hero Playbook and found that it matches my my own cross-disciplinary experience - from marketing to engineering, product, and entrepreneurship - very well:

Data success isn’t blocked by tech. It’s blocked by limiting beliefs.

This newsletter is one part review of Malcolm's great book and one part my own break-down how an outdated mindset is suffocating innovation in data team. And more importantly: what you can do to change it.

The Problem: The Silent Epidemic Killing Data Initiatives

Let’s get real.

Data teams aren’t failing because they lack tools. They’re failing because they’ve adopted limiting mindsets - often without realizing it.

And I can say this, because I carried these limiting mindsets with me for about the first 6 years of my career.

Malcolm calls it the epidemic of the status quo. As data teams, we:

• Complain about business users not “owning” data
• Blame poor data quality on “illiterate” customers
• Obsess over perfect governance before shipping anything useful
• Treat internal users like obstacles instead of customers

Sound familiar?

Here’s the trap:

We often focus too much on control, process, and perfection - while ignoring business outcomes, agility, and stakeholder needs.

The Diagnosis: What’s Holding Data Teams Back?

Let’s break it down. The common symptoms of a limiting mindset in data teams include:

→ External locus of control: Instead of owning the outcome, we blame business users, legacy systems, or a lack of "data culture."

→ Victim mentality: Phrases like “garbage in, garbage out” or “the business doesn’t care about data” are the battle cries of data teams giving up accountability.

→ All-or-nothing thinking: Believing that either the data is perfect or useless. That you either have a data culture or can’t move forward.

→ Technology-first obsession: Thinking the latest stack and tech will solve root problems without changing behaviors or operating models.

→ Customer avoidance: Instead of asking "what problem are we solving?", teams default to pushing dashboards and data platforms no one uses.

As a result, many data leaders don’t quantify the value they deliver. They focus on governance, but not growth. On process, but not outcomes.

My Journey: From Marketer to Global Data Leader to Product-Centric Entrepreneur

I didn’t start in data. I started in marketing.

Through a series of unlikely twists, I ended up deeply embedded in the data world, first as a data engineer, then data analyst, then data scientist, then as a global data leader managing teams across continents, then as a Chief Product Officer at a VC firm and now as an entrepreneur. 

Today, with the perspective of all these roles, I'm asking myself questions that I didn't ask myself when I was a first-time data leader:

• Would anyone pay for the data products my teams built?
• Do our dashboards actually solve business problems?
• Are we empowering or frustrating our internal customers?
• Are we solving the problem in the most simple way possible?

My career didn't really progress slowly (I led a global data team at age 29) but I'm convinced that I would have progressed much faster (and with much less pain) had I asked myself these questions earlier.

That’s why Malcolm's The Data Hero Playbook hit home for me. Just like Malcolm, I believe the real transformation in data happens not with tools, but with mindset, product thinking, and stakeholder obsession.

The Solution: Developing Your Data Leadership Superpowers

The answer isn’t more tech or more governance.

It’s about developing a growth mindset and building your data team like a startup or a product organization.

Here’s how.

1. Adopt a Customer-First Mindset

Malcolm calls internal data stakeholders customers and not "users" or "stakeholders" and he recommends you to do so, too.

While I'm not so opinionated on the wording here, I agree with the thinking behind it:

Build data products with the same level of customer obsession and urgency as a startup founder.

Ask yourself:

• What problems are we solving?
• How will we measure success?
• How easy is it to use what we’ve built?

Tip: Get your team out from behind dashboards. Job shadow real users. Create customer feedback loops. Stop guessing.

2. Re-think Governance

Many governance programs are misunderstood and they are too heavily biased toward control. That’s why they fail.

Instead, reframe governance as customer enablement:

• Support users in achieving their goals
• Minimize friction instead of adding red tape
• Treat policies like products with value propositions
• Deliver the right data to the right user at the right time

Malcolm recommends changing the language:

• From “Data Governance” to “Customer Enablement”
• From “Data Literacy” to “Customer Training”

3. Introduce Product Management to Data Teams

This one’s big.

Product management brings everything data needs:

• Focus on customer problems
• Bias toward value delivery
• Clear accountability and measurement

Many data teams lack this discipline. 

I started my data initiative at Rocket Internet with a 192-page concept and we developed without user feedback for 4 months. If Netflix would have started with a 192-page concept, we'd still be renting DVD's from Blockbuster.

How to fix it? 

Hire a real product manager: someone trained in customer discovery, prioritization, and usability testing. Let them bridge business needs with technical capabilities.

And if you're a small team and can’t hire one, start thinking like one:

• Write problem statements before solution designs
• Validate ideas with real users before implementation
• Think in MVPs and iterations

4. Measure What Matters: Customer Value

If you can’t prove the impact of your work, don’t expect trust or budget.

Introduce value engineering into your data org:

• Quantify the cost of every dashboard, pipeline, and product
• Estimate and track the business value delivered
• Use those numbers to guide your roadmap and team incentives

Malcolm suggests the ultimate thought experiment:

Could your data team run like a product P&L?

If the answer is no, what would need to change?

5. Lead With Growth Behaviors

You can’t shift your team’s mindset unless you start with your own.

Here’s how to lead by example:

• Seek candid feedback: even when it stings
• Act with integrity: align words and actions
• Assume positive intent: don’t blame users
• Celebrate failure as learning: debrief, don’t punish
• Be the learner-in-chief: model personal growth

By the way, as a learner-in-chief you might also like our ​10X Data Team Collective​ . We are 80+ data leaders who build impactful, AI-first data teams together. A membership includes my masterclass "​From Dashboard Factory to Strategic Partner​ " where I share all my frameworks that helped me transform from an overwhelmed first-time data leader to a data leader building impactful teams.

This is the hard stuff. But it’s what separates heroic data leaders from caretakers of technical debt.

Bottom Line: The Future Belongs to Growth-Minded Data Leaders

The data industry doesn’t have a tooling problem.

It has a mindset problem.

And that’s good news, because mindsets can be changed.

If you want to build a high-performing, business-aligned, future-proof data team, you don’t (only) need more AI. You need:

→ Customer obsession 

→ Product thinking 

→ Accountability for value 

→ Growth mindset - at every level

This shift won’t happen overnight. But it starts with you.

Cheers,

Sebastian