Michel Brok 🎯

Marketing Automation & Data Expert | Helping retail & CPG to drive customer engagement at scale.

You’re scaling your ads,  but you don’t trust the numbers you’re seeing.

And here's the real problem: 

- If your tracking is wrong, your signals are wrong.
- If your signals are wrong, the algorithm learns the wrong buyers.
- And if the algorithm learns the wrong buyers…

scaling becomes impossible.

Most people think it's just a 'tracking' issue. It's not.
It's an 'algorithm' training issue. 

What I do:

I train your ad algorithms to find your best buyers by fixing the data they learn from and upgrading the signals they receive.

When signals are complete, consistent and accurate, your ads stop guessing and start learning.

What this looks like:

- Accurate conversion tracking across Meta, Google, TikTok & Pinterest
- Smarter buying signals (customer data, click IDs, first-party events)
- Server-side setups (Stape Top Partner)
- Better matching, higher Event Quality, cleaner attribution
- Eliminating signal loss caused by browsers, consent mode & plugins

What clients typically see:

- 20-50% more Meta sales
- 13–21% more Google Ads sales
- Lower CAC
- More stable scaling
- Decisions you can actually trust
- ROAS that finally makes sense

If this sounds familiar:

- Your dashboards don't add up
- You feel like you're scaling blind
- Meta says one thing, Shopify says another
- Results swing for reasons you can't explain

Send me a message.

Co-Founder

AldrichX is a data-driven marketing agency that helps 6+ figure fashion eCommerce brands to grow their business whilst they can keep focussing on building their brands.

Sebastian Hewing 🚀

Founder Data Action Mentor | Swiss Army Knife for Data & Analytics | Ex-Rocket Internet

Over the last 18 years I have built and led data teams across all continents - except Antarctica. ☃️

But my first data leadership job almost broke me:

It was 2010.

At 29 years old, I led the data team of the world’s largest venture builder (Rocket Internet).

Rocket Internet scaled companies such as:

→ Zalando (Europe's leading online fashion platform)
→ Delivery Hero (a leading food delivery platform)
→ HelloFresh (the world's leading meal kit delivery company)
→ Lazada (the Amazon.com of SouthEast Asia, exited to Alibaba)
→ Jumia (the first African company on the NYSE).

I saw those companies and data teams grow from tiny startups into massive, global market leaders.

And even though we built data teams for 7 companies in 15 countries, I struggled...

... hard.

→ We were drowning in ad-hoc requests instead of shaping strategy.
→ We were shipping dashboards instead of outcomes.
→ And ultimately stakeholders didn’t trust what we built.
→ And my influence, impact and personal growth hit a ceiling. 

Over the following years, I became a trusted advisor to Founders and CEOs by building winning data teams for 50+ companies.

The one thing I realized over all those years after leaving Rocket Internet: 

I was lacking an operating system for building winning data teams.

So, I created it.

The Data Team Adjusted Lean Canvas Framework transforms how data teams create value based on best practices from:

- data engineering
- product management
- marketing 
- behavioral science
- and entrepreneurship.

In my Data Strategy Masterclass, I have helped hundreds of growth-oriented data experts from 40+ countries become more influential by applying my framework and by building impactful data teams in the age of AI.

Favorite testimonial: “I got more value from your $350 course than a $3,000 University of Berkeley executive education course.”

→ Find out more at http://www.dataactionmentor.com/masterclass-business-impact  ← 

P.S.: My biggest passion besides Data, AI & Entrepreneurship is traveling. After 2 years of "digital nomading", I now have a home-base in Taipei, but I typically spend 5-6 months per year traveling. I have been to 100+ countries and territories.

Founder & Mentor

Solve all your Data- & Analytics Challenges and connect with experienced Data & AI Professionals for a price and time investment that every startup can afford

One boat has all the tools.

One lonely boat knows where it's going.

Guess which one delivers business value?

Every week I meet teams drowning in the “modern data stack”:

→ dbt models piling up
→ 15 Looker dashboards per stakeholder
→ Half-built AI POCs

And when things break, no one asks “what is the business trying to achieve?”

They ask: “should we migrate to another tool?”

Meanwhile, the high-performing teams?

→ They start with a problem.
→ They talk to real users.
→ They focus on impact over elegance.

Ship.
Learn.
Adjust.

No hype cycle. No data cult. Just progress.

Data strategy isn’t a shopping list.

It’s a decision-making framework.

And until that’s in place, no tool will save you.

♻️ Repost if you've ever inherited a stack with 12 tools and zero outcomes

👉 And join 3,000+ data experts who read my free newsletter for weekly tips on data strategy: https://lnkd.in/grEH8a3c

Sebastian Hewing

Pragmatic Data Strategist → From Dashboard Factory to Strategic Partner

I used to design extremely complex data infrastructures.

Now I do the exact opposite.

When building data infrastructure, simplicity is king! 👑

This is true for high-growth startups AND large companies!

Here’s the exact approach for every new data infrastructure I build:

First, I talk to all stakeholders intensively.
↳ understand their use cases
↳ understand their challenges, struggles and desires
↳ understand how they are tackling these challenges today

This is typically about 25% of the WHOLE infrastructure project!

Second, I create a KPI/Dimension Map.

This is a cross-tab that captures all stakeholder challenges and use cases in an easy-to-read format.

Third, I evaluate a status quo for each KPI/Dimension combination (use case)
↳ where does the data come from?
↳ what is the data quality?
↳ are there manual processes involved in capturing the data?

Lastly, together with stakeholders, we define a data infrastructure MVP (minimum viable product).

The goal is to FULLY cover the first use cases in less than 4 weeks.

A lot of data people get the definition of a “data MVP” wrong.

A solution solving 10 use cases 50% is not an MVP.
A solution solving 1 use case 80% is.

♻️ Repost if you’ve ever built a pipeline only to find no one uses it

👉 And join 3,000+ data leaders who read my free newsletter for weekly tips on building impactful data teams in the AI-era: https://lnkd.in/gvTUPa5x

I will explain my approach in more detail in my coming newsletter this Sunday.

“Let’s define a data strategy.”

Translation: let’s argue about tools for 6 months.

Here’s the thing nobody wants to admit:

Most data strategies are just about data stacks.
Not actual strategy.

If you want a strategy that survives the next reorg, AI hype wave, or exec sponsor change - start here:

1. Problem: What are we solving?
2. Users: Who are you serving?
3. UVP: What's our data team's secret sauce?
4. Solution: What are we building and what are we NOT building?
5. Distribution: How will we get this in front of users without chasing them down?
6. Systems: What helps this scale without breaking us?
7. Outcomes: What will users actually do differently?
8. Costs: What’s the real investment? (Spoiler: it’s not just the software)
9. People: How will we grow a team that wants to stick around?
10. Vision: What guides our decisions when priorities clash?

Answer those 10?

You’ve got a data strategy.

Ignore them?

You’ve got a dashboard backlog and a stack no one uses.

♻️ Repost if you've ever watched a “data strategy session” turn into a Power BI vs Tableau debate.

👉 And join 3,000+ data leaders who read my free newsletter for weekly tips on building impactful data teams in the AI-era: https://lnkd.in/gpwGnGA2

Pragmatic Data Strategist | Outcome-driven data strategy that earns your seat in leadership decisions

For years, I thought this was the playbook:

→ Get data → Build tools → Deliver value

Spoiler: it never worked that way.

- I built beautiful dashboards that nobody used.
- I answered questions no one had asked.
- I fixed bugs in things that didn’t matter.

At some point, I realized:

→ Data doesn't create value. Action does.
→ And action doesn’t happen without buy-in.
→ Buy-in doesn’t happen without a story.
→ A good story only works if it speaks to a user problem.
→ And the only user problems that matter? Are the ones tied to business goals.

So now, I work like this:
👉 Business Goal
👉 User Problem
👉 Data
👉 Story
👉 Buy-in
👉 Action
👉 Value

It’s not about moving from data to value.

It’s about moving from understanding to action.

With data as your ally, not your starting point.

This is how you shift from builder of dashboards to creator of business impact.

Want to transform your team from dashboard factory to strategic business partner?

👉 Join 3,000+ data leaders who read my free newsletter for weekly tips on building outcome-driven data strategies: https://lnkd.in/gzDsAYYR

♻️ Repost if you've ever shipped a dashboard that even you forgot existed.

You're drowning in ad-hoc requests instead of shaping strategy.

- “Can you pull this real quick?”
- “Can we get a dashboard by Friday?”
- “Can I export this to Excel?”

You’re busy and about to burn out.

I wrote 6 data strategy pieces to help data leaders break that cycle.

From busy-work to business impact:

1/ The End of the Data Analyst
→ What happens to the data analyst role in the age of AI?
🔗 https://lnkd.in/giCNfYXy

2/ Who should lead the data leader?
→ The reporting line that shapes your whole data strategy.
🔗 https://lnkd.in/gMHwnS8g

3/ Re-thinking the Single Source of Truth
→ When one truth isn’t enough for your company.
🔗 https://lnkd.in/gPTZBXHT

4/ The Hidden Structure of High-Performing Data Organizations
→ The quiet reason business and data teams clash.
🔗 https://lnkd.in/gkZX_5QU

5/ When the Intern beats your AI model
→ How overengineered data projects lose to scrappy, useful ones.
🔗 https://lnkd.in/ga-mYAex

6/ Pragmatic Data Governance that doesn’t kill the vibe
→ Stop building what people ask for. Start solving what they need.
🔗 https://lnkd.in/gxdPYcfJ

Inside these posts, you'll learn:

💡 Why the analyst role is evolving, not ending 
💡 Simple solutions that drive real business results 
💡 How to bridge the gap between business and data teams

Want to stop being a dashboard factory and start creating real value?

👉 Join 3,000+ data experts who read my free newsletter for weekly tips on building impactful data strategies in the AI-era: https://lnkd.in/gk9AXBAH

♻️ Repost if you’ve answered “just one quick thing” more than 3 times today

18 years in data. My 10 most powerful lessons.

(aka: how to avoid 5 years of pain, firefighting, and dashboard chaos)

No fluff. No hype.

Just hard-earned truths that changed how I build data teams:

1. Build data teams like a start-up
→ Don’t fall in love with your solution. Fall in love with the problem.

2. Embrace an 80/20 mindset
→ Ask: “What’s the smallest thing we can build to deliver 80% of the value?”

3. Don’t stop at dashboarding
→ Own experimentation. Feed data into ops tools. That’s where decisions happen.

4. Self-service is real
→ But it’s not “let everyone build dashboards.” It’s “help people answer the WHY on their own.”

5. Market Risk > Product Risk
→ You can build anything. The real risk? No one needs it.

6. Outcomes > Output
→ Dashboards don’t matter if they don’t change decisions. Measure business impact, not tickets closed.

7. Ad-hoc ≠ agility
→ Reactive teams burn out and lose trust.

8. Control before agility. Always.
→ Without trustworthy and understandable data, speed is just a faster way to crash.

9. Data teams are not service desks
→ Saying “yes” to every request keeps you busy. But it won’t move the business forward.

10. Business logic does NOT belong in dashboards
→ Keep logic in your data models. Or enjoy version wars, Excel exports, and endless confusion.

If I had followed these earlier, I would’ve saved my team months of pain.

Which one hits hardest for you?

♻️ Repost if you've ever shipped a dashboard that got zero views and one confused Slack message

👉 And join 3,000+ data leaders who read my free newsletter for actionable tips on data strategy: https://lnkd.in/gsjMX856

Your org chart is not a data strategy.

But it will make or break your data team’s impact.

Here’s what happens in most companies:

- The CTO says, “We need better data infra”
- The CFO says, “We need clean numbers”
- The CMO says, “We need attribution analytics”
- The CEO says, “We need a dashboard for the board”

And just like that... the data team reports to everyone and no one at the same time.

That’s how you end up with a “data strategy” that looks like:

- 791 dashboards
- 15 AI POCs
- 3 different metrics for ‘active user’
- 1 data team stuck doing requests with no real mandate

Let’s reframe the org chart conversation:

Instead of asking “Who should the data leader report to?”, ask:

→ Where is the pain greatest?
Data teams thrive when they solve high-impact problems. Attach them to the leader who needs them most.

→ Who understands what good data work looks like?
If the CxO treats the data team like a dashboard vending machine: wrong choice.

→ Who has room to lead?
Don't put your data org under someone who’s drowning in direct reports.

→ What business are you actually in?
Data should be aligned to the business engine - not to the loudest stakeholder.

Bad reporting lines don’t just hurt morale.

They kill momentum. Slowly. Silently.

And six months later, someone’s asking,
“Why is our data team not driving impact?”

Now you know why.

♻️ Repost if you've ever had to explain for the 5th time that data doesn’t belong under the CTO "because it’s tech stuff"

👉 And join 3,000+ data leaders who read my free newsletter for actionable tips on pragmatic, outcome-driven data strategies: https://lnkd.in/gxcJYSYQ

When the boss asks:

“What are your data team’s goals for next quarter?”

Many data leaders freeze.

Then default to:

→ ship 15 dashboards
→ reduce pipeline downtime
→ close more tickets

Sounds reasonable.

But it’s also how data teams quietly turn into dashboard factories.

The real problem:
Most data teams define success in outputs, not outcomes.

If you want to be seen as a strategic partner, your goals must ladder up to the business.

In practice, almost every meaningful data goal fits into one of 5 categories:

1️⃣ Increase revenue, growth, or market share
2️⃣ Decrease costs
3️⃣ Enhance reputation or brand
4️⃣ Strengthen relationships with critical stakeholders
5️⃣ Increase organizational knowledge & decision-making capability

Dashboards, pipelines, SLAs?

Those aren’t goals.
They’re internal activities.

If your quarterly goals don’t clearly map to one of the five categories above…
… then don’t be surprised when leadership struggles to see your impact.

Outputs keep you busy.

Outcomes keep you funded.

♻️ Repost if you’ve ever defined an OKR as “uh… we’ll close more tickets.”

👉 And join 3,000+ data leaders who read my free newsletter for actionable tips on data strategy: https://lnkd.in/g36_8cRq

“I was hired to migrate old reporting into a modern data stack.”

What a huge red flag! 🚩

Here’s the painful trap I didn’t see coming 11 years ago:

I used to think modern tooling = modern impact.

But all it does is ship the same old stuff… just faster and prettier.

It took me way too long to realize:

→ Stack migrations don’t build outcomes.
→ They build artifacts.
→ New stack, same old problems.

Here is how I shifted from shipping data stacks to shipping outcomes:

- We stopped accepting “build me this dashboard”
- We started unpacking decisions and friction
- We focused on solving real problems, not fulfilling tickets

That’s when people started trusting the numbers.
That’s when we got invited upstream.
That’s when we stopped being a dashboard factory.

So when someone says
“We’re modernizing our data stack,”

I always ask:
“Cool. Are you modernizing how you deliver value too?”

Because great tools don’t change anything.

✅ Want to build a data team that drives real outcomes?

👉 Join outcome-driven data leaders from 40+ countries in my data strategy masterclass: https://lnkd.in/gN7ZHYeH

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Michel Brok 🎯

Sebastian Hewing 🚀

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The Battle

Michel Brok 🎯

Sebastian Hewing 🚀

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Sebastian Hewing 🚀's LinkedIn Performance

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