Sebastian Hewing

I started data projects with 3 silly questions.

Today, I skip all of them:

❌ “Which BI tool should we use?”
❌ “Should we build a real-time pipeline?”
❌ “What should our data architecture look like?”

The result:

I built a dashboard factory and lost trust with senior management.

That’s why I now use the same 5-step process to kickstart every data project.

Simple.
Repeatable.
Business-first.

1️⃣ Understand the game

• Where is the company today?
• Where do we want to be in 12 months?
• Which initiatives already exist?

6 things I would NEVER allow in my data team.

And what I expect instead:

1. Technology-first obsession
   ❌ “Let’s pick the stack first”
   ✅ Start with business problems, not tools

2. User avoidance
   ❌ “We already know what the business needs”
   ✅ Build with users, not for them

3. All-or-nothing thinking
   ❌ “If the data isn’t perfect, we can’t use it”
   ✅ 80/20 thinking

4. Governance as a barrier
   ❌ Power without accountability
   ✅ Clear ownership with real accountability

5. Victim mentality
   ❌ “We can’t create value without data culture”
   ✅ Create value first → culture follows…

“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.

Most analysts won’t lose their job to AI.

But many “SQL + dashboard” analysts will.

Right now the data world is split into two camps:

→ “AI will replace analysts.”
→ “Analysts are more important than ever.”

Both sides are partly right.

The real story:

The analyst role isn’t dying.
It’s evolving.

Knowing SQL and building dashboards was never the real value.

And with AI, it’s definitely not enough anymore.

The future analyst needs to:

• understand business context
• ask better questions
• structure messy problems
• drive decisions, not just reports…

Most data teams never make it to the promised land.

They’re either:

→ stuck in a bottleneck where everything goes through the data team
→ lost in a jungle of tools, chaos, and one-off solutions
→ trapped in a fortress where nothing escapes the compliance checklist

But here’s the twist:

You can set outcome-driven goals in any of these places.

In fact, you have to - otherwise, you’ll never move.

1️⃣ In the bottleneck?

Focus on reliability and trust.

• 90% of critical reports delivered by 9am
• 0% of bugs discovered by stakeholders

2️⃣ In the jungle?

“Help! I’m drowning in ad-hoc requests!”

Stakeholders ask for dashboards. You deliver.
Then they ask for another dashboard.
Then a slightly different cut.
Then export it to Excel.

You’re not building strategy. You’re running a reporting helpdesk.

Here’s a 9-question canvas that flips the script:

1. Problem
   What’s the real business pain?
   If you can’t name it in plain English, don’t build anything yet.

2. Users
   Who are you solving it for?
   No, “the business” is not a user.

3. UVP
   Why should users come to your team instead of just guessing, using gut feel, or pulling GA numbers?

“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.

“Let’s define a data strategy.”

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

Here’s the thing I don't like to admit openly:

I've been a data leader for almost 20 years.

In my first 6 years, I believed that data strategy was mostly about data stacks.

Over those years, I realized (the hard way):

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.

The real reason your data team is underperforming?

You're using them like a dashboard concierge service.

And treating strategy like a synonym for “data stack.”

Here’s how most companies use their data team:

→ To build dashboards no one uses
→ To build dbt models that collect dust
→ To chase tool-of-the-month hype
→ To enforce rules no one understands

And leaders wonder why the data team feels burned out and the business still isn’t “data-driven.”

Data teams are often treated like:

• Internal service desks taking tickets
• Janitors cleaning up tech debt
• Compliance cops for naming…

Data Leader reports to the CEO → company cares a lot about data

Data Leader reports to the COO → company cares about data

Data Leader reports to the CIO → company does not care much about data

Data Leader doesn't report to any CxO → company doesn't give a 💩 about data

Someone once said this to me and it made me laugh.

In 5 companies, I saw 5 reporting lines:

• CTO
• CFO
• CEO
• CMO
• CPO

No one knows where the Data Leader belongs.

The truth is: there is no perfect answer.

But there IS a better way to make the decision.

For years, I was confused by data org models.

Centralized, hub & spoke, mesh...

Everyone had strong opinions.
But I didn't understand what actually works.

Until it finally clicked for me:

You don’t need a PhD in operating models.

👉 You need four things:

1. Define the right roles.
   Ownership and responsibilities must be clear

2. Place them intentionally.
   Some roles belong in the business, some on the data team.

3. Get the ratios right.
   A lone analyst supporting 12 teams? Burnout incoming.

4. Start centralized.
   It's way easier to decentralize once you have trust, tooling, and maturity.

It took me 6 years to realize:

Data Quality was never the thing holding back my promotion.

All these years grinding to create "perfect data".

Until I finally came to the obvious conclusion:

Executives don't care about data quality.
They only care about revenue.

That gap is where most data work dies.

I once worked with a company where:

→ Customer Acquisition Costs looked too high
→ Marketing seemed underperforming
→ Growth was slowing down

So everyone tried to “optimize marketing campaigns.”

But the real issue?

We were measuring reality wrong.

I’ve audited 100+ data teams.

Most of them had the same problems:

→ Backlogs full of “quick questions”
→ Dashboards nobody really trusts
→ Stakeholders exporting to Excel anyway

Many teams think that these are the reasons:

• “Wrong tools.”
• “Data-illiterate stakeholders.”
• “Not enough people.”
• “Lack of data culture.”

They usually aren't.

The main root cause for their problems?

They either over-optimize for control:

• Perfect models
• Heavy governance
• Centralized everything

Or they over-optimize for agility:…