The Hidden Structure of High-Performing Data Organizations

"I've seen people from the business and data teams crying in meetings because they couldn't agree on who should do what."

This isn't a dramatic scene from an office reality show – it's the harsh reality I've witnessed across multiple organizations.

Let's tackle one of the most critical aspects of building data-driven organizations: how to structure your data analytics responsibilities between the data team and business domains.

Get this wrong, and you'll watch your data initiatives crumble, regardless of how sophisticated your tech stack is.

Why This Matters Now

The success of your data strategy hinges not on tools or technologies, but on how well your business and technical teams collaborate. Without clear role definitions and responsibilities, even the most promising data projects are doomed to fail.

The good news? There's a structured approach to solving this challenge.

Bridging the Technical-Business Divide

The key to successful data organizations lies in minimizing the gap between technical and business teams. This requires carefully distributing roles across a spectrum, from highest business understanding to highest technical expertise.

Here's how these roles stack up, from most business-focused to most technically focused:

The Missing Link: Super Users

One of the most common pitfalls I see is the absence of super users and product owners. This creates a dangerous chasm between users and analysts, effectively splitting your organization into "business people" and "data people" – a recipe for disaster.

What makes an ideal super user?

A business decision-maker with skin in the game
Shows genuine interest in working with data
Proficient in Excel/Google Sheets and BI tools
Often found in roles like Marketing Manager, Product Manager, or Finance Manager
Can sometimes be senior leaders (I've seen CFOs and Heads of Marketing excel in this role)

The 80/20 Rule of Data Team Communication

The goal is to create a structure where:

Each role communicates only with adjacent roles
Every role can self-serve 80% of their needs
The remaining 20% is handled by reaching out to the next role up the technical ladder (down the business-understanding ladder)

This prevents scenarios where business users bypass the chain and overwhelm data engineers with requests – a common source of friction and inefficiency.

Getting the Numbers Right

Success depends on maintaining healthy ratios between roles. While I'll dive deeper into exact numbers and implementation strategies in my masterclass "Create massive business impact with your data team", here are some key guidelines:

Keep user-to-super-user ratios below 10:1
Limit super-user-to-product-owner or analyst ratios to 3:1
Adjust based on your organization's size, company stage and complexity

The final piece of the puzzle involves deciding whether to centralize or decentralize these roles between your data team and business domains. This crucial decision depends on various factors including:

Company stage and size
Available skill sets
Team preferences
Organizational culture

This is just one example of a split that works well in Scale-ups. I'll share details on more setups in the masterclass .

The masterclass also covers:

Selecting and training super users
Setting up communication protocols
Scaling your data organization
Choosing between centralized and decentralized models
Making your CEO and your stakeholders love you 🥰

Best,

Sebastian

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