Who Owns Master Data? Why It’s a Governance (and Political) Decision

In last week’s post, The Role of Master Data in Analytics, we looked at how master data directly impacts your analytics stack, from broken hierarchies to duplicate records and inconsistent attributes. We made the case that most dashboard problems don’t start in the BI layer; they start upstream, in master data models that lack consistency, governance, and trust.

This week, we’re shifting focus from the technical to the political. Because knowing your data is broken isn’t enough. You have to know who owns it, and that’s where things get tricky. Ownership isn’t just a line in a RACI chart. It’s a decision-making structure shaped by influence, accountability, and power. In this post, we’ll unpack why ownership gets political, what happens when it’s unclear, and how to create the kind of durable, cross-functional model that real governance requires.

Master Data Ownership Is More Than Just a Tech Problem

Ask ten people in your org who owns master data, and you’ll probably get ten different answers:

“IT owns it.”
“No, the business does.”
“Depends on the system.”
“Everyone owns it.”
“Wait… what is master data again?”

They’re all kind of right, but also all kind of wrong.

Because ownership of master data isn’t just about who touches the data, it becomes political because decisions have to be made around who shapes and governs the data.

I don’t mean political in the “cutthroat, backroom-dealing” way. It is political in the sense that there are competing priorities and varying influences throughout the organization. Whoever “owns” master data, in practice, must be able to navigate a web of stakeholders with different goals, vocabularies, and power.

Ignoring this will cause your data strategy to fail. Trying to force ownership on people without creating alignment will cause everything to fall apart once there’s friction.

Master data ownership is about clarity, buy-in, and collaborative decision-making. It does not simply begin and end with the RACI chart.

Data Ownership vs. Stewardship: Why the Distinction Matters

One of the first mistakes teams make is using “ownership” and “stewardship” interchangeably, but they are not the same.

Ownership is about who can make decisions, and who is accountable for what the data means, how it is structured, and how it gets used. Because they define the rules, they are the ones on the hook if something goes wrong.

Stewardship, on the other hand, is more operational. Stewards are responsible for ensuring the data is accurate, clean, and compliant. They do this by enforcing rules established by owners, monitoring data quality, and resolving issues as they arise.

Consider this:

The owner decides what “Customer Type” should mean.
The steward makes sure that value is filled in correctly on every record.

When these roles are confused, your data suffers. Either no one makes decisions, or stewards get stuck enforcing unclear rules. Clear separation and collaboration between owners and stewards is essential for effective governance.

Why Master Data Ownership Gets Political

Master data doesn’t belong to just one department because it crosses boundaries.

Customer data is used by Sales, Marketing, Finance, Customer Support, and IT.

The same goes for product data. Supply chain, eCommerce, Finance, and Manufacturing all rely on it, and they all want it structured their way.

Here’s how it typically goes:

Sales wants customer records grouped by territory.
Marketing wants them grouped by engagement segment.
Finance wants rollups by billing structure.
IT wants one consistent definition across systems.

Each team has valid needs and thinks its perspective should take priority. Interestingly, none of them are entirely wrong.

But when every team optimizes for its own goals without a shared governance model, we begin to see issues:

Conflicting definitions get hardcoded into reports.
System integrations fail silently.
Cross-functional analytics lose credibility.

This results in an ownership “tug-of-war.” A solid framework helps to mediate the tensions that inevitably arise.

Signs of a Broken Data Ownership Model

How do you know if your organization has an ownership problem? Here are some common warning signs:

No one can say who approves new values in a core field.
Definitions change without notification or documentation.
Business teams argue over what a field “really” means.
IT gets blamed for data issues they don’t control.
People say “everyone owns the data,” which means no one is accountable.

This results in:

Finger-pointing instead of collaboration
Duplicated logic in downstream systems
Stewards working in isolation
Loss of trust in data products

Master data becomes fragile, which reduces (and eventually eliminates) reliance. Finally, your governance model loses authority.

How to Establish Clear Master Data Ownership

Removing the politics is almost impossible, but you can create a structure that helps manage it.

Here are some real-world, practical steps you can use to define and maintain data ownership:

Define Ownership by Business Domain, Not System

Don’t assign ownership based on where the data lives. A system like SAP or Salesforce doesn’t own data – people do.

Instead, align ownership with business domains:

Marketing owns segmentation logic.
Finance owns revenue classifications.
HR owns employee status definitions.
Product teams own SKU hierarchies.

This helps assign accountability to those who understand the meaning and use of the data, not just its storage location.

Set Up a Cross-Functional Data Council

Governance shouldn’t happen in silos. Form a data council with stakeholders from key domains: business, IT, data management, analytics, and compliance.

This group should:

Define shared data standards
Resolve ownership conflicts
Approve changes to core definitions
Maintain alignment across systems

We’re not talking about lots of bureaucracy. This should be nothing more than a recurring forum where decisions are made, documented, and communicated.

Document Field-Level Responsibilities

For any field that appears in reports, workflows, or integrations, you should be able to answer:

Who defines this field?
Who approves changes?
Who ensures it’s filled in correctly?
Who uses it downstream?

Store these answers in a data dictionary, governance catalog, or stewardship platform. The goal is transparency.

Empower Stewards with Authority and Backing

Stewards are often asked to improve data quality, but are not given the authority to fix root causes. That doesn’t work.

Owners must actively support stewards:

Approving escalation paths
Resolving rule conflicts
Helping define edge cases
Participating in quality reviews

A good owner shows up when decisions are needed.

Treat Conflict as a Governance Opportunity

When two teams disagree about a data definition, that’s an opportunity.

Rather than patching it locally, raise it to the governance council. Use the conflict to:

Clarify definitions
Improve documentation
Update policies

These moments refine your model and increase buy-in.

Ownership Isn’t a Chart, It’s a Relationship

You can create beautiful swimlanes and RACI matrices. You can map systems and roles down to the column.

But if the people involved don’t talk to each other, it won’t matter.

Real ownership happens in relationships:

Product managers working with stewards to define attribute rules
Finance collaborating with data engineers to clean up hierarchies
IT partnering with business teams to align source and reporting logic

When ownership is treated as a relationship (and not just a title), things get done faster, decisions last longer, and trust builds across the data landscape.

Everyone touches the data.

But someone has to lead it, and leadership starts with owning what matters most.