Governance before the tool: How clarity creates good master data

There are companies that have their master data under control. Materials, specifications, and supplier data are reliably maintained. The departments involved work from the same foundation. If a question arises, it's quickly clear who will handle it.

Of course, the system used also plays an important role. However, the foundation for reliable master data lies elsewhere. It arises from clarity.

That's exactly what I mean when I say: Governance comes before the tool.

I have been involved in master data projects for many years. At the beginning, the focus is often first on the software. This is understandable. A tool can be selected, configured, tested, and presented. It is visible and tangible.

The crucial questions initially lie on the organizational side. Who bears which responsibilities? What rules apply? How are decisions made? How do the departments involved collaborate?

That's where effective master data governance begins.

Behind reliable master data lies collaboration.

A master data record is rarely created in a single location. For a material, a recipe, a product specification, a supplier, or a customer, information is provided by multiple departments. Other departments then use this data for their daily work.

Purchasing requires reliable suppliers and procurement data. Planning works with scheduling parameters, units of measure, and lead times. Production accesses materials, bills of materials, or recipes. Quality assurance requires specifications, test characteristics, and release status.

This applies to discrete manufacturing as well as to the process industry, to a CDMO contract manufacturer or to laboratory diagnostics.

A good master data set therefore involves more collaboration than is visible in everyday practice. This is precisely where its importance lies. Reliable data provides orientation, supports processes, and creates a common basis for decisions.

For this to succeed, each relevant data field needs a clear professional responsibility. Additionally, a binding rule for maintenance and a defined decision-making process for questions, deviations, and special cases are required.

Once these points are clarified, data quality becomes controllable.

Roles provide orientation

Good governance begins with clearly defined roles.

A sponsor provides direction, mandate, and support for the project. A subject matter expert, the data owner, is responsible for a specific data area and makes fundamental decisions. A data steward defines rules, oversees their application, and monitors data quality. Other roles include data collection, review, and technical implementation.

This is about far more than boxes in an organizational chart. A viable role model answers very practical questions:

Who decides on technical specifications?

Who defines the requirements for a data field?

Who checks the quality?

Who is editing a data record?

Who makes the decision when interests differ?

Clear answers facilitate daily collaboration. Everyone knows their role. At the same time, it's transparent where a professional decision is made.

Rules create a common language

Roles unfold their effect in conjunction with binding rules.

These rules describe, for example, what information is mandatory, what standards apply to naming conventions, how duplicates are avoided, and what quality criteria a data set should meet.

Good rules provide security. They reduce recurring coordination and create a common language across departments, locations, and systems.

A governance policy defines the overarching guidelines. Standards and work instructions then translate these guidelines into concrete requirements for individual data objects and processes.

This creates a connection between strategic direction and daily work.

Processes bring responsibility into everyday life

The third pillar consists of the processes.

A good master data process describes the entire lifecycle of a data record. It begins with the request and continues through creation, review and approval to modification, blocking or archiving.

The process follows the data object across all participating areas. Professional responsibility, operational processing, and technical implementation are intertwined.

A clear process answers, among other things, the following questions:

What information is needed and when?

Which role provides or verifies this information?

What approvals are planned?

How are changes documented?

How are exceptions and differing professional interests handled?

This forms the basis for a process that all participants can understand and reliably apply.

The MDG tool is the enabler of governance

Once roles, rules and processes are defined, an MDG tool can reach its full potential.

It translates the organizational framework into daily work. The tool manages workflows, assigns tasks to the responsible roles, checks mandatory information, integrates validation rules, and documents decisions.

Governance provides the logic. The MDG tool makes it effective.

That is precisely its strength. It ensures that the previously defined governance is applied consistently. Processes become transparent, decisions traceable, and rules scalable. At the same time, the same principles can be used across locations, companies, and data areas.

The technical workflow thus becomes the visible expression of a professionally and organizationally well-thought-out governance.

A framework for many data areas

Much of good master data governance can be applied to different data areas.

This includes the guiding principles, the role logic, the decision-making processes, and the basic structure of the processes. This framework is then further specified for materials, suppliers, customers, and other data objects.

This creates consistency and saves time. Teams work on the same basis and specifically add the requirements of their respective data areas.

Once thoroughly thought through, it can be used effectively time and time again.

The first step begins with organization.

When considering a master data or MDG tool, the central question begins with the organization:

What roles, rules, and processes should the system later represent and support?

Once this framework is clear, the software becomes the lever that companies expect it to provide.

In the coming weeks, I will go through the individual building blocks step by step. These include the basic principles of good master data governance, a practical role model, the path from governance policy to a reusable standard, and the launch of a governance program.

Next week: The basic principles of good master data governance.