Master data management: tasks and benefits

Master data management becomes important when data sits in several systems, is maintained more than once or has to be transferred by hand. It refers to relatively stable core data on customers, suppliers, products, locations, contracts or assets that is used in many processes. For companies, good master data management usually means fewer media discontinuities, clearer responsibilities, more reliable processes and a better basis for reporting and decision-making.

This article explains the tasks, benefits, typical problems, useful organisational rules and the path to a suitable software solution in clear terms. If you are also looking for the framework for bespoke software and operations, an initial rough cost estimate, a project outline with follow-up questions, a no-obligation initial consultation, project examples or background on clean development work, you will find suitable starting points there.

What master data management means

Master data management describes an organisational and technical task. Companies define which central core data exists, how it is maintained and in which system the authoritative version of a record is stored.

Transactional data is created continuously through activities such as orders, invoices or notifications, while master data forms the basis for it.

Typical examples of master data include:

• customer data
• supplier data
• product and service data
• contacts
• locations
• contract types
• technical assets
• condition classes

In practice, a core problem quickly becomes apparent: not every department uses the same terms, categories or structures. What sales classifies as a customer may be named differently or structured differently in administration. That is exactly how misunderstandings, conflicting information and duplicate maintenance across several systems or files arise.

That is why master data management is not just an IT topic. It also requires specialist departments, clearly assigned responsibilities and fixed rules on who creates, changes, checks and uses data.

The topic becomes particularly relevant when organisations grow, involve multiple locations, use many systems in parallel, introduce new portals and interfaces or replace existing software. At that point, it is usually no longer enough for data simply to be available somehow; it has to be organised consistently and reliably.

What tasks are part of master data management

Master data management includes several tasks that need to fit together from both a business and an organisational perspective. At its core, it is about structuring data so that it can be understood consistently and used reliably across different areas.

The following points are especially important:

• define data models
• standardise terms and categories
• define mandatory fields
• avoid duplicates or identify them early
• regulate approvals and checks
• make changes traceable
• improve searchability and discoverability
• manage the exchange with other systems properly

A data model is the business structure that describes which information belongs to a record and how the elements relate to one another.

Even at this stage, it becomes clear how important consistent naming and formats are. This applies, for example, to addresses, contacts, categories or technical characteristics. If the same content is recorded using different spellings or structures, it leads to misunderstandings, incorrect reports and extra coordination work.

Duplicate checking is another part of it. Duplicate records should be detected, or ideally avoided as data is entered, so that employees do not accidentally work with different versions of the same information.

Roles and responsibilities are just as important: who is allowed to create data, who may change it, who checks it and who decides in the event of conflicts between departments?

Interfaces are part of it as well. If data is exchanged between CRM, ERP, specialist systems, portals or Microsoft 365, clear rules are needed for the direction, timing and priority of the transfer.

Finally, changes should be traceable in a history. That makes it easier to correct errors and understand decisions later on. The task also includes making reliable records easy to find, so that employees use them instead of creating new ones.

The day-to-day benefits of good master data management

Good master data management makes everyday work easier wherever information is currently maintained more than once. If customer data, assets, locations or categories are not managed in parallel across lists, specialist systems and files, the effort involved in duplicate entries falls significantly.

That also reduces manual transfers between systems. This cuts typical sources of error:

• typing errors in names, addresses or labels
• outdated information in individual systems
• misunderstandings caused by different terms or classifications

Another benefit becomes clear in reporting. Reports and key figures become more robust when terms, categories and responsibilities are used consistently across systems. For managers, that means decisions can be prepared more easily on the basis of more consistent data.

Specialist departments benefit as well. Clear master data makes approvals, handovers and coordination easier because there are fewer follow-up questions and everyone works from the same basis. For public-sector clients and organisations with many stakeholders, shared data sets can also make coordination between areas much easier.

Well-structured master data also has an effect on downstream processes. Portals, forms, reports, automations and interfaces work more reliably when the underlying core data is clearly structured.

This can also be seen in a web platform from a ballmüller solutions project in which municipalities manage their sewer network, its condition and planned measures in one place. Centrally maintained core data improves visibility there, supports planning and makes collaboration easier.

Why master data management often fails in practice

In many organisations, the causes are not new but have developed over years. Alongside specialist applications, Excel spreadsheets, individual files and manual interim solutions are still in use. On top of that come unclear responsibilities and different terminology across departments.

Typical stumbling blocks include, for example:

• system landscapes that have grown historically
• parallel maintenance in specialist applications and lists
• missing rules for creation, changes and approval
• different labels for the same content

A new piece of software alone will not usually solve the problem. If it remains unclear who maintains data authoritatively, who decides on changes and which system is the leading one, the contradictions simply move into a different tool.

What makes matters worse is that many exceptions and special routes gradually reduce data quality again. Media discontinuities arise when information is transferred from emails, files or one system into another instead of being maintained at one reliable source.

Interfaces are also often a weak point. If fields are understood differently or transferred incompletely, conflicting data sets arise. In practice, this shows up in missing mandatory information, inconsistent spellings and outdated records. That makes searching, reporting and processes more difficult. For durable solutions, clean development work is therefore important as well, for example through clear quality measures such as code reviews.

How companies build suitable master data management

It makes sense not to reorganise all company data at once. A better approach is to start with the most important data objects and processes: in other words, where errors occur particularly often, duplicate maintenance arises or several systems are involved.

Business clarifications come first. Above all, these include three questions:

• Which master data is critical for processes and reporting?
• Where do errors, contradictions or unnecessary transfers occur today?
• Which system should be the leading one?

A leading system is the place where a record is maintained authoritatively and from which other systems take it over.

On that basis, the target processes should be described: creation, changes, checking, approval, archiving and distribution to other systems. Only once these rules are clear can it be decided whether existing software can be extended or whether a bespoke solution is the better fit. That decision depends on processes, interfaces and business-specific requirements.

Bespoke software can be particularly useful when standard software does not map the data logic or the collaboration between several departments appropriately. For example, a CRM system from a ballmüller solutions project for an insurance broker shows how central customer and case data can be brought together in one solution.

The path to that can usually be structured clearly: clarify requirements, develop the concept and design, coordinate the development, ensure quality, go live and then continue developing the solution.