Accurate master data is essential for a smooth business flow. But which data belongs in this category? We explain how to distinguish between master and transaction data, how professional master data maintenance should be organized, and why it will become a competitive factor in the future.
Definition: What Is Master Data?
Master data is corporate data that is required for all business processes. A central feature: it remains unchanged over a considerable period of time. Master data includes, for example, data domains such as customer, product or supplier master data. The quality of master data management is a decisive factor in determining the strategic opportunities and operational success of a company.
Examples of Master Data Can Be Found in Each Business Area
Organizations are experiencing exponential data growth. New data is being added, modified and new formats integrated. Master data forms the grid into which this new, rapidly changing data is placed.
Key master data domains:
- Fixed Assets
Examples of master data include customer addresses, product categories, vendor names, and charts of accounts. Email addresses, birth dates and telephone numbers are also included.
Master Data vs. Transaction Data: What’s the Difference?
Master data forms the basis for transaction data. These are often department-specific and change frequently, while master data is used across departments and remains unchanged over a long period of time. It relates to basic criteria, while transaction data describes individual manifestations of customer behavior.
With the growing amount of data in companies and smart analytical tools, the importance of data for companies is rapidly increasing. Business intelligence applications allow users to regularly query data and create reports. However, they can only obtain a meaningful evaluation by combining customer master data and customer behavior data. Only if the master data in the system is correct and up to date, meaning that its data quality is adequate, are the analyses a reliable support in decision-making.
A few examples of the relationship between master data and transaction data:
Organizing Master Data Maintenance Optimally
Master data management (MDM) is part of the foundation for the digital transformation of a company.
Many companies keep their master data in different systems. Depending on the degree to which the systems of the various departments are harmonized or consolidated, they then have different options for data analysis. However, decentralized data storage easily creates redundancies. Updates are time-consuming and there is a risk that changes will not be transferred to each system.
Anyone who wants to optimize the organization and maintenance of their master data should introduce a master data management system (MDM system) and merge their data storage - at least within the individual master data domains. The system bundles the data, prepares it in the background and makes it available to applications throughout the company. The entire lifecycle of the data is organized via the MDM system - from input, through changes, to archiving of data. This way, the master data is available to the operative business processes at any time in high quality (up-to-date, consistent, accurate, etc.).
If master data management is centralized, it significantly contributes to making companies fit for further steps in the digital transformation. However, implementing master data software is no panacea for business challenges. What is more important is to set up master data management in the right strategic way. Implementation should be based on a company-wide data strategy.
Consequently, the project affects not only the IT department, but also management and business departments. Before introducing master data management, companies should therefore bring together all the stakeholders involved. Through their different perspectives, those responsible for the project gain a 360-degree view and can consider all relevant aspects of data storage and data use in the new master data management.
Roles and processes should be reviewed and adapted as part of the new master data governance. For example, if many employees previously had editing rights, a more differentiated approach can be implemented with the new master data software.
While the selection and implementation of the appropriate software is important, the strategic realignment of processes and change management, the communication of upcoming changes to employees, play a much greater role in the subsequent success of MDM. Many years of consulting practice have shown: IT determines only 10 percent of the success of an MDM project; organizational culture and processes account for 60 and 30 percent, respectively, of the overall outcome.
Conclusion: Management of Master Data Is Becoming a Success Factor
Many companies have so far underestimated the importance of master data. They store data redundantly and decentrally. In the best case, they achieve a good level of security, but this approach results in unnecessarily high costs for maintenance and care. The risk of outdated and inconsistent data is also high in this approach - especially since many companies lack processes to keep data quality at a high level in the long run.
In the future, companies can no longer afford to neglect their master data management. Decisions are being made less and less on the basis of gut instinct and more and more frequently driven by data. If companies want to make optimal use of the latest business intelligence applications and technologies such as PRP, AI or cyber-physical systems, they need a central master data management. It forms the necessary technological and process-related basis - and is thus developing into an increasingly important competitive advantage for companies in every industry.
Frequently Asked Questions About Master Data
Master data forms the basis for transaction data. Their evaluation is playing an increasingly important role in operational and strategic decisions. If they are not available or if they are of poor quality, this directly affects the validity of analyses and predictions. To make good data-driven decisions, companies should therefore ensure that their master data is of high quality.
By implementing a master data management solution, companies can ensure that they are working with a single point of truth. Data updates have a company-wide impact and inconsistencies are avoided. Companies must individually define what requirements they have for good data quality. However, it is not sufficient to clean up the data stock once with regard to these criteria. Companies must ensure that they implement processes for master data maintenance in order to permanently maintain the quality of the data sets.
The best solution considers the individual situation of the company. To make the right choice, companies should clarify their master data management requirements in detail before deciding to buy: Should multiple master data domains be maintained or a single one? Is an on-premise or a cloud solution favored? Which functions are non-negotiable? The right provider will result from the goals and expectations. Those who want support in choosing and integrating an MDM solution can turn to external service providers. Ideally, they will not only provide advice, but also implement the solution in the system - in accordance with the data strategy that has been developed.
You would like to introduce Master Data Management in your company?