Through daily operations and routine functions performed in companies, large amounts of data is generated and stored as part of the company’s infrastructure. This data may take the form of employee files, ledgers of transactions, product information, and a myriad of other medium. This data is important to the structure and streamlining of any business or internal operations the company performs, and so it needs to be managed effectively. Without proper data management, there may be mismatches in information, misuse of data, discrepancies in the implementation of data provided or various other problems. Master data management is a collection of tools, processes and standards by which some of this data is defined and managed. While proper management is important for all types of data, not every data entity is serviced by master data management. Master data is generally comprised of more complex, large-scale data that is used in many applications throughout an organization, and thus needs to be properly managed to ensure consistency within and without of the organization. As a an example, a specific bill sent to a single customer is important in the function of transactions between the customer and the company, but it is only a single entity whose scope is limited so it is not considered to be master data. However, a list of all the customers and their billing cycles would be used in different departments within the company for different functions, and would thus comprise a master data entity. There are many different ways of going about managing master data, but a few general steps which can be taken to work towards establishing an effective MDM system are:
- Identify master data and its sources – Identifying what data needs to be managed and where it is stored allows for appropriate methods to be implemented for its management, and can also reveal some problems at the very basic levels of data collection.
- Identify who produces and uses master data – By identifying what generates the master data and the applications in which it is used, targeted approaches to managing this data in a productive way for both ends can be made.
- Analyze the details of the data – The type of data, its dependencies, what constraints exist for the current system used to store data are all important variables to consider when implementing a data management system.
- Develop a model – A prototype of the master data—what the data should look like, what should be stored in it, how large it is, standards that should be used when recording data, etc.—is necessary to ensure that all data meets the criteria and assumptions under which the MDM system operates.
- Choose the method and tools to be used – After the details about the data are known, a set of tools that best fits the needs of the organization with regards to its master data can be selected, modified or created.
- Developing infrastructure and maintenance standards – With the systems in place to manage the data, how data is stored and maintained becomes the next concern. Without proper standards for updating and destroying data, the systems implemented for managing it will fail to remain consistent.