Best Practices for Master Data Management

Aside from customers, data is the most valuable resource available to any modern enterprise. Data can lead to more efficient business processes, help you design better products, vastly improve your analytics, provide better customer experiences, and much more. The problem is that many organizations are still failing to monetize their data as well as they should. If you want to have a successful digital transformation, then you’ll need to make master data management (MDM) a priority for your enterprise.

If you’re still relying on legacy systems, then each of your data sets are siloed. In other words, they all have separate data sources and have no way to communicate. Master data refers to data sets that are shared between two or more systems. If you collect customer data from your customer relationship management (CRM) system, for example, and you share demographics information with a marketing research information system (MRIS) to gather data for more effective promotions, then that data is master data.

Collecting and maintaining master data from your source systems is the best way to ensure that your data is always accurate and current. In other words, MDM is crucial to ensure data quality. Here are a few best practices for MDM that you should incorporate in your enterprise.

Integrate your software.

Integrate your software

It will be practically impossible to implement an effective MDM solution if you’re still keeping your data sets in separate data silos. While you could manually transfer data between silos, this process is prone to human error and may damage data quality. You could also use the traditional extract/transform/load (ETL) method, but this is time-consuming, and data may be out of date by the time you need to use it.

Fortunately, these days, the data transformation process can be handled automatically through data virtualization, and application programming interface (API) portals can allow your software systems to communicate. This way, you can automatically and continuously collect all your data items in a single source of truth.

Organize your data.

Organize your data

Once you have your master data collected in a single version of the truth, you can start consolidating your records and eliminate any duplicates of data sets that could be slowing your systems down. In order to improve operational efficiency, you’ll want asset data to reach your MDM program as soon as possible. This means eliminating redundant data reporting measures and establishing data hierarchies. Establishing hierarchies makes it easier to see the full relationships between both small and large data sets.

Encourage mass adoption of MDM.

Encourage mass adoption of MDM

It’s important to keep in mind that an MDM system isn’t just for data scientists or IT leaders. It’s meant to be a single solution that your entire enterprise can take advantage of. That’s why it’s important to choose a vendor who can provide a complete master data management program to manage all assets, such as the TIBCO EBX software.

TIBCO is an industry leader in data science, and their EBX software allows you to model any master data, including reference data and data governance artifacts, in one system. It also supports an easy application design process that lets you model any data sets to create data-driven applications. Use cases range from creating collaborative workflows between departments to providing personalized customer journeys. Best of all, EBX even makes it easy to create data visualizations, such as charts and graphs, to simplify raw data sets. This way, you’ll have no issues explaining even the most complicated data sets to stakeholders via accurate data models.

Finally, EBX is also compatible with other enterprise resource planning (ERP) technologies, such as SAP, OneStream, and TM1, so you can still use your existing systems, if applicable. An effective MDM process like this is the best way to get data updates in real-time and ensure that you’re working with the best business intelligence at all times.

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