21 Big Data Master Data Management Best Practices

21 Big Data Master Data Management Best Practices

21 Big Data Master Data Management Best Practices

Master Data Management (MDM) is the process of establishing and implementing standards, policies and tools for data that’s most important to an enterprise, including but not limited to information on customers, employees, products and suppliers.

Per Wiki: In business master data management (MDM) comprises the processes, governance, policies, standards and tools that consistently defines and manages the critical data of an organization to provide a single point of reference.[1] The data that is mastered may include: master data – the business objects for transactions, and the dimensions for analysis reference data – the set of permissible values to be used by other data fields Transactional data – supports applications Analytical data – supports decision making [2] In computing, An MDM tool can be used to support master data management by removing duplicates, standardizing data (mass maintaining), incorporating rules to eliminate incorrect data from entering the system in order to create an authoritative source of master data. Master data are the products, accounts and parties for which the business transactions are completed. The root cause problem stems from business unit and product line segmentation, in which the same customer will be serviced by different product lines, with redundant data being entered about the customer (aka party in the role of customer) and account in order to process the transaction. The redundancy of party and account data is compounded in the front to back office life cycle, where the authoritative single source for the party, account and product data is needed but is often once again redundantly entered or augmented.

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So, with task such important Master Data must be designed appropriately and after careful consideration to variour bells and whistles which are responsible for success and failure of the project. Following are top 21 bestpractices that needs to be considered before applying a good data management strategy.

1. Define “What is the business problem we’re trying to solve?”: With so much data and so many disperate data sources, it is very easy to get lost in translation. So, a mental road map on the overall objective will help in keeping the effort streamlined.

2. Understand how the project helps to prep you for big data: Yes, growing data is a concern and it should be sorted out at the planning stage. It is important to identify how master data management strategy will prepare your organization not only for generic enterprise data but to cope up with ever increasing big data.

3. Devise a good IT strategy: Good IT strategy always go hand in hand with a good data strategy. A disfucntional IT strategy could really throw off a most efficient designed data management strategy. A good IT strategy increase the chances of success for a good MDM strategy by several degrees.

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4. Business “users” must take full ownership of the master data initiative: It’s important that business and it’s users must take full ownership of the inititaitve. A well defined ownership will save project from several communication failure which is almost everytime responsible for any project failure.

5. Allow ample time for evaluation and planning: A well laid out planning stage ensures all the cracks and crevices are sorted out before project is rolled out. A rushed project often increases the rist of failure. Don’t underestimate the time and expertise needed to develop foundational data models.

6. Understand your MDM hub’s data model and how it integrates with your internal source systems and external content providers: When data model problems cropped up relatively late in the project, whether it was a disconnect between the hub and an important source system, or a misalignment between data modeled in the hub and an external information provider, it was very disruptive.

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