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.

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.

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. These problems can be avoided by really understanding how the hub is designed, and then mapping that back to your source systems and your external information sources.

7. Identify the project’s mission and business values:
This is another important area that needs it’s due attention. A clear project mission and business value definition helps in making sure high ROI is thought for and planned after in the process. One must link the initiatives to actionable insights.

8. Choose the best technology platform:
Choosing a good technology is important as well. Remeber, you don’t change your technology daily, so putting some thought and research into it makes a lot of different in sustainability of the project. A good technology should help organization grow to next several years without presenting too much growth bottlenecks.

9. Be real and plan a multi-domain design:
In a real world, many MDM technologies grew up managing one particular type of master data. A good strategy must be consistent across. So, applying the same approach to the various master data domains, whether those be customer, product, asset, supplier, location or person is a good strategy.

10. Active, involved executive sponsorship:
Most organizations are very comfortable with their “islands of data” and with technology being implemented in silos. For someone in the organization to come along and suggest changing that status quo, and to start managing critical information centrally, treating it as a true corporate asset, is going to mean some serious cultural change.

11. Use a holistic approach – people, process, technology and information:
This may be the most important best practice. You’ve got to start with the people, the politics, the culture, and then to make sure you spend at least as much time on the business processes involved in data governance and data stewardship. These really deserve a separate article of their own.

12. Pay attention to organizational governance:
You must have a very strong governance model that addresses issues such as change management and knowledge transfer. Afterall, the culture in an organization is a most important entity and a sorted plan to derisk project from it ensures success.

13. Build your processes to be ongoing and repeatable, supporting continuous improvement:
Data governance is a long term proposition. As a reality of any enterprise life, as long as one is in business, enterprise will be creating, modifying, and using master data. So if everyone in the company relies on them, but no one is specifically accountable for maintaining and certifying their level of quality, it shouldn’t be a surprise that, over time, like everything else, they become more and more chaotic and unusable. So plan from the beginning for a “way of life”, not a project.

14. Have a big vision, but take small steps:
Consider the ultimate goal, but limit the scope of the initial deployment, users told Ventana. Once master data management is working in one place, extend it step by step, they advised. Business processes, rather than technology, are often the mitigating factor, they said, so it’s important to get end-user input early in the process.

15. Consider potential performance problems:
Performance is the 800-pound gorilla quietly lurking in the master data management discussion, Loshin cautioned. Different architectures can mean different performance penalties. So, make some room for repair.

16. Management needs to recognize the importance of a dedicated team of data stewards:
Just as books belong in a library and a library needs librarians, master data belongs in a dedicated repository of some type, and that repository needs to be managed by data stewards. It is cruicial to start with convincing management of the need for a small team of data stewards who are 100% dedicated to managing the enterprise’s master data.

17. Consider the transition plan:
Then, there’s the prospect of rolling out a program that has an impact on many critical processes and systems — no trivial concern. Loshin recommended that companies should plan an master data management transition strategy that allows for static and dynamic data synchronization.

18. Resist the urge to customize:
Now that commercial off-the-shelf hub platforms have matured a bit, it should be easier to resist the temptation to get under the hood and customize them. Most vendors are still revving their products as often as twice a year, so you definitely don’t want to get into a situation where you are “rev locked” to an older version.

19. Stay current with vendor-provided patches:
Given the frequency of point releases, patches and major upgrades, you should probably plan for at least one major upgrade during the initial implementation, and be sure to build “upgrade competency” in the team that will maintain the hub platform after the initial project goes live.

20. Carefully plan deployment:
With increasing MDM complexity, training of business and technical people is more important than ever. Using untrained or semi-trained systems integrators and outsourcing attempts caused major problems and project delays for master data management users.

21. Test, test, test and then test again:
This is like the old saying about what’s important in real estate – “location, location, location”. Your MDM hub environment is going to be different, by definition, than every other environment in the world.

Originally Posted at: 21 Big Data Master Data Management Best Practices

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