2013 is rapidly approaching and everyone is focused on change, whether it's a shift in your current role, a New Year's resolution or a new business plan. Similarly, organizations are looking at their master data management (MDM) strategy with an eye on the accompanying change management. There is a shift towards a more reasonable approach to supplant the "pie in the sky" or "boil the ocean" mentality that accompanied many early MDM efforts.

David Loshin, president of Knowledge Integrity and a long-time consultant and thought leader in MDM, once wrote, "Pure and simple: The most critical factor to master data management is data quality." As more complex data sets become the norm in many enterprises, data quality will continue to increase in importance. At SAS, we have long understood that MDM is more of a journey than a destination – and it's part of an overall information management effort with many phases.  SAS Information Management provides both data quality and master data management capabilities as well as the consulting expertise at any juncture in the MDM lifecycle.

As you move through your MDM journey, you will obtain a unified view of customers, products or other enterprise assets. The project will also involve a wide variety of other disciplines including data governance, data quality, data integration, and identity resolution, all driven by business goals like optimizing revenue, reducing costs, or meeting compliance and regulatory requirements. A simple but effective planning process can make all the difference in coordinating efforts in this complex project.

MDM planning process

Data governance and MDM go hand in hand, as evidenced by the speakers at the 2012 SAS DataFlux IDEAS conference. Anytime you consolidate data from multiple data sources, it requires some rigor around defining the master entity objects, understanding the affected processes and allocating supporting resources. At SAS, we have a team of consultants with hundreds of years of combined experience in this space that can assist you in planning your MDM deployment to drive business value, reduce project risk, and improve collaboration between business and IT.

Since MDM is a large undertaking, IT and business groups require a way to manage this complicated process – and set the groundwork for more unified corporate information. Ideally, companies begin an MDM journey with the following processes:

1. Business case generation – This phase requires you to generate and prioritize of business requirements through business stakeholder interviews. This information is then distilled into business use cases that frame how MDM can deliver business value.
2. Source, business rule and entity definition – The second phase focuses on the translation of business requirements into technical requirements. A joint effort between IT and business, this phase identifies business rules and entity definitions through a governance process. From there, you can identify, prioritize and profile data sources to assess the level of data quality prior to MDM integration.
3. Plan a phased implementation – After the initial data governance efforts, you have answered the questions and started to deliver the business rules and IT logic to manage master data.  Now, you can generate and implement a phased MDM project plan.
4. Value augmentation – In this phase, you are building on prior project success, mastering new domains, or integrating additional sources into existing domains, always with an eye towards expanding the initial investment footprint into driving more business value. Internal evangelization and alignment is critical.

Where SAS fits in

As part of the mapping phase between business and IT, the Business Data Network component of the SAS Data Governance solution allows business users to agree on a common vocabulary and collaborate in the creation of an entity that can then be exported to the MDM solution. A data quality analyst or developer could then leverage SAS Data Quality or SAS Master Data Management to import that entity, profile sources, add technical information like the size and data type of the field and augment the matching rules – all while creating that entity definition. What this means is more IT and business collaboration and concrete technical artifacts output from a business process that feeds the front-end of your MDM or data quality initiative.

In my next blog post, I will explore the SAS DataFlux Master Data Management Foundations technology, a component of several of our information management offerings, and how it can assist with this phased approach to MDM. Master Data Management Foundations is unique in the marketplace in that it provides that transition between more short-term tactical data quality challenges (for example, de-duplicating a mailing list) and longer-term strategic MDM initiatives. Stay tuned, and have a happy 2013.