Microsoft Opens up Its Business Intelligence

Loraine Lawson

Master data management technologies can be a bit confusing. Some so-called MDM solutions are actually more focused on data integration or governance, others are more about business intelligence. There's also the issue of "categories" of MDM. Master data management evolved from both customer data integration and product information management, and solutions still tend to address one or the other, not both.

And then there's this whole question about master data management as a discipline, rather than a technology -- and the related questions about what role IT should and should not play in master data management implementations.

In short, if you're confused, don't be surprised. MDM is complicated.

Fortunately, I've found a very helpful free resource that sorts it all out for us.

It's a free sample chapter, published on InformIT, from a new book, "Enterprise Master Data Management: An SOA Approach to Managing Core Information," from IBM Press.

It's 32 pages, so this is no quick read, but it does a great job of explaining why companies need MDM and the types of MDM solutions, including a discussion on industry-specific MDM offerings, and giving a look at the three key methods of use for MDM:

  • Collaborative authoring
  • Operational, in which the MDM server acts as an Online-Transaction Processing (OLTP) system that responds to requests from multiple applications and users (it's commonly used in PIM domain)
  • Analytical, which is generally deployed with BI systems

For IT leaders, there's a particularly useful discussion on the four main styles of MDM implementations. Most of the pieces I've seen have focused on how MDM tools are used to pull data together, cleanse it and create a "golden copy" of master data. The chapter refers to this approach as "consolidation implementation style."

But there are actually other types of MDM implementations, including a registry implementation and a transactional hub implementation, which is an expensive and complicated style where all data changes are funneled to and through MDM.

The chapter also examines various business cases for MDM, including a look at cost savings, efficiencies, the impact on regulatory compliance, and how MDM can help with changes and innovations, including accommodating mergers and acquisitions.

It even briefly touches on how MDM can work with and complement service-oriented architecture, though I suspect the bulk of that discussion is covered in the book's remaining chapters.

All in all, it's the best resource I've seen online for explaining the nuances of MDM. That said, here are a few other IT Business Edge resources to help round out the discussion:



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Resource centers

Business Intelligence

Business performance information for strategic and operational decision-making

SOA

SOA uses interoperable services grouped around business processes to ease data integration

Data Warehousing

Data warehousing helps companies make sense of their operational data


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