There is a shift underway with Master Data Management (MDM) that can't be ignored. It is no longer good enough to master domains in a silo and think of MDM as an integration tool.
First generation implementations have provided success to companies seeking to manage duplication, establishing a master definition and consolidating data into a data warehouse. All good things. However, as organizations embrace federated environments and put big data architectures into wider use, these built-for-purpose MDM implementations are too narrowly focused and at times as rigid as the traditional data management platforms they support.
Yet, it doesn't have to be that way. By nature, MDM is meant to provide flexibility and elasticity to managing both single and multiple master domains. First, MDM has to be redefined from a data integration tool to a data modeling tool. Then, MDM is better aligned to business patterns and information needs as it is designed by business context.
Enter, the Golden Profile
When the business wants to put master data to use, it is about how to have a view of a domain. They don't think in terms of records, they think about using the data to improve customer relationships, grow the business, improve processes, or any host of other business tasks and objectives. A golden profile fits this need by providing the definition and framework that flexes to deliver master data based on context. It can do so because it is driven by data relationships.
Most master data management professionals understand this. The challenge is that it is not how MDM implementations end up being designed and deployed. Part of this is due to the MDM products themselves, part is due to a disconnect between best practice and what has always been done. In most instances, MDM is deployed for integration and not to deliver a golden profile because involvement with the business is meant to understand definition and uniqueness. The connection between how data is used in analysis and business processes is overlooked.
A Gold Standard for Modern Data Strategy
Increasingly, organizations are augmenting their integration tool kits with data virtualization and their data warehouse environments with Hadoop and NoSQL environments. This has shifted data structure away from a physical environment to a virtual or abstracted environment. MDM is a cornerstone to making sense of master data across structured, semi-structured and unstructured sources.
Regardless of the MDM style chosen for a particular master domain or business silo, MDM will increasingly move to a layered or hybrid implementation. It will also be required to integrate more widely across sources, integration techniques and data processing (including pre-processing like MapReduce).
Different Vendor Approaches
My recent research report, “Market Overview: Master Data Management Q2 2013 - A Critical Component for Data Agility,” provides a perspective on the vendor landscape to help you sort through your options and align your particular MDM need with vendors that are positioned to support your initiative and strategy. We've identified four MDM segments:
- Master Data Quality
- Master Domain Management
- Master Information Management
- Multi-platform MDM
Each solution type has a place in your MDM strategy depending on whether you want to support operations, analytics or if you need domain specific support or enterprise.
Read the report to learn more about approaches to shift to a Golden Profile for MDM and the four MDM tool segments.
This blog originally appeared at Forrester Research.
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