Mistake 2: Ignoring data governance needs at the project or enterprise level.

"The important thing to realize is that when doing MDM, you're really doing data governance," Shankar says. Data governance is unique to each company since it is based on the company's business processes, culture and IT environment, he points out. However, most companies select an MDM platform without much thought to their enterprise data governance needs. "It is critical that the underlying MDM platform is able to support the data governance policies and processes defined by your organization," Shankar notes. "In contrast, your data governance design could be compromised and forced to adapt to the limitations of some MDM software platforms with fixed or rigid data models and functionality."

Controls and auditing capabilities are also important data governance components, according to Shankar. To properly support this function, the RFP should "require the MDM platform to integrate with a company's security and reporting tools to provide fine-grained access to data and reliable data quality metrics."

Mistake 3: Failing to ensure the MDM platform works with a company's standard workflow tool. Workflow is an important component of both MDM and data governance because it can be used to approve the creation of a master data entity definition and to determine, in real time, which conflicting data entities survive, Shankar says.

Workflow can also be used to automatically alert the data steward (the employee who's in charge of the data) to any data quality issues. "It is important to raise the question of how the MDM platform will integrate with the standard workflow tool that you have selected," Shankar notes, adding that several MDM vendors bundle their own workflow tool, which may not offer integration with a CIO's standard workflow tool. "An open architecture that'll work with any workflow tool is key," he says.

For more on enterprise workflow, see Making Workflow Work and Flow for You.

Mistake 4: Failing to ensure the MDM solution supports complex relationships and hierarchies.

With a single-entity master data hub, such as a customer hub for sales and marketing, the hierarchies and relationships are relatively straightforward, Shankar notes. Conversely, hierarchies among multiple data entities can be highly complex. Examples include retail locations in the Eastern region stocking only certain products, complex counter-party legal hierarchies determining credit risk exposure, or an account holder's spouse being an individual of high net worth. "Relationships have been an afterthought for most of the MDM vendors," Shankar says. "Here is the MDM system, and then you slap on hierarchies. That's not right. Hierarchies need to be treated as master data."