Mistake 7: Thinking "probabilistic" matching is adequate.

Don't be scared; this isn't as complicated as it seems. "Matching" simply means the MDM system's techniques to reconcile the data. "If you look at an MDM hub, the crux of the system is matching," he says. "The truth that [CIOs] don't realize is that the matching is in eye of the beholder."

For example, Shankar points out that several types of matching techniques are commonly in use, such as deterministic, probabilistic and empirical. "No single technique is capable of compensating for all of the possible classes of data errors and variations in the master data," he says.

In order to achieve the most reliable and consolidated view of master data, Shankar advises that the MDM platform should support a combination of these matching techniques, with each able to address a particular class of data matching-a hybrid-type technology, so to speak. A single technique, such as probabilistic, likely cannot find all valid match candidates, or worse, may generate false matches, he says.

Mistake 8: Underestimating the importance of creating a golden record.

Ahhh, the golden record. The single version of the truth. This is what everyone is after. "Unless you have a golden record, you don't have MDM," Shankar says. "MDM needs a single point of truth in order to cater to all processes." So for MDM to be successful, believes Shankar, it is not enough to simply link identical data with a registry style because this will not resolve inconsistencies among the data. Rather, master data from different sources should be reconciled and centrally stored within a master data hub. "Given the potential number of sources across the organization and the volume of master data, it is important that the MDM system is able to automatically create a golden record for any master data type such as customer, product or asset."

In addition, he notes that the MDM system should provide a robust "unmerge" functionality to roll back any manual errors or exceptions-a typical activity in large organizations where several data stewards are involved with managing master data.

Mistake 9: Overlooking history and lineage features to support regulatory compliance.

Today, business users not only demand clean and reliable data, but they also require validation that the data is in fact reliable, which is a newer trend. "This is a challenging and daunting undertaking, considering that master data is continually changing with updates from source systems taking place in real time as business is being transacted, and while master data is merged with other similar data within the master data hub," Shankar says.