What shapes your master data repository and content strategy is built around the level of item detail necessary for identification, classification, and transaction management. The critical component that must be addressed is data quality.  A simple procurement transaction can be fulfilled with as little as a part number, price, and UOM, however rich item data must be available in order to record the transaction properly, ensure that the right part is being requisitioned, and capture as well as utilize data for plant management.

How much rich item data should be provided?  It’s important to consider not only the information required identifying the item but also the data elements required to support your information systems infrastructure.
Classification and Normalization are fundamental elements to your data quality decisions.  If you elect to use simple content with a very high-level classification structure, you lose the ability for spend analysis at the item level.

For example, if all office supplies are mapped to a UNSCPSC code of 44120000, you classify all of these items at a second tier level and thereby lump together distinct product categories—such as pens and printer cartridges.  Your ability to identify your spend in either of these two distinct commodities is limited as they are blended together with hundreds of other commodities. While the effort associated with sub-dividing this category make little sense in the procurement transaction itself, it could be critical to your spend analysis and future strategic sourcing efforts. 

Normalized content or data is just as important for certain commodities, although it is simple to select the correct ink pen from a description and a picture, selecting the correct 2000 PSI pump becomes a challenge. 
Normalized data not only allows users to compare items from a fit, form, and function perspective, it also enables important business processes such as asset tracking and management.

Although it may seem that a simple catalog with a nice picture and a marketing description meets basic user’s discovery needs, without the appropriate data quality for your content you could be diminishing your enterprise automation potential in other areas of the business.

-Raul Rom

DQM has to be implemented as a PIM module. The key objectives of this module are:

• Ability to analyze data in any Entity Data Container (Analysis Services)
• Ability to normalize, standardize data (Transformation Services)
• Ability to have a workbench environment with the right checks and approvals while working with data quality issues

Some of the key components of the DQM module will be:
• Data Quality Dashboard
• Data Quality Profiles Configuration
• Data Quality Workbench
• Service Jobs
• Data Quality Administration

-Raul Rom

Keep watching this blog. Next week I will be publishing a short article about the taxonomies challenge in the PIM arena. Are you looking to standardize all of your product information on a custom taxonomy or are are you considering UNSPSC? How about an industry standard such as PIDX? How do you plan to utilize multiple taxonomies within the PIM environment?

Stay Tuned for the article next week.

-Raul Rom