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Take a collaborative approach to defining data quality

EMC’s Barbara Latulippe explains the importance of working closely with the business to understand who is going to use the data and how.

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“Where the data is in its consumption life cycle will define what level of quality is needed. There are higher expectations of the data quality as you mature that life cycle.”

—Barbara Latulippe, senior director of enterprise data quality and governance at EMC

Barbara Latulippe, senior director of enterprise data governance at EMC, spoke to Potential at Work about her efforts to develop an enterprise data quality and governance roadmap at the company. She advocates a collaborative and consistent approach to defining data quality and emphasizes the need for cooperation across all business units, not just between IT and individual business units. With more than 25 years of hands-on experience with MDM and enterprise applications, she certainly knows what she’s talking about.

How can information leaders work with different people in their organizations to help them define data quality?

Latulippe: We use what we call Information Governance Councils that include cross-business unit and cross-functional participation. We’ve strived for common definitions of all enterprise attributes, which we’ve put in our business glossary once the definitions are reviewed and approved. We now can say that we have one common definition that's been approved by the Governance Council, and it's under change control. If someone wants to make a proposal to either add values to a particular field or use the field differently, they have to come to the Governance Council with a business proposal and a cost impact.

Have you ever had any conflict around different definitions of data quality?

Latulippe: It's required to have the data consumers talk to the data creators. I think that helps bring a lot more awareness of how the data is used in the various business processes which consume it. Oftentimes, these teams haven't really collaborated before. In those instances where there truly is a conflict that you can't resolve, typically it means you need a new data field. What is necessary is to follow the data in motion across the enterprise and put financials behind it.

For instance, we did a cost analysis of the implications of a missing zip code. We found not all applications required a zip code in their data model or data entry screens. The council followed the end-to-end process flows, and that one data point had a huge cost impact to the company if it was missing. As a result, it's now a mandatory field worldwide and it is checked in real time at the point of entry.

In your Governance Councils, you have people from all different areas working to define data quality. Can the same set of data have different definitions of data quality?

Latulippe: It goes back to the life cycle of data and what the consumer defines as good data quality What marketing needs is different than what someone trying to fill a sales order in CRM needs. Where the data is in its consumption life cycle will define what level of quality is needed for each attribute. There are higher expectations of the data quality as you mature that life cycle from campaign to retain as well as the number of attributes consumed.

Do you want to empower the business at your organization to share in the responsibility for data quality and governance? Consider getting certified in data quality at a level that corresponds best with your role.

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