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May 25, 2009

Data Quality Initiatives: Start Small

Filed under: Data Cleansing, Data Quality — Tags: — Olga Belokurskaya @ 8:24 am

Why enterprise-wide data quality initiative often turns into a total disappointment? According to Forrester’s report “A Truism For Trusted Data: Think Big, Start Small,” it happens because of managers’ ambitions to implement an enterprise-wide system for trusted data all at once.

However, experts from Forrester recommend thinking global, but starting small. In other words, they advice consider a bottom-up approach that defines quantitative and qualitative ROI for only those few select functional organizations that can best articulate and measure the business impact poor quality data has on processes.

Here is a short overview of the steps, Forrester recommends, to effectively implement data quality initiatives:

1. First of all, those responsible should start from defining what they mean by “data quality” and “trusted data”. As Jill Dyché once mentioned, it’s time to understand the following seldom-understood truth: That there are different levels of “acceptability” for data. And, according to her, the key is to understand company’s business requirements and then drill them down to data requirements. That will tell conclusively what good enough for the company really is.

Forrester defines “data quality”  as “data used by business stakeholders to support their processes, decisions, or regulatory requirements with no reservations as to the data’s relevance, accuracy, integrity, and other previously agreed upon definitions of quality.”

Forrester reminds that data quality must come directly from business stakeholders, for they are those who understand business requirements of the company, and thus may set the standards for company’s data quality.

Then, Forrester insists on building a business case that starts small.

“Scoping and prioritization based on the business processes within the organization that are most critically affected by poor data quality is the key to defining the business case that will get your trusted data initiative off the ground,” say experts.

3. As word of the value of the data quality project gets around, other organizations, such as those responsible for order management and fulfillment, may also want to implement data quality improvements within their environments.

“Eventually, the tide will turn and these business stakeholders will sign up and support an enterprise-class solution to solving their data quality problems, but for that to happen, value must be demonstrated,” Forrester concludes.