I was just thinking…
…about the folks who came up to the Trillium Software booth at the recent MDM New York 2009 Conference. They all talked about a very similar problem they’re trying to solve – calculating the benefits derived from multiple and different data quality initiatives across their organizations.One of them remarked, "We have five or six completely-separate data quality implementations throughout the company, and I would like to:
- Better understand the goals of each individual data quality initiative,
- Determine "standardized" best practices in order to reduce work load amongst the groups, and,
- Better understand where we are not capitalizing on a truly unified view of customers and products.”
And, most important, he wants to attempt to drive a “coherent, overall data quality strategy throughout the organization.” Easier said than done as many of you know.
It was refreshing to hear and talk to a good handful of customers and prospects that have moved beyond a single point solution for data quality within their organization to a state of ongoing operational data quality in support of strategic business processes.
And, like some kind of “Dr. Phil” of data quality, it was helpful for me to let them know they are not alone in their efforts to derive more business value from data quality. They are not the only ones feeling lost within their uncoordinated data quality efforts. Believe me!
If you find yourself in this situation, you can bet your colleagues on other data quality projects across the company feel the same way. Take a step forward and ask your colleagues, “Do you feel the same way I do?” If so, it’s important to know that there are real methodologies and best practices that have been established to help organize your goals, establish standards, and measure expected results for better return on investment.
Consultants can help, and software providers can assist as well. But, technology should not come first. You need to establish your internal goals and milestones for cross-enterprise operational data quality first, then bring in help to get it all together.
My friend and colleague Jim Orr blogs regularly about these first-step data governance and data quality best practices. Check him out.