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For data quality success, measure results

Say you're launching a program to review customer data or other corporate information and clear out errors, inaccuracies and duplications. Most likely, the sheer volume of names, numbers and other details is overwhelming, and it can be difficult to know if the effort is making any progress. To prove the value to company leaders and to measure the success of the data quality undertaking, companies need to develop benchmarks along the way.

For a company that has adopted address validation software or some other data quality solution, consider sending out employee surveys asking how their jobs have changed since they started - are they spending less time searching for files? Have their interactions with customers gone more smoothly? Hard numbers on productivity improvements are exactly the kind of information that chief executives want to hear.

Writing for Data Roundtable, Dylan Jones explains that in order to get people over to your side, you need to appeal to their values and priorities. Even when armed with an arsenal of facts and figures, your argument could end up falling on deaf ears. He advises creating a real-world analogy - bad data costs the same as employee sick days, for example - to make your managers care about data quality.

In an interview with Data Quality Pro, Jim Orr explains how not measuring data quality and the results of governance efforts - which involve list management, policy enforcement and cleansing programs - can make the outcome of a DQ project short-lived.

"If a program cannot demonstrate business value then it either goes dormant or goes away altogether," Orr says, pointing to data quality as one example. "Organizations around the world have data quality solutions implemented in a single project but struggle to expand their data quality footprint beyond the first implementation. The reason, because they never measure performance against business outcomes."

He adds that the problem is also endemic to data governance programs, in which employees have a hard time putting performance improvements in economic terms or comparing them to business outcomes. Without showing the economic value that comes from these initiatives, business leaders are unlikely to continue signing budgets for data quality and governance programs.

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