Dirty Data Blights the Bottom Line

Computerworld - When Nancy Rybeck was hired by Emerson Process Management six years ago, she was charged with salvaging a data warehouse that had been built to help the company better analyze customer activity. But after a thorough review, she opted to scrap it and start over. The warehouse, it seemed, was loaded with redundant and inaccurate data.

"The biggest reason [the earlier effort] had failed was data quality," says Rybeck, data warehouse architect at Austin-based Emerson Process Management, a global supplier of measurement, analytical and monitoring instrumentation and services. One major contributor to the failure was an assumption made by the group that launched the initial Microsoft Access-based effort: that sales entities all over the world would enter customer names and addresses in the same manner, regardless of whether they operated in the Asia-Pacific region, Europe or other areas in which Emerson does business. Cultural differences, combined with complications caused by Emerson's continuing growth through acquisition, resulted in numerous ways of entering quote, billing, shipping and other key data.

Image Credit: Andrew Skwish

Although many businesses tend to think that data quality primarily affects customer-facing initiatives, the impact can be more profound on internal operations. "CRM initiatives fail, and companies get into trouble with the security and privacy of customer data," says Gartner analyst Ted Friedman. "But the big money being lost [because of poor data quality] is in internal operations."

Inaccurate financial reporting, uncollected receivables, overpayments, poor product specifications, excess inventory—the problems caused by inaccurate data are endless, and they all affect the bottom line.

Meanwhile, mounting regulatory compliance requirements dictate increased data vigilance. "You can have all the controls in place, but if your data's not accurate, your CFO will be signing off on inaccurate information," says Robert Lerner, an analyst at Current Analysis Inc.

Data quality initiatives have long languished in the shadow of sexier projects. But thanks to failed CRM and ERP efforts, compliance violations, costly supply chain inefficiencies and more, that's starting to change. Investments in data quality suites are growing at a rate between 12% and 15% annually, according to Gartner, and the market is starting to consolidate as it matures.

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Tools that address data quality fall into a variety of categories, including data profiling software, which sifts data fields for duplication, missing information and other errors; data cleansing and matching tools, which parse data into discrete elements, clean it, standardize it in formats, and match and merge records; data enhancement tools, which enrich data by incorporating, for instance, third-party elements; and data monitoring tools, which ensure that data maintains a preset level of quality.