Ask a group of technology executives about the level of quality of their organization’s data and the consensus is likely to be: “It’s lousy.” This is confirmed by a recent Gartner study estimating that 25% of all enterprise data was of bad quality.
“The better the quality of the data, the better a business will run,” said Gartner’s Ted Friedman, an author of the study. Other experts agree.
“If, say, information about customers and prospects is incorrect, then not only are you going to lose opportunities, but you’ll be making bad decisions,” said Ruth Stevens, president of e-Marketing Strategy, a customer acquisition and retention consultancy.
“Most companies either don’t realize the pain they’re in (regarding poor data quality) or they’ve just learned to live with it,” added Shawn O’Rourke, CIO of NCCI Holdings, an insurance services company.
Friedman, O’Rourke and Stevens agree that one major problem is there are a lot of factors contributing to bad data quality. They also agree that, in many enterprises, executives — particularly non-technology executives — aren’t even aware their data quality is poor.
“When companies are doing well, they don’t focus on those things,” O’Rourke said. “But that’s the time you have to go into continuous improvement mode so you can do better.”
That means technology executives face three tasks: understanding the problem of poor data quality, convincing non-technical executives about the severity of the problem, and fixing it.
The Nature of the Beast
Data quality can degrade for a number of reasons, Friedman said. First, data might not exist or it doesn’t exist everyplace it’s needed, he said. Similarly, the data can be valid in some domains but not in others. Or, perhaps it can’t be delivered to the right users at the right time. And, of course, there are the well-known problems of inconsistency and inaccuracy.
“Basic data entry errors are one way of creating bad data,” Friedman said. “The human factor around data is a big driver for many data quality problems.” Unfortunately, he added, other, more complicated factors, also typically come in to play.
“As data flows through the enterprise, it’s like anything — it ages and degrades over time.”
Stevens claims that some classic examples of the effects of bad data quality pertain to her area of specialization: data used for sales and marketing.