The narrow definition of data quality is that it's about bad data - data that is missing or incorrect. A broader definition is that data quality is achieved when a business uses data that is comprehensive, consistent, relevant and timely. If you focus only on the narrow data definition you may be lulled into a false security when, in fact, your efforts fall short. We will address several more misconceptions about data quality.
In order to fix a problem you have to recognize you have a problem. According to recent Gartner research, 25 percent of Fortune 1000 companies are working with poor quality data. The Data Warehousing Institute (TDWI) estimated that data quality problems cost U.S. businesses $600 billion each year. Regulatory initiatives such as Sarbanes-Oxley and Basel II dictate that companies must provide transparent data. But even with the documented high costs of poor data quality and the tight regulatory environment, many companies are turning a blind eye to their data quality problems. Why? Perhaps it is because of their mistaken belief that bad data is the only data quality issue they need to worry about.
A corollary to the above: to fix a problem you first have to take responsibility for it. That's the rub. Taking responsibility is the biggest roadblock to dealing with data quality. In order to achieve a high level of quality, data has to be viewed from an enterprise and holistic perspective. Data may be correct within each data silo, but the information will not be consistent, relevant or timely when viewed across the enterprise. To make matters worse, you've got each report or analysis interpreting the data differently, so even when the numbers start off the same in each silo, the end results will not be consistent. Data is a corporate asset and has to be consistent across the entire corporation, not just within the business function or division where it originated.
Misconception #1: You Can Fix Data