Enterprise Knowledge Management: the Data Quality Approach (Book Review)

While data quality problems are widespread, it is rare for an event to take place that provides a high-profile example of how questionable information quality can have a worldwide business effect. The 2000 US Presidential election and the subsequent confusion around the Florida recount highlights the business need for high quality data. The 2000 election illustrated at least three types of potential data quality issues. A poor data presentation (in the form of the butterfly ballot) led to voter confusion in Palm Beach County; a poorly defined data validation rule resulted in disputes about how or whether to count punch-card ballots with hanging chads; and finally an invalid analytical model based on previous elections caused the Voter News Service to incorrectly predict the winner of the Florida not once, but twice. Data quality seems to be a hazy concept, but the lack of data quality severely hampers the ability of organizations to effectively accumulate and manage enterprise-wide knowledge. The text attempts to demonstrate that data quality is not an abstract notion, but rather one that can be quantified, meaured, and imporved, all with a focus on return on investment.