Better data quality can prevent costly business mistakes

Many companies are launching into big data strategies with the hope of using unexpected correlations about customers, supply chains and transaction information as the foundation for their investments and decisions. As company leaders invest in these tactics, they must consider data quality measures as well. If they don't take appropriate action to ensure their content is cleansed, accurate and complete, the costs might be more than they expect.

Good data is crucial to catching problems before they snowball, according to James Wallace, co-founder of Qvinci Software, in a blog post for franchise news source Blue MauMau. Wallace explains that while it may be difficult at first to calculate the costs of data quality issues, the effects can be felt long after.

To illustrate the importance of having information that's correct and complete, Wallace describes a hypothetical scenario in which a 100-unit franchise loses approximately $3.5 million in net present value and is forced to shut down 10 percent of its locations because the company failed to keep track of important data, such as financial and operational information. Because the chain's units didn't update and share data recorded in Excel spreadsheets, decision makers failed to reduce labor expenses, expand product offerings and alter marketing tactics.

These types of data issues might be contributing to the rate of small business closures, which have risen from 565,745 in 2005 to an estimated 660,900 in 2009, according to the United States Small Business Administration.