I have blogged on numerous occasions about data quality issues relating to infrastructure data at utiltiies including geolocational accuracy, most recently here.  

There is an interesting compilation of statistics in an InsightSquared infographic relating to the cost  of poor data quality in general, not just for utilities.

• The cost of bad or dirty data exceeds $600 billion for US businesses annually.
• Poor data across businesses and the government costs the US economy $3.1 trillion a year.

But it’s worthwhile remembering David Loshin’s (The Practitioner’s Guide to Data Quality Improvement) perspective about the statistics relating to data quality.  He cited some widely known statistics available in 2011

• Tom Redman’s 1998 article in Communications of the ACM said he was “aware of three proprietary studies that yielded estimates in the 8–12% of revenue range.”
• In 1999, Larry English suggested that “Based on numerous cost analyses, the typical organization may see from 15 to 25 percent of its revenue go to pay the costs of information scrap and rework.”
• In 2002, Tom Redman claimed that “Poor data quality costs the typical company up to twenty percent of revenue.”
• IN 2003, TDWI produced a report estimating that “that data quality problems cost U.S. businesses more than $600 billion a year.”
• More recently Larry English said “Poor information quality costs organizations 20-35% of operating revenue wasted in recovery from process failure and information scrap and rework.”

Loshin concluded that

1. There are few (if any) published papers on actual case studies providing tangible details about the cost of poor data quality.
2.  Academic notes and books base their numbers on estimates, “proprietary studies,” accumulations from survey responses, or extrapolation from other estimates of the “cost of quality.”
3. In the absence of tangible evidence of actual costs, according to the experts the costs seem to be rising, from a low of 8% of revenue in 1998 to 20-35% of operating revenue in 2009.  I’m not sure if that means that the cost of poor data quality is rising or that our understanding of the impact of poor data quality has expanded.

Some other interesting items in the InsightSquared infographic

• Data quality best practices boosts revenue by 66 %.
• If the
  median Fortune 1000 company were to increase the usability of its data
  by 10%, company revenue would be expected to increase by $2.02 billion.