Measuring and maintaining data quality remains a major business hurdle for large businesses — those with annual revenues greater than $1 billion — according to a report released August 4 by Pitney Bowes Business Insight and Silver Creek Systems.
The report, entitled “The State of Data Quality Today,” is based on responses from companies across Europe and North America and across a variety of industries to a third-party survey. While many of the respondents indicated that good quality data was a major corporate asset, they also admitted that measuring and improving the quality of data across an enterprise continues to be a challenge. Only 37% of companies surveyed have a data quality initiative in place, and 17% have no plans at all to start a data quality initiative in the near future. Lack of interest from the C-suite was the most often-cited barrier to data quality initiatives.
“The effects of poor data quality may be hidden to the executives because people at the department level may furiously clean up their customer lists before a big mailing or maybe the cost of return mail for a major marketing campaign is seen as just part of doing business,” Dean Wiltshire, senior product analyst for data quality at Pitney Bowes Business Insight. “But in fact costs could be reduced significantly and more money could be applied to the actual program rather than on wasted postage. There’s a serious disconnect in information about data quality not getting up to the people that need to make decisions. Companies have not taken a hard look at their data and are just doing business as usual, which can be wasteful.”
Only 4% of respondents rated their company’s data quality as “excellent,” while a full third of respondents designated their data quality as “poor at best.” Non-standard, incorrect and incomplete data continue to plague two-thirds of respondents, and 71% reported that maintaining high quality product data is a particular challenge.
Data maintenance is becoming more complicated, too, as companies add components to their databases; 81% of respondents said that data quality was about more than just “name and address.”
“Address validation and name de-duplication is just a piece of the data quality solution,” Wiltshire said. “Location intelligence is also starting to resonate with the marketplace because once you have a valid address and accurate name you can locate where that person resides or works and understand the risk of the location or demographics of that location and start to pull in true insight to your customer base.”
