Global Research Institutes: An Insight on Data management

• According to The Data Warehousing Institute, U.S. businesses suffer losses, problems or costs of more than $600 billion per year due to poor quality data, and typical business between 10% & 25% of revenues.

  Source - Aug 2008 newsletter of ECCMA

• By 2012, “dirty data” will cause 50% of insurers to have compromised decision-making assumptions, despite the deployment of enhanced BI and analytic tools.

  Source - Gartner Inc. research report of Feb 2008

• A European Gartner BI survey of more than 600 BI users found that more than 35 percent identified data quality as a top-three BI problem facing their organization in the next 12-18 months, making it the second biggest challenge overall.

  Source - Gartner report of 22 January 2008 titled “Organizations Must Establish Data Stewardship Roles to Improve Data Quality”

• Although 92% of companies surveyed believe having an integrated view of customer data is either "critical" or "very important," only 2% have actually managed to achieve this.

  Source - Forrester Research report

• Research by Marketing Direct magazine indicates that direct marketers waste £195m in postage costs by sending incorrectly addressed international mail
• A survey by QAS found that 28 percent of the direct marketers admitted that their company rarely cleans its data or in some cases, not at all
• ₤ 88 M per year is spent mailing the deceased(!), £100m per year mailing people who have moved house
• Up to 67% of UK B2B mailings contain errors
• 37% of companies still do not have a data quality strategy in place
• 88% of all data integration projects fail because of poor quality data– Source
  http://www.dqglobal.com/, http://www.dataflux.com

According to Friedman, the more powerful strategic approach to data quality requires a more complex perspective: profiling, standardization, matching, and enrichment. And while customer data has always been and continues to be the primary focus, the Gartner report notes that organizations are increasingly looking to deploy data quality tools in other subject areas--in particular, product data and financial data.