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Data Quality

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Data Quality

  1. 1. A critical business issue Saturday, May 16, 2009
  2. 2. Information Value $0 Data Quality Saturday, May 16, 2009
  3. 3.  Poor quality customer data costs U.S. companies $611 billion annually in postage, printing and staff expense. - The Data Warehouse Institute  The cost of poor data quality can reach as high as 15% to 25% of operating profit. - The Data Warehouse Institute  At least 25% of critical data within Fortune 1000 companies will be inaccurate. - Gartner Saturday, May 16, 2009
  4. 4.  Billing and payment errors cause negative customer perceptions and affect a company’s ability to accurately state their financials  Operating expenses are inflated due to returned mail, and the manual rework to get it sent correctly  Regulatory fines are levied due to inaccurate reporting of data to government entities Saturday, May 16, 2009
  5. 5.  Customers (and revenue) are lost due to an inability to track customer interactions or to recognize high-value customers  Negative publicity is generated when a company fails to meet customer obligations on a large scale, like a disruption of service  Flawed analytics lead to poor tactical and strategic decisions Saturday, May 16, 2009
  6. 6.  Extra time is required on IT projects to reconcile data  Delays are incurred in deploying new systems  Credibility in a system or application is lost when it doesn’t perform as advertised Saturday, May 16, 2009
  7. 7.  Completeness – is all relevant data entered?  Consistency – is the data entered in the same format?  Accuracy – is the entered data correct?  Relevance – is the data collected useful?  Timeliness – is the data available when needed?  Integrity – is the data consistent when duplicated? Saturday, May 16, 2009
  8. 8. Saturday, May 16, 2009
  9. 9.  Assess  Improve  Data Cleansing  Prevent Data Quality Deterioration  Recognize Data Imperfections  Monitor Saturday, May 16, 2009
  10. 10. Claims Data Underwriting Data  Many data quality edits  Several underwriting and rules built into and policy admin CWS systems with various  Data Quality levels of DQ edits Scorecard in place;  No data quality most quality is good scorecard in place  Targeted projects as  Some data clean-up needed to cleanse associated with other data projects  No metadata for data  No metadata for data exceptions exceptions Saturday, May 16, 2009
  11. 11.  Tasks  Begin assessment of underwriting data  Document data quality rules  Begin data cleansing as possible Challenges  No dedicated business resources  Limited IT resources  No automated cleansing tools Saturday, May 16, 2009
  12. 12.  Tasks  Review and update the Corporate Data Strategy  Analyze/evaluate resources needed to support a more comprehensive enterprise data quality program  Secure support for enterprise data quality program  Challenges  Competing business priorities  Limited corporate resources Saturday, May 16, 2009
  13. 13. Saturday, May 16, 2009

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