1. A critical business issue
Saturday, May 16, 2009

2. Information
Value
$0
Data Quality
Saturday, May 16, 2009

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.  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.  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.  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.  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. Saturday, May 16, 2009

9.  Assess
 Improve
 Data Cleansing
 Prevent Data Quality Deterioration
 Recognize Data Imperfections
 Monitor
Saturday, May 16, 2009

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.  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.  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. Saturday, May 16, 2009