S-Score: A New Tool for Standardizing Disparate Data Quality Measurments
Bob Gaede
Logistics Data Quality Manager
Intel Corporation
As companies pay more attention and resources to measuring and improving Data Quality, it is important to have the capability to score, summarize, and report quality improvements using standard formats and processes. Standardization not only makes the concept of measuring quality understandable, it also allows disparate measurements to be compared and combined at all levels of detail. Additionally, it provides the ability to look at quality in innovative ways for analysis and improvements.
The methodology described in this presentation uses concepts and tools used in other areas but utilized for the first time to create a standardized quality score, or “S Score”. This methodology has been tried and proven, and the results are significant. By sizing, standardizing, scoring, and summarizing quality results, dramatic improvements in Data Quality have been demonstrated across Intel’s supply network.
Comments (1)
Bob Gaede said
at 9:02 am on May 2, 2006
Summary of presentation:
-S-Score is a methodology for adding unlike or disparate types of Data Quality measures together into an easily understood summary
-S-Score is based on standard statistical principles that are familiar and comfortable (like your favorite jeans!)
-S-Score consists of 4 easy steps
1. Scrutinize - measure the quality of your data
2. Standardize - create a standard scoring curve
3. Symbolize - assign actual results a standard score
4. Summarize - roll scores together into a scorecard
-Real improvements in Data Quality can be made by using S-Score
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