Three data warehouse project management metrics |
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EXPERT RESPONSE FROM: William McKnight
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QUESTION POSED ON: 25 August 2008
I'm in charge of a data warehouse project. Can you please help me with the metrics that should be put in place for this project? I'm looking for data quality metrics, as well as general metrics.
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There are several data warehouse project management metrics worth considering. I'll discuss the top three.
Business return on investment (ROI)
The best metric to use is business return on investment. Is the business achieving bottom line success (increased sales or decreased expenses) through the use of the data warehouse? This focus will encourage the development team to work backwards to do the right things day in and day out for the ultimate arbiter of success -- the bottom line.
Data usage
The second best metric is data usage.You want to see the data warehouse used for its intended purposes by the target users. The objective here is increasing numbers of users and complexity of usage. With this focus, user statistics such as logins and query bands are tracked.
Data gathering and availability
The third best data warehouse metric category is data gathering and availability. Under this focus, the data warehouse team becomes an internal data brokerage, serving up data for the organization's consumption. Success is measured in the availability of the data more or less according to a service level agreement. I would encourage you to use these business metrics to gauge your success.
Other, less important data warehouse project management metrics are technical performance indicators like up time, cycle end times, successful loads and clean data levels. Speaking of clean data levels, I have a full white paper about measuring and improving data quality metrics for data warehouses -- email me for more information. In short, you want to eliminate intolerable defects – as defined by the data stewards. These defects come in 10 different categories: referential integrity, uniqueness/deduplication, cardinality, subtype/supertype constructs, value domains/bounds, formatting errors, contingency conditions, calculations, correctness and conformance to "clean" set of values.
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