Q

Three data warehouse project management metrics

Data warehouse project management requires careful planning. So learn three data warehouse project management metrics that just might make things run smoother.

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.

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.

This was first published in August 2008

Dig deeper on Data warehouse project management

Pro+

Features

Enjoy the benefits of Pro+ membership, learn more and join.

Have a question for an expert?

Please add a title for your question

Get answers from a TechTarget expert on whatever's puzzling you.

You will be able to add details on the next page.

0 comments

Oldest 

Forgot Password?

No problem! Submit your e-mail address below. We'll send you an email containing your password.

Your password has been sent to:

-ADS BY GOOGLE

SearchBusinessAnalytics

SearchAWS

SearchContentManagement

SearchOracle

SearchSAP

SearchSOA

SearchSQLServer

Close