The best place to start is at the beginning. Are you undertaking an effort to build a solution that involves reporting, analysis, mining, or running rules against your enterprise data? Before designing a data warehouse, data mart, or operational data store, take some time to really understand your business needs.

A recent book by a Yale University econometrician, Ian Ayers, looks at a new trend in organizations, changing the decision-making process from one based on expertise and intuition to a data-based effort. This change is possible due to an almost inexhaustible supply of data on every topic, gathered from many sources and made available by the development of huge databases and the tools to manipulate the data in a variety of ways.

In my last article, I described extreme scoping, an agile project management approach for building DW and BI applications. I mentioned that this new development approach requires a new dynamic project team structure, which is the topic of my article in this issue. But before we explore the new team structure, let’s review how teams are organized traditionally.

We have been discussing the issue of accuracy as it pertains to the quality of the information that is being used. There have been many books and articles that have focused on that information characteristic. I would like to now begin our discussion of the other characteristics that the researchers at MIT and other universities associated with the Total Data Quality Management program at MIT have also identified as contributing to the overall quality of the information used within organizations.

The state of your enterprise information depends on its data quality and meta data. Poor quality data coupled with incorrect interpretation and use of information from an enterprise application is a recipe for failure, since it extinguishes all confidence with the organization’s consumers, your users. The consequences can be poor customer service, inept business processes, shipping or invoicing errors, lack of compliance, penalties from regulatory reporting issues and many others.