Last month we continued our discussion by approaching the topic of the characteristics of data that impact its overall quality. This month we will look at the characteristic that is considered to be the foundation for good data quality

Many government agencies and corporations are currently examining the meta data management tools in the marketplace to decide which of these tools, if any, meet the requirements for their meta data management solutions.

Running a data warehouse without the advantage of metrics is like trying to navigate a ship without a chart, compass, or sextant. Without metrics, we have no way of knowing if we have delivered a data warehouse that anyone could consider to be successful.

I have had the opportunity to work in data management for large corporations most of my career. I have thoroughly enjoyed learning about all of the different aspects of business and how businesses can understand more about their products and services from analyzing their data. Industries such as travel, transportation, retail, finance, and others. When I first started working in healthcare, it quickly became apparent there were many differences in the nature of the systems and data they produce.

Most data governance initiatives start as a result of either an event (discovery of data security breaches, development of a data warehouse, reaction to a poor data quality audit, etc.) or as part of an overall Enterprise Information Management program. The approach to governance frequently involves research into the topic, investigation of online sites for information, and possibly some discussion with one or more consultants. These are all fine ways to explore the discipline, but this list ignores one very useful approach that is gaining acceptance in the information management field: social networking.

In my last article “We Don’t Need No Stinkin’ Methodology,” I illustrated the need for using a methodology to manage projects and develop applications. My story also pointed out that traditional waterfall methodologies don’t work for data warehouse (DW) and business intelligence (BI) projects because they are much too rigid. It is not uncommon for seasoned project managers who use a traditional methodology on a DW or BI project to feel completely out of control. The requirements appear to be a “moving target,” communication between staff members takes too long; assigning tasks in a traditional way seems to result in too much rework; and so on. To top it all off, the business users are pressuring the project teams for quick deliverables (90 days or less) as they are still “fine-tuning” their requirements. As the project team scrambles to meet those user expectations, data standardization is skipped, testing is cut short, documentation is not done, and quality is compromised. Sound familiar? So, how can you “have the cake and eat it too?” Try an agile project management approach that I call “extreme scoping.”