Many companies are discovering a problem when they attempt to integrate separate systems into an enterprise view of data – poor data quality. This discovery often leads to the development of a data governance program to create a “single version of the truth” and to present clean and reliable data to all who use it within the organization, and, where appropriate, to customers and other consumers of data external to the organization.

In the previous article we discussed the data warehouse marketplace from the perspective of history and vendors. The discussion continues with ETL products.

In the early ‘80’s, there was little interest in the idea of Enterprise Engineering or Enterprise Modeling and the use of formalisms and models was generally limited to some aspects of application development within the Information Systems community. The subject of architecture was acknowledged at that time, however, there was little definition to support the concept. This lack of definition precipitated the initial investigation that ultimately resulted in the “Framework for Information Systems Architecture.”

In my last article, I explained how I chose my column name: Back to Basics. As an example, I mentioned how amazed I was to discover that many companies don’t have a data strategy; something I consider to be very basic. I am equally astounded that many companies don’t require a methodology for project planning or application development.

In last month’s column I discussed the importance of data governance for the Enterprise Information Management (EIM) program. In this article I will discuss the first tasks that the data governance team must address and the typical data stewardship activities that they will be involved in.

Information quality can have many different definitions. If you listen carefully to people describing issues they have with data that they use, you hear them talk about inaccurate data, data that is not relevant, data that is not timely, as well as having too much information. The work done as part of the Massachusetts Institute of Technology (MIT) research concerning data quality conducted by Richard Wang, Yang Lee, Diane Strong, and Leo Pipino indicates that one can identify 16 characteristics that impact the overall quality of the information people are expected to use in fulfilling their job and task responsibilities.

Many organizations struggle to obtain the full benefits of their EIM initiative by approaching the framework components as a set of projects and not as an interdependent program. This type of approach leads to a series of enterprise initiatives without a cohesive information strategy or goals. In this type of method, the EIM Framework component projects fail to leverage the co-dependency and benefits required to build an efficient and agile data management organization with enhanced capabilities for information creation, capture, distribution, and consumption.