Over the past few years, I’ve had the opportunity to discuss enterprise metadata to a wide variety of audiences and much of this conversation is captured in this “Best Practices” implementation framework. The model has evolved over the past few years as our program continues to do the same. Of course, this summary can only be a few pages long so the depth of the content here will be a tad shallow but you should be able to get the basics from the diagram and the description that follows. Figure 1 provides the new framework and the content follows to describe each section.
Many errors and accidents are made/caused by misunderstandings of the meanings of terms used.
How many times have you been in a meeting when the words you heard being said did not match what you thought they were?
Many business decisions are made (and later regretted) due to a misunderstanding of the data, and what the data element used in a report is signifying. Some of these accidents and misunderstandings are large enough to be reported in the media. In prior papers I refer to the Mars Lander episode, where the unit of measure was assumed and not made explicit, miscalculations were made and the equipment was lost. Our businesses are filled with many such examples, although not as costly perhaps, are still quite impactful to the business.
A few months ago we ran a series of columns dedicated to defining each of the major disciplines of data integration: extract, transformation and load (ETL); enterprise application interchange (EAI); and enterprise information integration (EII). We also asked for input as to which method or methods of integration are in use, or planned to be used, in your organizations.