Meta data has long been the Wednesday's child of information processing systems. From corporate failures with data dictionary in the 1970s to IBM's legendary failure with repository in the 1980s, meta data has presented the data processing community with a seemingly intractable problem. And as organizations grow large, the problems with meta data multiply. However, the problems associated with meta data present concomitant opportunities. Nowhere are the problems and opportunities with meta data more apparent than in the large-scale enterprise data warehouse environment that is centered around a terabyte-size data warehouse. Why Does Meta Data Have Problems? Why is it that meta data carries with it such burdensome problems? There are a multitude of reasons for the travails of meta data.

The first and probably most profound reason why meta data poses such a problem is that each unit of meta data is being stretched in opposing directions by two very strong forces. In any environment where there is a very hard and constant pull, there is inherent instability. In order to understand this dramatic pull of diametrically opposing forces against meta data, consider the diagram shown in Figure 1.

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