Will Rogers once said, "I never met-a-data I didn't like." Well, maybe it was a misquote. However, in a data warehouse architecture, there is much to like about meta data. Meta data is hard to define, but essential to life as we know it. Sort of like those old "better living through chemistry" commercials. So just what is meta data? The common definition for meta data is that it is data about other data. Some alternative definitions might be that meta data is:

Meta data is defined and used throughout the data warehouse--from data acquisition through ad hoc queries. The three most common uses of meta data in the data warehouse are in acquisition, transformation and access of data. First, meta data is used in data acquisition for identifying the data description map of the operational source files. Table layouts, acceptable values and field edits provide valuable analytical information and should be stored in a data dictionary. Second, in the transformation of data, meta data is integral to defining the rules of source data scrubbing prior to loading the data warehouse. Why is this important? One reason is that operational source systems seldom keep a longitudinal view of key information.

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