Meta Data

     The first image most people have of the data warehouse is a large collection of  historical, integrated data. While that image is correct in many regards, there is another  very important element of the data warehouse that is vital - metadata.

      Metadata is data about data. Metadata has been around as long as there have been  programs and data that the programs operate on. Figure 1 shows metadata in a simple form.

     While metadata is not new, the role of metadata and its importance in the face of the data warehouse certainly is new. For years the information technology professional has worked in the same environment as metadata, but in many ways has paid little attention to metadata. The information professional has spent a life dedicated to process and functional analysis, user requirements, maintenance, architectures, and the like. The role of metadata has been passive at best in this milieu. 

    But metadata plays a very different role in data warehouse. Relegating metadata to a backwater, passive role in the data warehouse environment is to defeat the purpose of data warehouse. Metadata plays a very active and important part in the data warehouse environment.

     The reason why metadata plays such an important and active role in the data warehouse environment is apparent when contrasting the operational environment to the data warehouse environment insofar as the user community is concerned.

      It serve to identify the contents and location of data in the ware house metadata us bridge between the DWH and decision support system application. Meta data is needed to provide an unambiguous interpretation. Metadata provide a catalogue of data in the DHW and pointer to this data.  Meta data is used to building, maintaining, managing, and using DWH.

Meta Data repository should contain:

1.      A description of the structure of the DWH. This includes  ware house schemes, view, dimensions, hierarchies and derived data definition, data marts etc.

2.      Operational meta data such as data linkage, currency of data and monitoring information.

3.      Summarization processes which include dimension definition. Data on granularity partitions, summary measure etc.

4.      Detail of data source which includes source databases and their content, gateway description, a data partitions, data extractions etc.

5.      Data related to system performance.

6.      Business meta data, which includes business terms and definition, data owner ship information and changing policies.