Data modeling techniques for data mining IBM by Chuck Ballard, Dirk Herreman, Don Schau, Rhonda

By Chuck Ballard, Dirk Herreman, Don Schau, Rhonda Bell,
Eunsaeng Kim, Ann Valencic

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Data warehousing has become generally accepted as the best approach for providing an integrated, consistent source of data for use in data analysis and business decision making. However, data warehousing can present complex issues and require significant time and resources to implement. This is especially true when implementing on a corporatewide basis. To receive benefits faster, the implementation approach of choice has become bottom up with data marts. Implementing in these small increments of small scope provides a larger return-on-investment in a short amount of time.

24 Data Modeling Techniques for Data Warehousing Reconciled data is seldom explicitly defined. It is usually a logical result of derivation operations. Sometimes reconciled data is stored only as temporary files that are required to transform operational data for consistency. 2 Enterprise Data Model An EDM is a consistent definition of all of the data elements common to the business, from a high-level business view to a generic logical data design. It includes links to the physical data designs of individual applications.

They are typically unwilling to wait for a more global infrastructure to be put in place. Chapter 4. 2 Bottom Up Implementation A bottom up implementation involves the planning and designing of data marts without waiting for a more global infrastructure to be put in place. This does not mean that a more global infrastructure will not be developed; it will be built incrementally as initial data mart implementations expand. This approach is more widely accepted today than the top down approach because immediate results from the data marts can be realized and used as justification for expanding to a more global implementation.

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