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Chapter 20. Using Access with Microsoft ... > In the Real World—OLAP and PivotTabl...

In the Real World—OLAP and PivotTables

Data warehouses and their smaller sibling, data marts, introduce a new database lexicon and an alternative approach to analyzing data. Traditional relational database structures comprise a two-dimensional set of columns and rows, analogous to a spreadsheet. Conventional aggregation techniques, typified by Jet crosstab queries, enable managers to review summary data. Front ends for data analysis usually provide multiple dropdown lists to choose attributes (tables and query criteria), such as products, employees, regions, and time periods. Excel PivotTables and PivotCharts are ideal for presenting two-dimensional crosstab query result sets.

The primary objective of data warehouses and marts is to provide a central source of decision-support information, available to all who need it, that lends itself to unstructured analysis by users. Unstructured analysis lets users, not database developers and administrators determine views of the data. One of the benefits of letting the user specify his or her own view of data is the ability to perform heuristic analysis. Heuristic analysis begins with an ad hoc question, the answer to which intuitively leads to another question, and so on. Online analytic processing (OLAP) almost universally involves interactively querying of data in a heuristic function. Developers and database administrators are very unlikely to be able to predefine queries that satisfy all requirements of all users, because the path of an analysis often evolves by serendipitous observation of interesting relationships between successive views of data. PivotTables currently are one of the best methods of enabling heuristic analysis.


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