Data Volatility and Data Dimensions
What about Data Volatility? Only two kinds of operations occur in a data warehouse or DSS database, loading of data and accessing data. We can add data in batches but there is no online updating and changing of data. So the DSS data is non-volatile. Operating data is volatile. Operating data changes when new transactions occur.
Why are Data Dimensions important? Having multiple dimensions is probably the most distinguishing characteristic of DSS data. From a managerís and a DSS analyst's point of view, DSS data are always related in many different ways. For example, when we analyze product sales to a specific customer during a given span of time, we are likely to ask multi-dimensional questions. We may want to ask, "How many products of type X were sold to customer Y during the most recent six months?" DSS data can and will be examined from multiple dimensions, for example, product, region, and year. The ability to analyze, extract, and present data as information in meaningful ways is one of the major differences between DSS data and transaction data. In contrast to DSS data, operational data has only one dimension.
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