Building the Data Warehouse

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Different environments have different time horizons. A time horizon is the parameters of time represented in an environment. The collective time horizon for the data found inside a data warehouse is significantly longer than that of operational systems. A 60-to-90-day time horizon is normal for operational systems; a 5-to-10-year time horizon is normal for the data warehouse. As a result of this difference in time horizons, the data warehouse contains much more history than any other environment.


Operational databases contain current-value data-data whose accuracy is valid as of the moment of access. For example, a bank knows how much money a customer has on deposit at any moment in time. Or an insurance company knows what policies are in force at any moment in time. As such, current-value data can be updated as business conditions change. The bank balance is changed when the customer makes a deposit. The insurance coverage is


time variancy


operational    data warehouse



•    time horizon—current to 60-90 days


•    update of records


•    key structure may/may not contain an element of time


Figure 2.4 The issue of time variancy.



•    time horizon—5-10 years


•    sophisticated snapshots of data


•    key structure contains an element of time


changed when a customer lets a policy lapse. Data warehouse data is very unlike current-value data, however. Data warehouse data is nothing more than a sophisticated series of snapshots, each taken at one moment in time. The effect created by the series of snapshots is that the data warehouse has a historical sequence of activities and events, something not at all apparent in a current-value environment where only the most current value can be found.

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