Subject-Oriented Data
In operational systems. We store data by individual applications. In the data sets for an order processing application, we keep the data for that particular application. These data sets provide the data for a function for entering orders, checking stock, verifying customer’s credit. And assigning die order for shipment. But these data sets contain only the data that is needed for those functions relating to this particular application. We will have some data sets containing data about individual orders, customers. Stock status, and de-tailed transactions, but all of these are structured around the processing of data.
Similarly. For banking, institution, data sets lot a consumer loans application contains data for that particular application. Data sets for other distinct applications of checking. Accounts and savings accounts relate to those specific applications. Again, in an insurance company, different data sets support individual appikailo115 such as automobile insurance, life insurance. and workers' compensation insurance.
In every industry, data sets are organized around individual applications to support those particular operational systems. These individual data sets have to provide data for the specific applications to perform the specific functions efficiently. Therefore, the data sets for each application need to be organized around that specific application.
In operational systems. We store data by individual applications. In the data sets for an order processing application, we keep the data for that particular application. These data sets provide the data for a function for entering orders, checking stock, verifying customer’s credit. And assigning die order for shipment. But these data sets contain only the data that is needed for those functions relating to this particular application. We will have some data sets containing data about individual orders, customers. Stock status, and de-tailed transactions, but all of these are structured around the processing of data.
Similarly. For banking, institution, data sets lot a consumer loans application contains data for that particular application. Data sets for other distinct applications of checking. Accounts and savings accounts relate to those specific applications. Again, in an insurance company, different data sets support individual appikailo115 such as automobile insurance, life insurance. and workers' compensation insurance.
In every industry, data sets are organized around individual applications to support those particular operational systems. These individual data sets have to provide data for the specific applications to perform the specific functions efficiently. Therefore, the data sets for each application need to be organized around that specific application.
In striking contrast, in the data warehouse, data is stored by subjects, not by applications. If data is stored by business subjects, what arc business subjects'? Business subjects differ from enterprise to enterprise. These are the subjects critical for the enterprise. For a manufacturing company, sales, shipments, and inventory are critical business subjects. For a retail store, sales at the check-out counter are a critical subject.
Figure 2-I distinguishes between how data is stored in operational systems and in the data warehouse. In the operational systems shown, data for each application is organized separately by application: order processing, consumer loans. Costumer billing, accounts receivable, claims processing, and savings accounts. For example. Claim is a critical business subject for an insurance company. Claims under automobile insurance policies are processed in the Auto Insurance application. Claims data for automobile insurance is organized in that application. Similarly, claims data for workers' compensation insurance is organized in the Workers' Comp Insurance application. But in the data warehouse for an insurance company, claims data are organized around the subject of claims and not by individual applications of Auto Insurance Workers' comp.
