Ch. 7
Building Data-Driven Decision Support Systems

What differs about operating and DSS Data structures? Let’s examine the extent and nature of differences in format and structure. Often operating data are stored in a relational database management system. These relational transaction systems have data structures called tables that have been highly normalized. The tables are normalized to avoid anomalies in the data when transactions like updating, adding records and deleting records occur. Normalization is the process of reducing a complex data structure into its simplest, most stable structure. The process involves removing redundant attributes, keys, and relationships from a conceptual data model.

In general, in an operating data storage or transaction system, both the software and the hardware are optimized to support transactions about the daily operations of a company. For example, each time an item is sold it must be recorded and accounted for in appropriate transaction tables. Also, related data like customer data and inventory data are updated in transaction processing systems. In order to provide effective and efficient update performance, transaction systems store data in many small tables, each with a minimum number of fields. Thus, a simple sales transaction might need to have data elements recorded in five or more different tables. For example, we may add or update records in an invoice table, an invoice line table, a discount table, a store table, and a department table.

Although this structural approach of creating many small tables is effective in a transaction database, it is not appropriate for DSS data. Queries will tend to be slow and in many cases tables will need to be joined to complete a query. For example, to create an invoice to mail to a customer from the operating database all of the tables may need to be joined. In a large database of transactions joining tables is time consuming and uses extensive system resources. Operating data are usually stored in many different tables and the stored data represents information about specific transactions. DSS data are generally stored in many fewer tables. It does not always include the details of each operating transaction. DSS data also includes transaction summaries. In general, the data are integrated from multiple operating databases and DSS data are sometimes aggregated and summarized in the database to support predefined decision support needs. Also, DSS data may have data redundancies in the data structures if that will speed up queries.

The different data components of a data warehouses are: metadata, current detail data, older detail data, lightly summarized data, and highly summarized data. Extensive normalization is not appropriate for DSS data and some normalization will actually reduce the processing efficiency of a Data-Driven DSS. Normalization is not needed because the data will not be changed once it is in the data base and hence anomalies can not occur.