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Book Contents

Ch. 7
Building Data-Driven Decision Support Systems

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An Inter-Connected Data-Driven DSS Architecture

Letís begin by examining the inter-connected elements in a Data-Driven DSS architecture. Design teams should begin development by researching Data Warehouses and Data-Driven DSS as an architectural template, and seek to understand a typical system's components, interfaces, how it fits into the typical organization, and what the typical reasons are for success or failure. After understanding data warehousing and Data-Driven DSS in general, developers should map the typical data and interfaces used onto their companyís specific situation. What subjects will be included? What questions will be asked by decision makers? At a minimum, we need to provide data structures for a data store, guidelines for a data extraction and filtering management tool, interfaces for a query tool, and some predefined charts and tables for use with a data analysis and presentation tool. Letís examine the components in more detail.

The data store component consists of one or more databases built using a relational database management system, a multi-dimensional database management system, or both types of systems. As we have noted, business data is extracted from operating databases and from external data sources. The external data sources provide data that cannot be found in company transaction systems but that are relevant to the business such as stock prices and market indicators. The data store is a compilation of many "snapshots" of a companyís financial, operating and business situation. When we create business data for the data store we summarize and arrange the operating data in structures that are optimized for analysis and rapid retrieval of data. The aging process inside a data store moves current detail data to older detail data based on when the data was loaded. This occurs when we do a batch update. In most situations only summarized data is indexed in the data store.

The data extraction and filtering component is used to extract and validate the data taken from the operational databases and the external data sources. For example, to determine the relative market share by selected product line the DSS requires data about competitors' products. Such data may be located in external databases provided by industry groups or by companies that market such data. As the name implies, this component extracts the data from various sources, filters the extracted data to select the relevant records, and formats the data so it can be added to the DSS data store component.

A data analyst or a manager can create the queries that access the DSS database using an end user query tool. We usually customize the interface for managers to make it easy to use. The query tool actually accesses the data store and retrieves requested data.

Finally, an end user analysis and presentation tool helps a manager perform calculations and select the most appropriate presentation format. For example, managers may want a pivot table summary report, a map, a pie or a bar chart. The query tool and the presentation tool are the front end to the DSS. Client/server technology enables these components to interact with other components to form a complete DSS architecture.

Once the software architecture is developed for a specific DSS that has been designed for a specific company and a specific purpose, we still face many challenges associated with implementing a new Data-Driven Decision Support System.

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