Decision Support Systems (DDS), Executive Information Systems (EIS) and query/report writing tools are used to produce reports about data, usually aggregating it through any number of dimensions. Another use of these tools is to detect trends and patterns in customer dat~ that will help answer some questions about the business. When used in this mode, a query is created to access the records relevant to the question(s) being formulated. After the data is retrieved, it is examined to detect the existence of patterns or other useful information that can be used in answering the original question(s). We call this the Verification Model. In this mode, the user of a DSS generates a hypothesis about the data, issues a query against the data and examines the results of the query looking for affirmation or negation of the hypothesis. In the first case, the
process ends. In the latter case, a new query is refcrmulated and the process iterates until the resulting data either verifies the hypothesis or the user decides that the hypothesis is not valid for his data.
Consider the following example: A sales executive has a limited budget to do a mailing campaign for a new product. In order to optimize the use of this money,the marketing executive wants to identify the largest set of people who are the most likely candidates to buy the new product and can be reached within the budget limitation. To identify these customers and to verify that the customer set has been adequately narrowed to match the available promotional budget, the executive makes a hypothesis about the potential customer set. Issuing a query against the databases that contain historical data about customer purchases and demographic information respectively, the set of customers that have made significant purchases of competitive products can be obtained. Further more, to limit the number of customers found to a reasonable number, the executive requests to only get information about these customers that are characterized by having ages between 30 and 45 years, being heads of household with combined incomes between Rs. 50,000 and Rs. 1,00,000 and living in any of the four metros - Delhi, Bombay, Calcutta and Madras. If the result of this query returns a number of customers that match the available budget for mailing promotions, the process ends. However, if either significantly more (or less) customers are found than the number that can be reached with the given budget, a new query limiting (or expanding) the set of customer address requested must be issued.
In the above example, the hypotheses used in formulating the queries were quite explicit (e.g. incomes between certain amounts). Even when the hypotheses are implicit, the process of finding useful trends or patterns using queries can be described by the above behavior, as shown in the following exa'!1ple involving a query drill-down process.
Queries, such as those used in the previous two examples, always return records that satisfy the query predicates. Thus, little new information is created in this retrieval process: either the hypothesis is verified or it is negated. The user does the process of information finding by successive iterations upon examining the results of query after query and linking the verified and refined hypotheses. This is the essence of a Verification Model.