Evaluation Tools and Techniques
For many years, business professors have been discussing the issues surrounding financial evaluation of capital expenditure projects. The argument continues. Typical evaluation tools recommended are Return on Investment (ROI), Net Present Value (NPV), and discounted cash flow. These tools are closely tied to the capital budgeting process and they are intended to provide a rational allocation of capital. This is a laudable goal.
Because managers are asked to spend funds on a Decision Support Systems project, anticipated results and benefits should be quantified so that the requested expenditure can be evaluated in comparable units. But for a DSS project it is difficult to quantify the results and benefits. DSS analysts are basically making estimates and guesses. A financial analysis is especially difficult because the costs are uncertain and many of the benefits are qualitative and intangible.
A number of alternative tools are available for evaluating data warehouse projects. Incremental value analysis is an evaluation of "soft" benefits such as improving staff productivity, improving the speed of strategic actions, enhancing a company's competitive advantage, or improving access to data. Another alternative, the scoring approach, considers intangible benefits and other considerations that are not considered credible by analysts who only focus on financial criteria. A third alternative, the qualitative benefits scenario approach attempts to estimate what decision-making will be like when a proposed DSS is in place and hence speculate on how the company will benefit. All of these qualitative approaches have pluses and minuses, but each can be improved by understanding the upside and downside of a DSS project. Table 12.1 lists 6 different evaluation tools and techniques.
Table 12.1 – Summary of Evaluation Tools and Techniques
When choosing an evaluation method, you need to consider many questions, including: Which tools work best? What technique should I use for this specific DSS project? Should I use different techniques for Data-Driven DSS than Model-Driven DSS projects? Does the scope of the project (amount of dollars to be spent) influence the technique I should use? The next few paragraphs provide more details on the evaluation tools and assist in answering these questions.