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Certified Business Intelligence Professional Business intelligence economics

Business intelligence economics

Economics refer to feasibility of the product or service. In business, profitability occurs when revenue exceeds expenses. Using the total cost of a product to calculate expenses gives you a more accurate picture of profitability. The total cost of a product takes into account a wide range of expenses, including all fixed and variable costs associated with producing the product.

Operational feasibility– a measure of how well a solution meets the system requirements.
Cultural (or political) feasibility- a measure of how well a solution will be accepted in an organizational climate.
Technical feasibility– a measure of the practicality of a technical solution and the availability of technical resources and expertise.
Schedule feasibility– a measure of how reasonable the project timetable is.
Economic feasibility- a measure of the cost-effectiveness of a project or solution.

Legal feasibility- a measure of how well a solution can be implemented within existing legal/contractual obligations.

Operational feasibility– a measure of how well a solution meets the system requirements.
Cultural (or political) feasibility- a measure of how well a solution will be accepted in an organizational climate.
Technical feasibility– a measure of the practicality of a technical solution and the availability of technical resources and expertise.
Schedule feasibility– a measure of how reasonable the project timetable is.
Economic feasibility- a measure of the cost-effectiveness of a project or solution.
Legal feasibility- a measure of how well a solution can be implemented within existing legal/contractual obligations.

Business Intelligence enables organizations to make well informed business decisions and thus can be the source of competitive advantages. This is especially true when you are able to extrapolate information from indicators in the external environment and make accurate forecasts about future trends or economic conditions. Once business intelligence is gathered effectively and used proactively you can make decisions that benefit your organization before the competition does.

The ultimate objective of business intelligence is to improve the timeliness and quality of information. Timely and good quality information is like having a crystal ball that can give you an indication of what's the best course to take. Business intelligence reveals to you:

The position of your firm as in comparison to its competitors Changes in customer behaviour and spending patterns The capabilities of your firm Market conditions, future trends, demographic and economic information The social, regulatory, and political environment What the other firms in the market are doing

You can then deduce from the information gathered what adjustments need to be made.

Businesses realize that in this very competitive, fast paced, and ever-changing business environment, a key competitive quality is how quickly they respond and adapt to change. Business intelligence enables them to use information gathered to quickly and constantly respond to changes.

Benefits of Business Intelligence

Business Intelligence provides many benefits to companies utilizing it. It can eliminate a lot of the guesswork within an organization, enhance communication among departments while coordinating activities, and enable companies to respond quickly to changes in financial conditions, customer preferences, and supply chain operations. Business Intelligence improves the overall performance of the company using it.

Information is often regarded as the second most important resource a company has (a company's most valuable assets are its people). So when a company can make decisions based on timely and accurate information, the company can improve its performance. Business Intelligence also expedites decision-making, as acting quickly and correctly on information before competing businesses do can often result in competitively superior performance. It can also improve customer experience, allowing for the timely and appropriate response to customer problems and priorities.

Factors Influencing Business Intelligence

Customers are the most critical aspect to a company's success. Without them a company cannot exist. So it is very important that you have information on their preferences. You must quickly adapt to their changing demands. Business Intelligence enables you to gather information on the trends in the marketplace and come up with innovative products or services in anticipation of customer's changing demands.

Competitors can be a huge hurdle on your way to success. Their objectives are the same as yours and that is to maximize profits and customer satisfaction. In order to be successful you must stay one step ahead of your competitors. In business you don't want to play the catch up game because you would have lost valuable market share. Business Intelligence tells you what actions your competitors are taking, so you can make better informed decisions.

Business Partners must possess the same strategic information you have so that there is no miscommunication that can lead to inefficiencies. For example it is common now for businesses to allow their suppliers to see their inventory levels, performance metrics, and other supply chain data in order to collaborate to improve supply chain management. With Business Intelligence you and your business partners can share the same information.

Economic Environment such as the state of the economy and other key economic indicators are important considerations when making business decisions. You don't want to roll out a new line of products during an economic recession. BI gives you information on the state of the economy so that you can make prudent decisions as to when is the right time to maybe expand or scale back your business operations.

Internal Operations are the day to day activities that go on in your business. You need an in depth knowledge about the internal workings of your business from top to bottom. If you make an arbitrary decision without knowing how your entire organization works it could have negative affects on your business. BI gives you information on how your entire organization works.


The difficulties in assessing the costs and benefits of information systems has long been an topic of interest for DSS researchers. Keen (1981) introduced the concept of Value Analysis (VA) as an alternative approach to traditional cost-benefit methods. VA is a methodology for planning and evaluating DSS proposals. Keen identifies the key issues as: (1) a reliance on prototypes; (2) the absence of cost-benefit analysis; (3) the evolutionary nature of DSS development; and (4) the nature of perceived benefits. Rather than reducing all variables into monetary terms, the VA approach acknowledges that the perceived benefits of a DSS are significant determinants in justifying investment in the system. Money  et al  (1988) noted the inadequacy of cost-benefit approaches to evaluating the effectiveness of decision support systems (DSS), and demonstrated the
use of Keen’s approach. Money  et al  (1988) justified the move away from cost based analysis to a focus on ‘value’ with two key points. First, they argued that the initial investment takes place as a research and development (R & D) project that is below the capital expenditure level (minimizing cost considerations); and secondly, they noted that the majority of DSS expenditure is relatively inexpensive, and therefore does not play a major role in the evaluation process. These points may not apply to modern BI. Many BI tools are large, expensive, and often invasive to the processes of the firm. Cost considerations are an important factor, even in the prototyping stages, as software license fees are often high. The VA method does however provide the ability to establish agreed values for outputs, which may otherwise be classed as intangible and ignored. Value analysis may prove a useful technique in BI evaluation, particularly when evolutionary development is used. Anecdotal evidence suggests that this method would work in a BI environment, some BI vendors are already offering to install their software free of charge, using real data, into prospective client sites in order to more clearly demonstrate benefits.

Tayyari and Kroll (1990) noted that there are numerous intangible benefits and costs associated with IT projects, and that assigning a monetary value to such benefits is ‘very difficult, even impossible’. They suggested that these benefits may be quantified by using surrogate or proxy indicators. For example, higher employee morale may be measured by the monetary values of its consequences (lower turnover or higher productivity). Unfortunately, this method provides little direction on how to go about choosing the proxy measures. Once the measures are chosen, it relies on proven financial calculations (such as return on investment (ROI) and net present value (NPV)) to determine their worth.

Anandarajan and Wen (1999) recognized that traditional accounting methods are inadequate for evaluating intangible benefits, resulting in their application only being appropriate for simple cost-benefit applications, not for evaluating complicated IT investments. They submit that many of the r ecent methods proposed to incorporate intangible benefits into the evaluation process are too obscure for use in industry. Their method aims to incorporate many intangible benefits through conducting discussions with employees from all divisions affected by the IT implementation. Through the use of a case study example, they demonstrated how many of the hidden costs may be quantified and incorporated into the decision-making process. The steps of the evaluation framework are: Step 1- Determining tangible and intangible benefits, Step 2- Determining the costs of different technologies, and Step 3- Identifying the net present values and risk. Whilst the method provides a straightforward process in which to measure and quantify intangible benefits, it does rely heavily on the opinion of the discussion participants, and could be adversely affected by bias and subjectivity.

The Quantification Technique (Hares & Royle, 1994) is a formal way of measuring intangible benefits. This technique is also known as “bridging the gap” and involves the following steps: (1) identifying the benefits; (2) making the benefits measurable; (3); predicting in physical terms; and (4) evaluating in cash flow terms. This approach requires a significant amount of judgment when performed, and thus the results are subjective and open to questioning. The identification of intangibles themselves will depend on the stakeholders involved. The third step of predicting in physical terms can be difficult, due to the large numbers of methods available to convert the measures into actual figures. The use of market surveys is the approach recommended by Hares and Royle, but it may not be applicable in many BI implementations.

Strassman (1990) developed the method of Return on Management (ROM), which is a performance measure based on the value added to the organization provided by management. This method assumes that the information costs of an organization are equal to the costs of managing the enterprise. The method is performed both pre and post IT implementation to obtain the technology’s contribution to the organization. Using a number of calculations, the ROM is management value-added divided by the total costs of managing the enterprise. This method is not without its problems, the difficulty in distinguishing between the operational costs and the management costs of an organization ha s been identified as a concern (Willcocks, 1992a, p. 262).
The suggestion is that ROM may only be an indirect measure of how effective management information is exploited. There are also concerns with its appeal to management and the usability of the approach. Negotiation and imputation are methods of evaluating intangible benefits according to Remenyi (2000). The method involves asking managers using a particular resource to place a value on it. For example, “would this report be worth $100 to you?”, if yes then they are asked “would it be worth $1,000 to you?” This binary search is continued until a value of the report is agreed on, and it is this value which may be considered the value of the intangible benefit. Although the method places an actual dollar value on the asset, Remenyi acknowledges that this method produces only subjective evaluations.  

The Information Economics (IE) approach (Parker &  Benson, 1998) builds on other traditional approaches, and is largely an investment feasibility framework (Willcocks, 2001, p 72). ‘Value’, rather than cost, is viewed as a combination of an enhanced return on investment, a business domain assessment, and a technology domain assessment. In order to assess the way technology contributes to business performance, the method classifies benefits into six classes: (1) return on investment (ROI); (2) strategic match; (3) competitive advantage; (4) management information; (5) competitive response; and (6) strategic IS architectures. The IE process is long and somewhat complex. In essence, the method first builds on traditional cost-benefit analysis and includes four other methods (value linking, value restructuring, value acceleration, and innovation
valuation) for establishing an ‘enhanced’ ROI figure. The process then goes further to incorporate business and technological domain assessments. The critics of this a pproach argue that it might  lack credibility with upper management as many of the measures are based on subjective scoring. The IE approach can be overly time consuming. Despite its comprehensiveness and ability  to be adapted to BI, W illcocks (1994) identified statistical problems with the scoring methods and how they are weighed.

Methods based on multi-objectives and multi-criteria (MOMC) attempt to create a measure of utility provided by a particular piece of IT within a business. In terms of their own preferences and opinion, users and stakeholders are required to evaluate the relative usefulness of different outcomes; they then rank those preferences by applying a weight to each. When many stakeholders are involved in the evaluation process, the preference that provides the highest aggregate utilty, or highest overall measure of satisfaction, is considered the most viable. This method is useful when applied to complex projects, particularly if there are large numbers of stakeholders. This method also accommodates intangible factors but does not however provide any input for a traditional ROI calculation. MOMC methods are still in their infancy, but have already spawned a great deal of interest amongst researchers (Sylla and Wen, 2002).