Business analytics, and thus business intelligence efforts, are dependent upon data. If there are no data, there are no business analytics. If there are no business analytics, then we cannot exploit the edge of understanding the business, its performance, and its context, which in turn means we cannot improve our decision making. All of that suggests that the performance of our corporation will not be up to its potential. In fact, in todayās competitive world, it may mean that the organization may no longer exist.
Hence, before we can talk about how to make models more understandable or how to project the appropriate information to the screen, it is critical to discuss how to know what data need to be included in the DSS. Before we can do that, we need to define data and its associate, information.
Data are things known or assumed. The term generally refers to facts and/or figures from which conclusions can be drawn. For example, the raw counts of walnut consumption and cholesterol levels discussed in Chapter 2 represent data. Similarly, the cost of commercial time and the distribution of viewing audiences of television programs represent data to those making marketing plan choices. Details about shipping procedures, cost, and reliability of various haulers represent data relevant to the development of a logistics plan.
However, these are not the only kinds of details that might be considered data for the purposes of DSS. When making choices, some decision makers value the opinions of trusted colleagues. For example, when purchasing managers consider new, unknown vendors, they often seek opinions regarding service and reliability from colleagues at other corporations who have purchased from those vendors. They would not use these opinions solely but would use them to enrich a cost model developed from more objective data. Similarly, when developing a long-range plan, a CEO enlists knowledgeable subordinates to gauge the expected changes in regulations, governments, vendors, competitors, and clients over a 20-year period. These opinions are melded with quantitative models, which alone do not provide reliable long-range forecasts, as the basis of a long-range estimate of the companyās needs. In each of these cases, opinions and judgments are used as inputs to a choice process. They supplement standard āobjectiveā data to represent aspects of the choice that would otherwise be lacking. Since the DSS is intended to support the choice process, it must accommodate such subjective data and opinions and provide efficient ways of searching for and using these data.
For other decisions, decision makers might need data that are not stored in conventional ways. For example, decision makers considering the choice of textiles for the manufacture of furniture believe the support provided by pictures is superior to that provided by verbal descriptions of the colors, patterns, and textures. Images supplement data such as price, vendor, or shrinkage that would be accessed in a standard fashion. Decision makers considering a large-scale disaster relief plan might need a video of the affected area to assess the problems and needs of an area fully. Such a video needs supplementary geographical information systems support to assess land use, damage estimates, and population statistics for each affected area. Or, a symphony music director might find it beneficial to have audio files of possible selections to help select a balanced and appealing program. With the audio data, the music director might combine data, including programs in which the piece has been used, audience size, reviews, and comments, to develop models that maximize the number of new compositions played by an orchestra while still being sensitive to the expected composition of the audience, thus pairing new selections and established favorites in a pleasing fashion.
With virtual-reality technology, decision makers might also access āexperiencesā before they select alternatives. For example, city planners might make use of virtual reality in positioning new buildings or green spaces, including the evaluation of the aesthetics and access. Similarly, fashion collections can be modeled using virtual reality (replicating the variety of poses and settings that might happen at actual fashion shows) in order to get a fast opinion of designers and/or customers prior to their announcement. Or, a logistics planner could use virtual reality to evaluate space needs, safety issues, or production principles.
One of the purposes of the DSS is to transform these data into information that can help the decision maker. While data represent things known or assumed, information refers to processed data or āacquired knowledge.ā Processing can be a summarization (either numerical or graphical) or the output from one or more models. For example, scores on an exam in a particular class represent data; each score represents performance by the corresponding individual. However, they do not represent information. This list does not help you, as an individual student, decide how to respond to your performance on the exam. Once the data are processed, however, they do support your decision. With a computation of class mean and standard deviation or the identification of cutoffs associated with each letter grade, students can decide whether they performed at a personally acceptable level, whether they should study harder, and whether they should drop the class.
In the simplest terms, if the data are not in and of themselves information, or if the data cannot be transformed into information, then they should not be included in the database. As you can imagine, this leaves a great deal of ambiguous latitude. Returning to basics reminds us that the goal of business intelligence is to study historical patterns and performance so as to predict the future and improve the organizationās response to future events. That means that the data need to represent practical indicators of what is happening in the organization, indicators of when changes occur, and indicators of when and how actions need to be taken. The data need to reflect historical, current and predictive views of the organization and its environment.
There are three approaches to operationalization of the description. The first is to take a normative approach to the information needs: What information should the decision maker want to make this type of decision? This assumes that which meets the standard guidelines for making a particular decision will be useful in a given decisionmaking situation. It is the material taught in business administration cour...
