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 today's 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 ...

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