Using Data for Efficiency and Effectiveness

Read this chapter for a discussion of how business organizations make decisions and the role information technology plays in decision making. What is the process that uses technology to make automated decisions? Consider system input, decision-making algorithms, and output. How does an organization track the decisions made by the system?

Decision making: systems view

A previous chapter introduced the idea of viewing an organization as a system that acquires inputs, processes them, and generates outputs. The organization interacts with its environment, in that it acquires inputs from the environment (e.g. purchasing parts from suppliers), and creates outputs that it hopes will be accepted by the environment (e.g. through sales of products to customers). The organization also receives feedback from the environment, in the form of customer compliments or complaints, etc. This way of perceiving an organization is typically referred to as the systems view, in that the organization is essentially viewed as a system operating within an environment.

As discussed in a previous chapter, it is also possible to break the organization into a series of smaller subsystems, or business processes. For example, we might view the purchasing function as a system that accepts inputs (e.g. materials requests from the production process), processes them (e.g. reviewing pricing and delivery details for a variety of suppliers), and generates outputs (e.g. purchase orders forwarded to specific suppliers). The purchasing process also receives feedback (details concerning orders received by the inventory control function, etc.).

For many organizations, these business processes (organizational subsystems) are supported by information systems. Before describing how this occurs, we need to define a few terms.

Data, information and knowledge

In a previous chapter, definitions and examples were provided to help differentiate between the terms data, information, and knowledge. As was discussed, the term data is generally used in reference to representation of raw facts. This might include mathematical symbols or text that is used to identify, describe or represent something, such as a temperature or a person. We should also note that this definition of data is considered by some people to be rather narrow; the term is sometimes also used to include images, audio (sound), and video. For the purposes of our discussion in this chapter, we will focus on the more narrow definition of data.

Again referring to a previous chapter, information is data that is combined with meaning. A temperature reading of 100 will have a different meaning if it is combined with the term Fahrenheit or with the term Celsius. Additional meaning could be added if more context for the temperature reading is added, such as whether the reading was for a liquid (e.g. water) or a gas (e.g. air), or knowledge that the "normal" temperature for this time of year is 20°.

As such, the term information is generally used to imply data combined with sufficient context to provide meaning for a human being.

Knowledge can be thought of as information that is combined with experience, context, interpretation and reflection. We will expand on this definition as we discuss specific examples later in this chapter.

Information System as an Input-Process-Output Model

One way of viewing possible relationships between data, information and knowledge is to consider an information system from the perspective of an IPO (input-process-output) model. On the input side we have data, as discussed previously. These data are then massaged or manipulated in some way (e.g. sorting, summarizing, filtering, formatting) to obtain information. Note that the transformation of data into information may be completed by a person (e.g. using a calculator) or by a computer program, although for our purposes we are typically more interested in situations where computer programs are employed.

A simple example could be the "what if" type of analysis that an electronic spreadsheet package offers. We can use our current understanding of a situation to develop a model of how sales will go up or down by a certain factor based on the amount we spend on advertising and other factors such as price.

The resulting information is used by a human to reach decisions (how many people to hire, how many products to produce, how much to spend on advertising). The outcomes of these decisions are observable results, such as sales volume during a certain time period, or the number and size of back-orders, etc. If these objective outcomes (results) are monitored and examined, then knowledge may be gained (e.g. how to avoid inventory shortages, or how to balance inventory carrying costs against costs associated with product shortages).

The role of feedback

In addition, the observable results can be used to provide feedback into the system. This feedback can be used to help improve knowledge. The improved knowledge can then be used in two ways. First, it can be used to determine what (if any) changes are needed in the way data are transformed into information. For example, it might be decided that summarizing sales data by product category and time period is not adequate, and that a breakdown by geographic region is also needed. If such a determination is made, the result could be a change to a computer program to provide sales reports in a new format, showing information that was previously hidden within the raw data.

The second way that knowledge can influence the information system is that it can be used by decision makers to help them interpret information, influencing future decisions and actions. An example could be a review of outstanding debts in the light of prevailing or expected changes in interest rates. In this way, the quality of the decisions reached should hopefully improve over time, leading to more effective actions.

Organizations do not operate within a vacuum; they interact continuously with their environment. As such, organizations need to constantly adjust to changes in their environment. Similarly, information systems should not be viewed as being static. As new knowledge is obtained, information systems are modified, updated and expanded to address challenges or to take advantage of opportunities.