Data Processing and Data Management
| Author | Hal Kirkwood |
| Pages | 155-159 |
Page 155
Data processing and data management are critical components of business organizations.
Data processing refers to the process of performing specific operations on a set of data or a database. A database is an organized collection of facts and information, such as records on employees, inventory, customers, and potential customers. As these examples suggest, numerous forms of data processing exist and serve diverse applications in the business setting.
Data processing primarily is performed on information systems, a broad concept that encompasses computer systems and related devices. At its core, an information system consists of input, processing, and output. In addition, an information system provides for feedback from output to input. The input mechanism (such as a keyboard, scanner, microphone, or camera) gathers and captures raw data and can be either manual or automated. Processing, which also can be accomplished manually or automatically, involves transforming the data into useful outputs. This can involve making comparisons, taking alternative actions, and storing data for future use. Output typically takes the form of reports and documents that are used by managers. Feedback is utilized to make necessary adjustments to the input and processing stages of the information system.
The processing stage is where management typically exerts the greatest control over data. It also is the point at which management can derive the most value from data, assuming that powerful processing tools are available to obtain the intended results. The most frequent processing procedures available to management are basic activities such as segregating numbers into relevant groups, aggregating them, taking ratios, plotting, and making tables. The goal of these processing activities is to turn a vast collection of facts into meaningful nuggets of information that can then be used for informed decision making, corporate strategy, and other managerial functions.
Data consist of raw facts, such as customer names and addresses. Information is a collection of facts organized in such a way that it has more value beyond the facts themselves. For example, a database of customer names and purchases might provide information on a company's market demographics, sales trends, and customer loyalty/turnover.
Turning data into information is a process or a set of logically related tasks performed to achieve a defined outcome. This process of defining relationships between various data requires knowledge. Knowledge is the body or rules, guidelines, and procedures used to select, organize, and manipulate data to make it suitable for specific tasks. Consequently, information can be considered data made more useful through the application of knowledge. The collection of data, rules, procedures, and relationships that must be followed are contained in the knowledge base.
In order for information to be valuable it must have the following characteristics, as adapted from Ralph M. Stair's book, Principles of Information Systems:
Accurate. Accurate information is free from error.
Complete. Complete information contains all of the important facts.
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Economical. Information should be relatively inexpensive to produce.
Flexible. Flexible information can be used for a variety of purposes, not just one.
Reliable. Reliable information is dependable information.
Relevant. Relevant information is important to the decision-maker.
Simple. Information should be simple to find and understand.
Timely. Timely information is readily available when needed.
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