Information Technology Reference
In-Depth Information
As you read this chapter, consider the following:
•
What steps can a business take to retain corporate knowledge within the business?
•
How does computer intelligence compare to human intelligence?
•
How can people and businesses make the best use of artificial intelligence and other
specialized systems?
Knowledge management and specialized information systems are used in almost
every industry. If you are a manager, you might use a knowledge management sys-
tem to support decisive action to help you correct a problem. If you are a production
manager at an automotive company, you might oversee robots that attach wind-
shields to cars or paint body panels. As a young stock trader, you might use a special
system called a neural network to uncover patterns and make millions of dollars
trading stocks and stock options. As a marketing manager for a PC manufacturer,
you might use virtual reality on a Web site to show customers your latest laptop and
desktop computers. If you are in the military, you might use computer simulation as
a training tool to prepare you for combat. In a petroleum company, you might use an
expert system to determine where to drill for oil and gas. You will see many additional
examples of using these specialized information systems throughout this chapter.
Learning about these systems will help you discover new ways to use information
systems in your day-to-day work.
Why Learn About
Knowledge
Management and
Specialized
Information
Systems?
Like other aspects of an information system, the overall goal of knowledge management and
the specialized systems discussed in this chapter is to help people and organizations achieve
their goals. In some cases, knowledge management and these specialized systems can help an
organization achieve a long-term, strategic advantage. In this chapter, we explore knowledge
management, artificial intelligence, and many other specialized information systems, includ-
ing expert systems, robotics, vision systems, natural language processing, learning systems,
neural networks, genetic algorithms, intelligent agents, and virtual reality.
Chapter 1 defines and discusses data, information, and knowledge. Recall that
data
consists
of raw facts, such as an employee number, number of hours worked in a week, inventory part
numbers, or sales orders. A list of the quantity available for all items in inventory is an example
of data. When these facts are organized or arranged in a meaningful manner, they become
information.
Information
is a collection of facts organized so that they have additional value
beyond the value of the facts themselves. An exception report of inventory items that might
be out of stock in a week because of high demand is an example of information.
Knowledge
is the awareness and understanding of a set of information and the ways that information
can be made useful to support a specific task or reach a decision. Knowing the procedures
for ordering more inventory to avoid running out is an example of knowledge. In a sense,
information tells you what has to be done (low inventory levels for some items), while
knowledge tells you how to do it (make two important phone calls to the right people to get
the needed inventory shipped overnight). See Figure 11.1.
Figure 11.1
Data
There are 20 PCs in stock at the retail store.
The Differences Among Data,
Information, and Knowledge
Information
The store will run out of inventory in a week unless more is ordered today.
Knowledge
Call 800-555-2222 to order more inventory.
