Knowledge Management: A Careful Look.

Abstract

During the past three decades, corporations, governments, and educational institutions in industrialized nations have adopted information technology to run their daily operations. Information technology, both computer hardware and software and communications technologies, is at the core of knowledge-based economy. Knowledgebased economy is a major driving force for the economy and society of industrialized nations [www.wkforum.org]. Knowledge management has become an important subject in this context. However, the term “knowledge management” is vaguely understood. One reason is that the term “knowledge” itself has been both overused and loosely used. To understand the technical issues and challenges in knowledge management, one must first understand the term knowledge better. In this article we will take a careful look at the term “knowledge”, and on that basis shed some light on the concept of knowledge management, and then in turn point out some major issues and challenges in knowledge management. 1 TYPES OF KNOWLEDGE The term “knowledge” is often used interchangeably with the term “information”. Although the marketing literature on some data-processing products talk of knowledge being more useful and advanced than information, and information being more useful and advanced than raw data, we do not believe there is a compelling distinction between information and knowledge. (Knowledge is also known in some philosophical circles as true or verified belief.) The term “data”, however, can be distinguished from “knowledge”. “Data” refers to uninterpreted and unprocessed raw data, while “knowledge” refers to either data or “value-added data”. “Metadata” is also “data”. Metadata is data about data, and is found in data dictionaries or system catalogs in database systems. “Value-added data” is data obtained by querying, manipulating, analyzing, and interpreting raw data. A list of customers and items they purchased from a store would be raw data. The metadata for this data describes the data, for example, customer name as a 20-byte string, purchase date in mm/dd/yy date format, purchase KNOWLEDGE MANAGEMENT: A CAREFUL LOOK 30 JOURNAL OF OBJECT TECHNOLOGY VOL. 2, NO. 1 items as a list of 20-byte strings, etc. A purchase pattern discovered from the list of customers and items they purchased, such as a sudden surge in the purchases of canned goods or purchases of certain combinations of items, would be knowledge. Sometimes one refers to “knowledge” as “actionable data” as a way of distinguishing data that is relevant to a given objective from one that is not. For example, the discovery of a sudden surge in the purchases of canned goods may be actionable knowledge to a retail chain, in that the retail chain may then order additional canned goods or raise the price on canned goods. However, such discovery as “most husbands are men” would not be useful to a rental car company. “Actionable data” may be raw data or data obtained from raw data; in other words, raw data, such as customer names and addresses, may be directly relevant to a given objective, such as a marketing campaign. This is why we believe knowledge should be regarded as encompassing both raw data and data obtained from raw data, rather than just data obtained from raw data. To help understand knowledge in a systematic way, we provide a taxonomy of knowledge in the Figure below. Knowledge may be either computerized or noncomputerized. Computerized knowledge is one that is stored in a computer system and is amenable to processing by computer software and hardware. Non-computerized knowledge is one that resides in human brains or is recorded in recordable, but not computer-processable, media, such as paper. In the context of “knowledge-based economy”, for example, knowledge means both computerized and non-computerized knowledge. “Knowledge management systems”, on the other hand, deal only with computerized knowledge. Computerized knowledge has two types. One is explicit knowledge and another is implicit knowledge. Explicit computerized knowledge is knowledge that is captured and stored in a computer system, such as databases, files, data warehouses, the Web, information portals, etc. Explicit knowledge in turn comes in two types. One is knowledge whose semantics are known to computer software such that the computer Taxonomy of Knowledge Computerized knowledge