Knowledge-Based Economy


The world has witnessed three distinct ages so far—the Agrarian Age, the Industrial Age, and now the Information Age. Globalization, rapid technological change and the importance of knowledge in gaining and sustaining competitive advantage characterize this Information Age (Wurzburg, 1998). Traditionally, economists have seen capital, labor, and natural resources as the essential ingredients for economic enterprise. In recent years, it has been noticed that the new economy of the 21st century is increasingly based on knowledge with information, innovation, creativity and intellectual capitalism as its essential ingredients (Persaud, 2001; Sharma & Gupta, 2003b). The shift to a knowledge-based economy results largely from developments in information and communications technologies. The facility to communicate information instantaneously across the globe has changed the nature of competition. A company's knowledge assets are inherent in the creativity of its knowledge workers combined with technological and market know-how (Halliday, 2001). Information can now be delivered with such speed that companies must develop their knowledge assets to solve competitive problems (Blundell et al., 1995; Bassi, 1997).

Earlier, neo-classical economics has recognized only two factors of production: labor and capital. Knowledge, productivity, education, and intellectual capital were all regarded as exogenous. Technological developments in the 20th century have transformed the majority of wealth-creating work from physically-based to "knowledge-based" (Foray & Lundvall, 1996). Technology and knowledge has become the third factor of production in leading economies (Romer, 1994). Romer argues that in today's world, new technological developments help further innovations and technology. Accumulation of knowledge has become one of the key drivers of economic growth in a knowledge-driven economy. A knowledge-driven economy is one in which the generation and exploitation of knowledge play the predominant part in the creation of wealth (Benhabib & Spiegel, 1994).

In a knowledge-based economy, knowledge drives the profits of the organizations for gaining and sustaining competitive advantage. Intellectual capital i.e. employees, their knowledge on products and services, and their creativity and innovity, is a crucial source of knowledge assets. The knowledge-based economy is all about adding ideas to products and turning new ideas into new products (Organization for Economic Co-operation and Development, 1996, 2000).

Realizing the importance of knowledge assets, many companies have changed their traditional organizations' structures. The traditional command-and-control model of management is rapidly being replaced by decentralized teams of individuals motivated by their ownership in the companies (McGarvey, 2001; Sharma & Gupta, 2003). The new structure of the economy is emerging from the convergence of computing, communications and content. Products are becoming digital and markets are becoming electronic. The knowledge-based economy is based on the application of human know-how to everything we produce, and hence, in this new economy, human expertise and ideas create more and more of the economy's added value, making some of the aforementioned questions less relevant or useful when trying to evaluate assets of a company (Benhabib & Spiegel, 1994). Thus, the knowledge-based economy is all about adding ideas to products and turning new ideas into new products (Edvinsson & Malone, 1997).

click to expand
Figure 1

The knowledge content of products and services is growing significantly as consumer ideas, information, and technology become part of products. In the new economy, the key assets of the organization are intellectual assets in the form of knowledge. Knowledge is what happens when human experience and insight benefit from recognizing or inferring patterns in data, information or already existing knowledge (Sharma & Gupta, 2003). And, wisdom is what happens when knowledge is accrued by human beings. Knowledge resides in the user and not in the collection of information. Leveraging knowledge involves:

  • Capturing the patterns recognized by human experience and insight (knowledge) so that these are available to and reusable by others.

  • Making it easy to find and reuse this knowledge, either as explicit knowledge that has been recorded in physical form or timely access to a human expert.

  • Aiding and abetting collaboration, continual learning and knowledge sharing.

  • Improving decision-making processes and quality.

What is KM and Knowledge?

Although many definitions of knowledge management have been posited, a particularly useful one has been described by the Gartner Group: Knowledge management is a discipline that promotes an integrated approach to identifying, managing, and sharing all of an enterprise's information needs. These information assets may include databases, documents, policies, and procedures as well as previously unarticulated expertise and experience resident in individual workers (Lee, 2000: Sharma & Gupta, 2003a). Knowledge management requires the application of a triad of people, process, and technology (Ruggles, 1997).

Data refers to transactions, processes, functions, products, services, events, concepts, sites, people and more—all gathered, processed and stored using basic organizational transaction and data collection systems. Information is the result of placing data into meaningful context—using ad hoc query and reporting tools to extract data from a database and combining it with other data or by categorizing text elements. Knowledge is information taken to the next level of abstraction, which is revealed in relationships. Knowledge is what happens when human experience and insight benefit from recognizing or inferring patterns in data, information or already existing knowledge (Roos et al., 1997). And, Wisdom is what happens when knowledge is accrued by human beings. Table 1 explains the level of complexity and tools involved for data, information and knowledge.

Table 1: Level of Complexity and Tools Involved for Data, Information and Knowledge.

Level of Complexity

Tools Involved

Data

Online transaction processing (OLTP) systems, databases, servers, local and network-based file systems, website click streams, e-mail

Information

Ad hoc query and reporting applications; content tagging (with metadata), indexing and categorization; text processing and mining

Analysis

Online analytical processing (OLAP) applications, data mining

Knowledge

Human insight derived from data, information and/or analyses

Wisdom

The mind of the knowledgeable beholder

Knowledge resides in the user and not in the collection of information. Leveraging knowledge involves:

  • Capturing the patterns recognized by human experience and insight (knowledge) so that these are available to and reusable by others.

  • Making it easy to find and reuse this knowledge, either as explicit knowledge that has been recorded in physical form or timely access to a human expert.

  • Aiding and abetting collaboration, continual learning and knowledge sharing.

Knowledge is of two kinds: tacit and explicit as explained in Table 2. Tacit Knowledge is knowledge gained from experience rather than that instilled by formal education and training personnel. It is context specific and difficult to formulize and explain. This includes know-how, crafts, and skills. This form of knowledge is created by human beings as mental models such as schemata, paradigms, perspectives, beliefs and viewpoints, etc. Explicit Knowledge is codified knowledge and refers to knowledge that is transmittable in formal systematic language. For example; documents, reports, memos, messages, presentations, database schemas, blueprints, architectural designs, etc. (Cole et al., 1997).

Table 2: Knowledge Contents.

From/To

Tacit Knowledge

Explicit Knowledge

Tacit Knowledge

Socialization

(Sympathized Knowledge)

Externalization

(Conceptual Knowledge)

Explicit Knowledge

Internalization

(Operational Knowledge)

Combination

(Systematic Knowledge)

In knowledge economy, it has become important for organizations to create knowledge management systems to gain competitive advantage. In the fast changing world, organizations have to continuously learn. Learning means not only using new technologies to access global knowledge, but it also means using them to communicate with other people about innovation. Organizational learning is the process by which organizations acquire tacit knowledge and experience. Such knowledge is unlikely to be available in codified form, so it cannot be acquired by formal education and training. Instead it requires a continuous cycle of discovery, dissemination, and the emergence of shared understandings. Successful firms are giving priority to the need to build a "learning capacity" within the organization (Foray & Lundvall, 1996; Lundvall & Johnson, 1994). To become knowledge driven, companies must learn how to recognize changes in intellectual capital in the worth of their business and ultimately in their balance sheets. A firm's intellectual capital—employees' knowledge, brainpower, know-how, and processes, as well as their ability to continuously improve those processes—is a source of competitive advantage (Booking, 1996; Sveiby, 1997; Fayyad et al., 1996).

Why Knowledge Management Matters for Creation of Intelligent Enterprises

Knowledge has emerged as the strategic focus for business and has been growing in importance over the last decade. As organizations focus on the competitive advantages about buying patterns, relationships with customers and trading partners, and best practices, companies need new ways to penetrate and dominate markets. Knowledge management is a competitive necessity. The main competitive uses of knowledge management (KM) are driving innovation and building value chains. KM brings order "to the chaos of infoglut" with powerful organizational, search and retrieval technologies that enable employees to find and focus on business-and task-relevant knowledge (Johnston & Blumentritt, 1998). Paybacks include reduction in the time for and improvement in the quality of decisions and more strategic benefits as employees access rich repositories of "corporate memory," which stimulates reuse and reapplication of the enterprise's collective experience and knowledge.

KM also helps by simplifying communication paths and reducing knowledge transfer to a near one-to-one employee exchange. Capturing the knowledge of experts effectively increases their "span of influence" because others can access the expert's knowledge without direct contact. An enterprise can also augment individual learning when employees have access to the insight and experience of others and when they interact with communities of people outside their own work teams. When this organizational learning is linked to enterprise strategy, employee learning will focus on the enterprise's future and its core competencies rather than simply on skills development.

Knowledge sharing and collaboration deliver operational benefits, including speeding delivery of products and services by connecting people to the expertise necessary to complete tasks more quickly (Tobin, 1998). And, by capturing the decision-making processes of employees into automated form, the need for human intervention can be lessened.

Initially, KM is mostly internally-focused toward business units and employees. But e-business is externally-focused and includes leveraging the intellectual assets of individual enterprises into strong value chains or exploiting the knowledge exchange with customers. That makes KM critical in enabling e-business transformation. Traditional separation of data and information into departmental and operation-specific systems can be overcome with knowledge management systems and tools that reach across functional, hierarchical, regional and business unit boundaries (Mansell & Wehn, 1998).

Why Do Organizations Need Business Intelligence?

The popularity of Web portals such as Excite and Yahoo—with their metaphor of providing a single, user-friendly entry point to a world of information sources—is filtering into business. Until not too long ago, many analytical professionals and corporate decision makers relied on desktop spreadsheets such as IBM's Lotus 1-2-3 or Microsoft's Excel to provide rudimentary BI capabilities. As their analytical needs outgrew these systems, they turned to full-fledged databases to expand their capabilities and provide greater scalability through the construction of data warehouses and data marts. Only the most strategically intelligent businesses will remain competitive and thrive in global, internet worked economy—those that have an enterprise-wide view of key business operations and that have the tools to link business strategy with operational execution. Executive-level BI applications such as Oracle Business Intelligence System (BIS) and Strategic Enterprise Management (SEM) that address these needs are becoming increasingly critical for businesses to view, manage, and act quickly and strategically upon their growing stores of information.

Prior to intelligent systems, traditional DSSs were designed to empower a small class of applications, usually those relating to sales and financial analysis. BI tools and applications are designed to broaden or extend DSS capabilities to include supporting applications such as human resources, supply-chain management, and customer service. Data warehouses and data marts by and large are still constructed to store historical data on past operations, but BI tools and applications apply the DSS functions that were once available only for viewing the past to today's online operational applications—those that capture the daily transactions within an enterprise, including accounting, manufacturing, supply-chain, and even front-office applications for customer-relationship management such as support and call-center tools.

The Business-Intelligence Hierarchy

The business intelligence term has become an umbrella description for a wide range of decision-support tools, some of which target specific user audiences. At the bottom of the BI hierarchy are Extraction and Formatting Tools which are also known as data-extraction tools. These tools collect data from existing databases for inclusion in data warehouses and data marts. The next level of BI hierarchy is known as Warehouses and Marts. Because the data comes from so many different, often incompatible systems with various file formats, the next step in the BI hierarchy is formatting tools, which are used to "cleanse" the data and convert it all to formats that can easily be understood in the data warehouse or data mart. The next level of tools needed are reporting and analytical tools. These are known as Enterprise Reporting and Analytical Tools. OLAP engines such as Oracle Express and analytical application-development tools are for professionals who analyze data and do business forecasting, modeling, and trend analysis.

Human Intelligence tools are those whereby human expertise, opinions, and observations can be recorded to create a knowledge repository. These tools are at the very top of the BI hierarchy. These tools bring analytical and BI capabilities along with human expertise (Fruin, 1997).




Intelligent Enterprises of the 21st Century
Intelligent Enterprises of the 21st Century
ISBN: 1591401607
EAN: 2147483647
Year: 2003
Pages: 195

flylib.com © 2008-2017.
If you may any questions please contact us: flylib@qtcs.net