Knowledge Management, Essay Example

E-systems use knowledge management in a variety of ways: Hewlett-Packard, for example, implemented a knowledge management policy “that Notes should be used for discussion-oriented applications, and the Web for publishing purposes” (Davenport & Prusak, 2000, p. 123). Since that decision around 1995, Hewlett-Packard has evolved its knowledge management systems precipitously: the company’s “Electronic Sales Partner (ESP) system contains hundreds of thousands of documents that help HP’s computer systems sales force in the sales process” (pp. 123-124). Moreover, HP maintains an intranet web, which includes “white papers, sales presentations, technical specifications, and pointers to external materials” (p. 124). And HP’s “Web-based system… ‘Connex’” can identify experts in any relevant field (p. 124). These are outstanding examples of knowledge management.

An example in education is the use of automatic learning object selection and sequencing in automatic courseware systems: as Karampiperis and Sampson (2006) explained, “the main idea is to generate a course suited to the needs of the learners” (pp. 56-58). Automatic learning object selection facilitates this process by filtering through a possible learning objects in order to “generate a pool of learning objects that match the general content requirements” (p. 58). According to O’Brien, Hanka, Buchan and Heathfield (2002), the WaX Active Library is a knowledge management system used in the ever more-specialized field of medicine (pp. 64-66). WaX Active Library supports “clinical decision-making by putting key clinical knowledge, evidence-based information, details of services provided locally, outcome measures, information for patients” in the hands of doctors (p. 66). The advantages of WaX are many: it provides doctors with a great deal of specialized information, literally “a library of electronic books”, which they can search at will (p. 66). WaX effectively and efficiently manages the considerable corpus of medical knowledge in an easy-to-use format.

Knowledge management systems perform diverse functions within the context of information systems. The seminal advantage of a well-constructed knowledge management system is its power to organize information in topical, relevant ways, making it easier to find. The paramount function of a knowledge management system within an information system context is to turn a plethora of data into usable, well-organized information, thereby increasing the efficiency and productivity of those who use it.    

The architecture of a general knowledge management system might follow one of two basic designs: either a centralized architecture, or a distributed architecture (Maier, 2007, p. 18). According to Maier, “the prevalent architectural design recently has been impacted profoundly by… service-oriented architecture (SOA)” (p. 302). The key SOA concepts include “an application frontend” and an emphasis on the service that the system is intended to provide (p. 303). With an integrated architecture, components such as the sources of information—e.g. “external sources” and “inter-/intranet”—are brought together with the information services, such as “publishing”, “work flow”, and “library” (p. 317). This produces the integration services, such as “search engine”, “data warehouse”, “directory”, and “viewer” (p. 317). Collectively, these elements are part of an integrated work place (p. 317). What makes this system integrated, however, is that it consists of a number of separate architectures, suggesting “a number of components organized on… layers, but none of the architectures comprises all the layers” (p. 318). They are integrated, to be sure, but they are not bound together by a unifying, directing architectural principle.

By contrast, a centralized knowledge management system has a focus, a component of the system that serves as the ordering principle (Maier, 2007, p. 319). The example given by Maier has the “knowledge worker” on the very top tier: the knowledge worker accesses services, such as “authentication; translation and transformation for diverse applications and appliances (e.g., browser, PIM, file systems, PDA, mobile phone)” (p. 319). The knowledge worker can then personalize the service, selecting particular knowledge portals, profiling information, and using “push-services; process-, project- or role-oriented knowledge portals” (p. 319). This in turn allows the knowledge worker to access the knowledge services, acquiring information, editing and publishing it, etc. This in turn leads to the integration services, which encompass the taxonomy and structure of knowledge, and thence to the infrastructure services, which include “messaging, teleconferencing, file server, imaging, asset management, security services” and the like (p. 319). The final result is useful data—knowledge—of any kind desirable (p. 319).

The previous descriptions covered the architecture of a basic knowledge management system. Such a system would be well-suited for managing a database of information. However, there are many other types of e-systems, including:

Public key cryptosystems: As described by Obaidat and Boudriga (2007), public key cryptosystems use cryptographic algorithms called ciphers to encrypt and decrypt information (p. 25). Cryptographic algorithms work with a key, usually “a word, number, or phrase, to encrypt the plaintext” (p. 25). The purpose of such systems is to protect information exchanged between two or more parties from unauthorized access, providing security for online exchanges and transactions (p. 25).

Weak authentication systems: These authentication systems are commonly used throughout cyberspace, in order to authenticate users to a system or other information (Obaidat & Boudriga, 2007, p. 50). Passwords are the most common of these: the system “compares the entered password against the expected response after the login of the user” (p. 50). PIN-based authentication is similar, except that the PIN—personal identification number—is stored on “a physical device, such as a banking card” (p. 51).

Public key infrastructure (PKI) systems: PKIs differ from public key cryptosystems in ensuring a new level of authentication: they are “designed to generate digital certificates (or public key certificates) to bind a public key to the identifying information about its owner” (Obaidat & Boudriga, 2007, p. 76). PKIs ensure that a person or entity online is who they claim to be: individuals can “have confidence that the public keys they use truly belong to the entity… with whom they are communicating” (p. 76).

Expert systems: Expert systems are designed to perform a specific function, typically a function associated with human expertise, at “expert” levels of proficiency (Davenport & Prusak, 2000, p. 127). McDonnell Douglas developed such a system in order to “scan aircraft approaching the runway and determine if they were positioned properly for landing”, in an effort to replicate the knowledge base and insight of experienced ground crews (p. 127). As the ground crews “were not in a position to communicate corrections to the pilots”, the purpose of the system was to analyze the conditions and then relay the information to the pilots (p. 127). After two years and considerable expense, the system “eventually proved to be 80 to 85 percent as accurate as the two-second human glance” (p. 127).

Enterprise resource planning (ERP) systems: ERPs are a very specific kind of e-system, consisting of software packages used “for the core systems necessary to support enterprise systems” (Gelinas & Dull, 2009, p. 3). In the accounting profession, ERPs are crucial, because accountants “will be members of the teams that will install and operate systems in their organizations” (p. 3).  

A real-world example of an outstanding knowledge management system is that of Polk, a company described by Henschen (2011) as “the kingpin of auto-market data and analysis” (p. 1). According to Henschen, last year Polk, a “longtime Oracle customer,” decided to “upgrade to that vendor’s Oracle Exadata Database Machine” (p. 1). The upgrade has been a considerable endeavor: Henschen reported that Polk is “now nearly halfway through a steady enterprise-wide migration of multiple databases and applications” (p. 1). However, the advantages have been nothing short of precipitous: in the process of implementation, Polk has “consolidated database licenses, eliminated servers, compressed data dramatically, and delivered results 10 times faster” than it did previously, on its “Oracle Real Application Clusters (RAC) deployments” (p. 1).

Polk has been in the business of tracking auto sales for a long time: since the 1920s (Henschen, 2011a, p. 1). With its knowledge management system, Polk is able to compile and collate “state-supplied information on new and used auto registrations with manufacturer-supplied sales data, and third-party demographic and lifestyle data” (p. 1). Although the company has “several key customer groups,” the most important revenue stream comes from Polk Insight, “an analytic datamart that helps manufacturers understand where they’re winning and where they’re losing by product, competitor, new vs. used, region, dealer, and other dimensions” (p. 1). For example, Polk can answer questions such as “How many BMW 3 Series vehicles were sold in California last month—and is the model gaining or losing share against the Audi A4?” or “Which Hyundai dealers are driving the lion’s share of that brand’s sales gains?” (p. 1). This is the power of knowledge management in action: Polk is able to collect these disparate sources of data and extract meaningful conclusions out of them.

Polk’s services are very valuable for those in car- and truck-marketing business (Henschen, 2011a, p. 1). With the information Polk provides them, dealers can “acquire new customers, retain existing ones, [and] build brand awareness,” all important applications of Polk’s work (p. 1). Polk’s research and knowledge management can also help dealers to identify and target “specific customer segments that can be reached with advertising and promotional campaigns delivered across email, direct mail, and websites” (p. 1). The secret is the rich mine of demographic and lifestyle data that Polk unearths: this information gives Polk’s clients “a clear picture of consumer tastes and preferences by age, income, zip code, leisure activities, and other dimensions” (p. 1).

Polk initially hesitated on buying Oracle’s Exadata 1.0, citing concerns that “it was a 1.0 product,” and, moreover, the company had already purchased a considerable quantity of new technology (Henschen, 2011a, p. 1). However, in retrospect it has become apparent that the move was an overdue one: because Polk’s work demands complex, multidimensional queries, e.g. queries “requesting demographic buyer insight within specific regions and dealer zones,” Polk’s previous technology was no longer up to par, with said queries “taking as long as three to five minutes” (p. 2). The benefits of switching to Exadata have been considerable: the storage efficiency of data improved drastically, with an approximate two terabytes compressing “to about 600 gigabytes” (p. 2).

The case of Polk, automobile data marketer, demonstrates the effective use of knowledge management systems in action. As Davenport and Prusak (2000) explained, “data describes only a part of what happened; it provides no judgment or interpretation and no sustainable basis of action” (p. 3). This is the function of a knowledge management system: to interpret the data and make sense of it, enabling the user to make judgments for future action based upon these results. A properly-ordered knowledge management system offers the user ease and efficiency of use in accessing important information.

References

Bragg, S. M. (2011). The new CFO financial manual (2^nd ed.). Hoboken, NJ: John Wiley & Sons.

Davenport, T., & Prusak, L. (2000). Working knowledge: How organizations manage what they know. Watertown, MA: Harvard Business School Press.

Gelinas, U. J., & Dull, R. B. (2009). Accounting information systems. Mason, OH: South-Western Cengage Learning.

Henschen, D. (2011a, August 30). How Oracle helps Polk decode car buying secrets. Information Week. Retrieved from http://www.informationweek.com/—. (2011b, August 31).

Salesforce.com taps HTML5 for tablets, opens up Chatter. Information Week. Retrieved from http://www.informationweek.com/

Karampiperis, P., & Sampson, D. (2006). Automatic learning object selection and sequencing in web-based intelligent learning systems. In Z. Ma (Ed.), Web-based intelligent e-learning systems: Technologies and applications (pp. 56-71). Hershey, PA: Idea Group, Inc.

Maier, R. (2007). Knowledge management systems: Information and communication technologies for knowledge management (3^rd ed.). New York: Springer.

Mohorovi?i?, S., Tijan, E., and ?iši?, D. (2010). Using web content management systems in university e-commerce courses. International Journal of Emerging Technologies in Learning, supp. 2, pp. 38-42. doi:10.3991/ijet.v5s2.1249

O’Brien, C., Hanka, R., Buchan, I., & Heathfield, H. (2002). Managing information overload in the health sector: The WaX ActiveLibrary system. In S. Barnes (Ed.), Knowledge management systems: Theory and practice (pp. 64-81). Oxford, UK: The Alden Press.

Obaidat, M. S., & Boudriga, N. (2007). Security of e-systems and computer networks. Cambridge, MA: Cambridge University Press.

Rahim, M., & Singh, M. (2007). Understanding benefits and impediments of B2E e-business systems adoption: Experiences of two large Australian universities. Journal of Internet Commerce, 6(2), pp. 3-17. doi:10.1300/J179v06n02_02 3

Velte, T., Velte, A., & Elsenpeter, R. C. (2010). Cloud computing: A practical approach. New York: McGraw-Hill.