Intrductin t Knwledge Management What Is Knwledge Management? By UNC Chapel Hill, NC Knwledge Management is ne f the httest tpics tday in bth the industry wrld and infrmatin research wrld. In ur daily life, we deal with huge amunt f data and infrmatin. Data and infrmatin is nt knwledge until we knw hw t dig the value ut f it. This is the reasn we need knwledge management. Unfrtunately, there's n universal definitin f knwledge management, just as there's n agreement as t what cnstitutes knwledge in the first place. We chse the fllwing definitin fr knwledge management fr its simplicity and brad cntext. Or Simple Definitin: Knwledge Management (KM) refers t a multi-disciplined apprach t achieving rganizatinal bjectives by making the best use f knwledge. KM fcuses n prcesses such as acquiring, creating and sharing knwledge and the cultural and technical fundatins that supprt them. Knwledge Management may be viewed in terms f: Peple hw d yu increase the ability f an individual in the rganizatin t influence thers with their knwledge Prcesses Its apprach varies frm rganizatin t rganizatin. There is n limit n the number f prcesses Technlgy It needs t be chsen nly after all the requirements f a knwledge management initiative have been established. Culture The biggest enabler f successful knwledge-driven rganizatins is the establishment f a knwledge-fcused culture Structure the business prcesses and rganizatinal structures that facilitate knwledge sharing Technlgy a crucial enabler rather than the slutin. What Is Knwledge Management Related T? Knwledge management draws frm a wide range f disciplines and technlgies: Cgnitive science Expert systems, artificial intelligence and knwledge base management systems (KBMS) Cmputer-supprted cllabrative wrk (grupware) Library and infrmatin science Technical writing Dcument management Decisin supprt systems 1
Semantic netwrks Relatinal and bject databases Simulatin Organizatinal science bject-riented infrmatin mdeling electrnic publishing technlgy, hypertext, and the Wrld Wide Web; help-desk technlgy full-text search and retrieval perfrmance supprt systems Althugh arund 20 kinds f disciplines and study areas were listed abve, there is n way t include all f the related subjects t knwledge management. The Histry f Knwledge Management A number f management therists have cntributed t the evlutin f knwledge management 1. 70's 2. 80's Peter Drucker: infrmatin and knwledge as rganizatinal resurces Peter Senge: "learning rganizatin" Lenard-Bartn: well-knwn case study f "Chaparral Steel ", a cmpany having knwledge management strategy Knwledge (and its expressin in prfessinal cmpetence) as a cmpetitive asset was apparent Managing knwledge that relied n wrk dne in artificial intelligence and expert systems Knwledge management-related articles began appearing in jurnals and bks 3. 90's until nw A number f management cnsulting firms had begun in-huse knwledge management prgrams Knwledge management was intrduced in the ppular press, the mst widely read wrk t date is Ikujir Nnaka s and Hirtaka Takeuchi s The Knwledge-Creating Cmpany: Hw Japanese Cmpanies Create the Dynamics f Innvatin (1995) The Internatinal Knwledge Management Netwrk(IKMN) went nline in 1994 Knwledge management has becme big business fr such majr internatinal cnsulting firms as Ernst & Yung, Arthur Andersen, and Bz-Allen & Hamiltn The Value f Knwledge Management Sme benefits f KM crrelate directly t bttm-line savings, while thers are mre difficult t quantify. In tday's infrmatin-driven ecnmy, cmpanies uncver the mst pprtunities and ultimately derive the mst value frm intellectual rather than physical assets. T get the mst value frm a cmpany's intellectual assets, KM practitiners maintain that knwledge must be shared and serve as the fundatin fr 2
cllabratin. Yet better cllabratin is nt an end in itself; withut an verarching business cntext, KM is meaningless at best and harmful at wrst. Cnsequently, an effective KM prgram shuld help a cmpany d ne r mre f the fllwing: Fster innvatin by encuraging the free flw f ideas Imprve decisin making Imprve custmer service by streamlining respnse time Bst revenues by getting prducts and services t market faster Enhance emplyee retentin rates by recgnizing the value f emplyees' knwledge and rewarding them fr it Streamline peratins and reduce csts by eliminating redundant r unnecessary prcesses These are the mst prevalent examples. A creative apprach t KM can result in imprved efficiency, higher prductivity and increased revenues in practically any business functin. Knwledge Management Tday Accrding t a recent IDC reprt, knwledge management is in a state f high grwth, especially amng the business and legal services industries. As the perfrmance metrics f early adpters are dcumenting the substantial benefits f knwledge management, mre rganizatins are recgnizing the value f leveraging rganizatinal knwledge. As a result, knwledge management cnsulting services and technlgies are in high demand, and knwledge management sftware is rapidly evlving. Knwledge Management Drivers The main drivers behind knwledge management effrts are: Knwledge Attritin: Despite the ecnmic slwdwn, vluntary emplyee turnver remains high. A recent survey by the glbal cnsulting firm Drake Beam Mrin revealed an average vluntary emplyee turnver rate f 20 percent with 81 percent f rganizatins citing emplyee turnver as a critical issue. Estimated annual csts f emplyee turnver were a staggering $129 millin per rganizatin. Much f this cst is due t knwledge attritin, which can be effectively minimized using knwledge management techniques. Knwledge Merging: Since 1980, the annual value f mergers has risen 100 fld reaching a cumulative $15 trillin in 1999. Over 32,000 deals were annunced; triple the number f 10 years earlier and mre than 30 times as many as in 1981. The recent frenzy f crprate mergers cupled with the increased need t integrate glbal crprate cmmunicatins requires the merging f disparate and ften cnflicting knwledge mdels. Cntent Management: The explsin f digitally stred business-critical data is widely dcumented. Frester Research estimates that nline strage fr Glbal 2,500 cmpanies will grw frm an average f 15,000 gigabytes per cmpany in 1999 t 153,000 gigabytes by 2003, representing a cmpund annual grwth rate f 78%. As the vlume f digital infrmatin expands, the need fr its lgical rganizatin is critical fr purpses f infrmatin retrieval, sharing and reuse. 3
E-Learning: As the ecnmy becmes mre glbal and the use f PCs mre pervasive, there has been a dramatic increase in e-learning, als knwn as cmputer based training. E-learning is clsely linked t and verlapping with, but nt equal t knwledge management. E-learning can be an effective medium fr knwledge management deliverables. KM Objectives The graph belw shws the results f a recent IDC study in which crpratins cited varius bjectives fr knwledge management effrts: Activities related t these bjectives include: creating knwledge sharing netwrks that facilitate a crprate knwledge culture, develping knwledge leaders, ptimizing intellectual capital by prducing knwledge management slutins such as cdificatin strategies and knwledge bases, and estimating revenue and efficiency gains resulting frm knwledge management in terms f return n investment (ROI). KM ROI Althugh 65% f rganizatins that are currently implementing KM initiatives have nt measured the impact f their perfrmance, large revenue gains and efficiency imprvements have been recrded by numerus majr crpratins. Fr instance: Frd Mtr Cmpany accelerated its cncept-t-prductin time frm 36 mnths t 24 mnths. The flw n value f this has been estimated at US $1.25 billin, The Dw Chemical Cmpany saved $40 millin a year in the re-use f patents, Chase Manhattan, ne f the largest banks in the US, used Custmer relatinship management KM initiatives t increase its annual revenue by 15%, and Pfizer credits 4
KM practices fr discvering the hidden benefits f the Viagra drug. Technlgies That Supprt Knwledge Management The fllwing diagram reflects the main technlgies that currently supprt knwledge management systems. These technlgies rughly crrelate t fur main stages f the KM life cycle: 1. Knwledge is acquired r captured using intranets, extranets, grupware, web cnferencing, and dcument management systems. 2. An rganizatinal memry is frmed by refining, rganizing, and string knwledge using structured repsitries such as data warehuses. 3. Knwledge is distributed thrugh educatin, training prgrams, autmated knwledge based systems, expert netwrks. 4. Knwledge is applied r leveraged fr further learning and innvatin via mining f the rganizatinal memry and the applicatin f expert systems such as decisin supprt systems. All f these stages are enhanced by effective wrkflw and prject management. 5
Present and Future State f KM Currently, cmmunities f practice such as the Knwledge Management Netwrk and the develpment f standards and best practices are in a mature stage f develpment. KM curricula such as certificatin, crprate training and university graduate certificate prgrams are n the rise. Techniques such as data mining and text mining that use KM fr cmpetitive intelligence and innvatin are in the early stages f develpment. Finally, rganizatins are investing heavily in ad hc KM sftware that facilitates rganizatinal knwledge. The chart belw estimates the state f their current and future KM activities. The Future f Knwledge Management In the next several years ad-hc sftware will develp int cmprehensive, knwledge aware enterprise management systems. KM and E-learning will cnverge int knwledge cllabratin prtals that will efficiently transfer knwledge in an interdisciplinary and crss functinal envirnment. Infrmatin systems will evlve int artificial intelligence systems that use intelligent agents t custmize and filter relevant infrmatin. New methds and tls will be develped fr KM driven E-intelligence and innvatin. 6
The Effect f Knwledge Management n Databases Multiple crprate databases will merge int large, integrated, multidimensinal knwledge bases that are designed t supprt cmpetitive intelligence and rganizatinal memry. These centralized knwledge repsitries will ptimize infrmatin cllectin, rganizatin, and retrieval. They will ffer knwledge enriching features that supprt the seamless interperability and flw f infrmatin and knwledge. These features may include: the incrpratin f vide and audi clips, links t external authritative surces, cntent qualifiers in the frm f surce r reference metadata, and anntatin capabilities t capture tacit knwledge. Cntent will be in the frm f small reusable learning bjects and assciated metadata that prvides cntextual infrmatin t assist KM reasning and delivery systems. The Implicatins f Knwledge Management Fr... Database Users: Frm business class users t the general public, database users will enjy a new level f interactin with the KM system including just-in-time knwledge that delivers precise relevant infrmatin n demand and in cntext. Mre cmplex, smart systems will translate t ptimal usability and less time spent searching fr relevant infrmatin. Fr example, data analysts will enjy simplified access and mre pwerful tls fr data explitatin. The use f knwledge bases can reduce custmer service csts by prviding custmers with easy access t 24/7 self service via smart systems that reduce the need t cntact custmer service r technical supprt staff. Database users may even create custmized views f knwledge bases that supprt their needs. Database Develpers: The design and develpment f knwledge based systems will be cnsiderably mre cmplex than current database develpment methds. Develpers must cnsider the verall technical architecture f the crpratin t ensure seamless interperability. The use f standardized metadata and methds will als facilitate bth intra-crprate and inter-crprate interperability. Making effective physical strage and platfrm chices will be equally mre cmplex. Bth knwledge base develpers and administratrs must understand the rle f the knwledge base in the verall KM system. Database Administratrs: Database Administratrs will evlve int Knwledge Managers. The knwledge base will stre and maintain crprate memry and Knwledge Managers will becme the gatekeepers f crprate knwledge. The lines between technical rles such as Web Develper, Data Analyst r Systems Administratr will blur as these systems merge int and verlap with KM systems. DBAs will need t have sme knwledge abut each f these disciplines. General Public: Even if they are nt interacting directly with a knwledge base, the general public will benefit frm the secndary effects f imprved custmer service due t faster access t mre accurate infrmatin by service prviders. Summary Organizatins are realizing that intellectual capital r crprate knwledge is a valuable asset that can be managed as effectively as physical assets in rder t imprve perfrmance. The fcus f knwledge management is cnnecting peple, prcesses and technlgy fr the purpse f leveraging crprate knwledge. The database prfessinals f tday are the Knwledge Managers f the future, and they will play an integral rle in making these cnnectins pssible. 7