To transform information into knowledge- a firm must expend additional resources to discover, patterns, rules, and context where the knowledge works

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1 Chapter 15- Managing Knwledge Knwledge Management Landscape Knwledge management systems- supprt the creatin, capture, strage, and disseminatin f firm expertise and knwledge, have becme ne f the fastest-grwing areas f crprate and gvernment sftware investment. Tday we live in a knwledge sciety in which scial, ecnmic, and plitical changes are taking place as cuntries mve frm the industrial t what have been called the pst-industrial age. Imprtant Dimensins f Knwledge T transfrm infrmatin int knwledge- a firm must expend additinal resurces t discver, patterns, rules, and cntext where the knwledge wrks Wisdm is thught t be the cllective and individual experience f applying knwledge t the slutin f prblems. Wisdm invlves where, when, and hw t apply knwledge. Tacit knwledge- undcumented knwledge residing in minds f emplyees Explicit knwledge- knwledge that has been dcumented LOOK AT TABLE 15-1 Pg. 503 Organizatinal learning- the prcess f change Arguably, rganizatins that can sense and respnd t their envirnment rapidly will survive lnger than rganizatins that have pr leaning mechanisms. The Knwledge Management Value Chain Knwledge management- the set f business prcesses develped in an rganizatin t create, stre, transfer, and apply knwledge. Knwledge management increases the ability f the rganizatin t learn frm its envirnment and t incrprate knwledge int its business prcesses. LOOK AT FIG 15-1 Pg.502 Knwledge Infrmatin System s Activities Acquire Knwledge discvery Data mining Neutral netwrks

2 Genetic algrithms Knwledge wrkstatins Expert knwledge netwrks Stre Dcument management systems Knwledge databases Expert systems Disseminate Intranet prtals Push reprts Search engines Grupware Cllabratin Apply decisin supprt systems Enterprise applicatins Chief knwledge fficer (CKO)- is a senir executive wh is respnsible fr the firm s knwledge management prgram. The CKO helps design prgrams and systems t find new surces f knwledge r t make better use f existing knwledge in rganizatinal and management prcesses. Cmmunities f practice (COPs) are infrmal scial netwrks f prfessinals and emplyees within and utside the firm wh have similar wrk-related activities and interests. Types f Knwledge Management Systems 3 majr types f knwledge management systems: LOOK AT FIG 15-2 PG 504 ******* Enterprise-wide knwledge management systems General purpse firm-wide effrts t cllect, stre, distribute, and apply digital cntent and knwledge Knwledge wrk systems

3 Are specialized systems built fr engineers, scientists, and ther knwledge wrkers charged with discvering and creating new knwledge fr a cmpany Intelligent techniques Such as data mining, neutral netwrks, fuzzy lgic genetic algrithms, and intelligent agents Enterprise-Wide Knwledge Management Systems LOOK AT PAGE 506 TABLE 15-2 *** Systems fr structured and semi-structured knwledge functin as knwledge repsitry- is a cllectin f internal and external knwledge in a single lcatin fr mre efficient management and utilizatin by the rganizatin. They may prvide access thrugh enterprise prtals and search engine technlgy and may include tls fr accessing infrmatin frm crprate databases. One f the first challenges that firms face when building knwledge reciprcities f any kind is the prblem f identifying the crrect categries t use when classifying dcuments. Firms are increasingly using a cmbinatin f internally develped taxnmies and search engine techniques. Taxnmy- is a scheme fr classifying infrmatin and knwledge in such a way that it can easily be accessed. A taxnmy is like a table f cntents in a bk r like a library s system fr classifying bks and peridicals accrding t subject matter and authr. A business can access infrmatin much mre easily if it devises its wn taxnmy fr classifying infrmatin int lgical categries. Learning management systems (LMS)- prvide tls fr the management, delivery, tracking, and assessment f varius types f emplyee learning and training. Knwledge Wrkers- include researchers, designers, architects, scientists, and engineers wh primarily create knwledge and infrmatin fr the rganizatins. Knwledge wrkers usually have high levels f educatin and memberships in prfessinal rganizatins and are ften asked t exercise independent judgment as a rutine aspect f their wrk. They have 3 Key Rles critical t the rganizatin and t the managers wh wrk within the rganizatin: Keeping the rganizatin current in knwledge as it develps in the external wrld (technlgy, science, scial thught, the arts) Serving internal cnsultants regarding the areas f their knwledge, the changes taking place, and pprtunities

4 Acting as change agents, evaluating, initiating, and prmting change prjects Knwledge Wrk Systems Requirements f Knwledge Wrk Systems Mst knwledge wrkers rely n ffice systems, such as wrd prcessrs, vice mail, , videcnferencing, and scheduling systems, which are designed t increase wrker prductivity in the ffice. Examples: Cmputer-aided design (CAD)- autmates the creatin and revisin f designs, using cmputers, and sphisticated graphics sftware. Virtual reality systems- have visualizatin, rendering, and simulatin capabilities that g far beynd thse f cnventinal CAD systems. Virtual Reality Mdeling Language (VRML) is used. Investment wrkstatins T leverage the knwledge and times f its brkers, traders, and prtfli managers. Intelligent Techniques Artificial intelligence and database technlgy prvide a number f intelligent techniques that rganizatins can use t capture individual and cllective knwledge and t extended their knwledge based. Artificial intelligence technlgy (AI technlgy)- cnsists f cmputer-based systems (bth hardware and sftware) that attempt t emulate human behaviur. Capturing Knwledge: Expert Systems Expert system- is an intelligent technique fr capturing tacit knwledge in a very specific and limited dmain f human expertise. They capture the knwledge f skilled emplyees in the frm f a set f rules in a sftware system that can be used by thers in the rganizatin. Hw Expert System Wrk Knwledge base- human knwledge mdeled by Expert systems as a set f cllective rules Inference Engine- the strategy used t search thrugh the knwledge base Knwledge engineer- same as a traditinal systems analyst but has special expertise in eliciting infrmatin and expertise frm ther prfessinals

5 Tw strategies cmmnly used: Frward chaining- infrmatin is entered by the user and then searches the knwledge base t arrive at a cnclusin Backward chaining- the strategy fr searching the knwledge base starts with a hypthesis and prceeds by asking the user questins abut selected facts until the hypthesis either cnfirmed r disapprved. Organizatinal Intelligence: Case-based reasning Organizatinal knwledge- cllective knwledge and expertise that they have built up ver the years. Expert systems primarily capture the tacit knwledge f indivisual experts, but rganizatins als have cllective knwledge and expertise that they built up ver the years. Case-based reasning (CBR)- descriptins f part experiences f human specialists, represented as cases, are stred in a database fr later retrieval when the user encunters a new case with similar parameters. Fuzzy Lgic Systems Fuzzy lgic- a rule-based technlgy that can represent such imprecisin by creating rules that use apprximate r subjective values. It can descruibe a particular phenmenn r prcess linguistically and then represent that descriptin in a small number f flexible rules. READN PG 521 Neutral Netwrks Neutral netwrks-are used fr slving cmplex, prly understd prblems fr which amunts f data have been cllected. They find patterns and relatinships in massive amunts f data that wuld be t cmplicated and difficult fr a human being t analyze. Genetic Algrithms Genetic algrithms- are useful fr finding the ptimal slutin fr a specific prblem by examining a very large number f pssible slutins fr thse prblems. Hybrid Al Systems Hybrid AI systems- single applicatin that integrates fuzzy lgic, neutral netwrks, and expert systems t take advantage f the best features f these technlgies Intelligent Agents Intelligent agents- sftware prgrams that wrk in the backgrund withut direct human interventin t carry ut specific, repetitive, and predictable tasks fr an individual user, business prcess, r sftware applicatin.

6 Agent-based mdeling applicatins- develped t mdel the behaviur f cnsumers, stck markets, and supply chains and t predict the spread f epidemics.

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