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Unformatted text preview: Chapter 11: Managing Knowledge and Collabora5on Part 1: Introduc5on to Knowledge Management Learning Objec5ves • Assess the role of knowledge management and knowledge management programs in business • Describe the types of systems used for enterprise‐wide knowledge management and demonstrate how they provide value for organiza5ons • Describe the major types of knowledge work systems and assess how they provide value for ﬁrms • Evaluate the business beneﬁts of using intelligent techniques for knowledge management Produced by Dr. Brian Janz 1 Knowledge in Organiza5ons • Knowledge workers – Researchers, designers, architects, scien5sts, and engineers who create knowledge and informa5on for the organiza5on – Three key roles: • Keeping organiza5on current in knowledge • Serving as internal consultants regarding their areas of exper5se • Ac5ng as change agents, evalua5ng, ini5a5ng, and promo5ng change projects The Knowledge Management Landscape • To transform informa5on into knowledge, ﬁrm must expend addi5onal resources to discover paOerns, rules, and contexts where knowledge works • Knowing how to do things eﬀec5vely and eﬃciently in ways other organiza5ons cannot duplicate is primary source of proﬁt and compe55ve advantage that cannot be purchased easily by compe5tors – Example: Having a unique build‐to‐order produc5on system Produced by Dr. Brian Janz 2 The Knowledge Management Landscape • Substan5al part of a ﬁrm’s stock market value is related to intangible assets: knowledge, brands, reputa5ons, and unique business processes • Knowledge‐based projects can produce extraordinary ROI • Sales of enterprise content management soTware for knowledge management expected to grow 15 percent annually through 2012 Important Dimensions of Knowledge • Knowledge is a ﬁrm asset – Intangible – Crea5on of knowledge from data, informa5on, requires organiza5onal resources – As it is shared, experiences network eﬀects – May be explicit (documented) or tacit (residing in minds) – Know‐how, craT, skill – How to follow procedure – Knowing why things happen (causality) • Knowledge has diﬀerent forms Produced by Dr. Brian Janz 3 Important Dimensions of Knowledge • Knowledge has a loca5on – Cogni5ve asset or event – Both social and individual – “S5cky” (hard to move), situated (enmeshed in ﬁrm’s culture), contextual (works only in certain situa5ons) • Knowledge is situa5onal – Condi5onal: Knowing when to apply procedure – Contextual: Knowing circumstances to use certain tools Knowledge in Organiza5ons • Three major types of knowledge in enterprise – Structured documents • Reports, presenta5ons • Formal rules – Semi‐structured documents • E‐mails, videos – Unstructured, tacit knowledge • 80% of an organiza5on’s business content is semi‐structured or unstructured Produced by Dr. Brian Janz 4 Knowledge Management: New Organiza5onal Roles and Responsibili5es • Chief knowledge oﬃcer execu5ves • Dedicated staﬀ / knowledge managers • Communi5es of prac5ce (COPs) – Informal social networks of professionals and employees within and outside ﬁrm who have similar work‐related ac5vi5es and interests – Ac5vi5es include educa5on, online newsleOers, sharing experiences and techniques – Facilitate reuse of knowledge, discussion – Reduce learning curves of newemployees The Knowledge Management Value Chain • Knowledge management: Set of business processes developed in an organiza5on to create, store, transfer, and apply knowledge • Knowledge management value chain: Each stage adds value to raw data and informa5on as they are transformed into usable knowledge – Knowledge acquisi5on – Knowledge storage – Knowledge dissemina5on – Knowledge applica5on Produced by Dr. Brian Janz 5 The Knowledge Management Value Chain: Knowledge Acquisi5on • Documen5ng tacit and explicit knowledge – Storing documents, reports, presenta5ons, best prac5ces – Unstructured documents (e.g., e‐mails) – Developing online expert networks • Tracking data from TPS and external sources The Knowledge Management Value Chain: Knowledge Storage • Databases • Document management systems • Role of management: – Support development of planned knowledge storage systems – Encourage development of corporate‐wide schemas for indexing documents – Reward employees for taking 5me to update and store documents properly Produced by Dr. Brian Janz 6 The Knowledge Management Value Chain: Knowledge Dissemina5on • Portals • Push e‐mail reports • Search engines • Collabora5on tools • A deluge of informa5on? – Training programs, informal networks, and shared management experience help managers focus aOen5on on important informa5on The Knowledge Management Value Chain: Knowledge Applica5on • To provide return on investment, organiza5onal knowledge must become a systema5c part of management decision making • Decision‐support systems can help here – New business prac5ces – New products and services – New markets Produced by Dr. Brian Janz 7 Chapter 11: Managing Knowledge and Collabora5on Part 2: Enterprise‐Wide Knowledge Management Systems and Knowledge Work Systems Knowledge Management Systems • Three major types of knowledge management systems: – Enterprise‐wide knowledge management systems – Knowledge work systems (KWS) • General‐purpose ﬁrm‐wide eﬀorts to collect, store, distribute, and apply digital content and knowledge – Intelligent techniques • Specialized systems built for engineers, scien5sts, other knowledge workers charged with discovering and crea5ng new knowledge • Diverse group of techniques such as data mining used for various goals: discovering knowledge, dis5lling knowledge, discovering op5mal solu5ons Produced by Dr. Brian Janz 8 Enterprise‐Wide Knowledge Management Systems • Help capture, store, retrieve, distribute, preserve – Documents, reports, best prac5ces – Semistructured knowledge (e‐mails) • Bring in external sources – News feeds, research • Tools for communica5on and collabora5on Enterprise‐Wide Knowledge Management Systems • Enterprise‐wide content management systems – Key problem – Developing taxonomy • Knowledge objects must be tagged with categories for retrieval – Digital asset management systems • Specialized content management systems for classifying, storing, managing unstructured digital data • Photographs, graphics, video, audio Produced by Dr. Brian Janz 9 Enterprise‐Wide Knowledge Management Systems • Knowledge network systems – Provide online directory of corporate experts in well‐deﬁned knowledge domains – Use communica5on technologies to make it easy for employees to ﬁnd appropriate expert in a company – May systema5ze solu5ons developed by experts and store them in knowledge database • Best‐prac5ces • Frequently asked ques5ons (FAQ) repository Enterprise‐Wide Knowledge Management Systems • Major knowledge management system vendors include powerful portal and collabora5on technologies – Portal technologies: Access to external informa5on • News feeds, research • Access to internal knowledge resources – Collabora5on tools • E‐mail • Discussion groups • Blogs • Wikis • Social bookmarking Produced by Dr. Brian Janz 10 Enterprise‐Wide Knowledge Management Systems • Learning management systems – Provide tools for management, delivery, tracking, and assessment of various types of employee learning and training – Support mul5ple modes of learning • CD‐ROM, Web‐based classes, online forums, live instruc5on, etc. – Automates selec5on and administra5on of courses – Assembles and delivers learning content – Measures learning eﬀec5veness Knowledge Work Systems • Knowledge work systems – Systems for knowledge workers to help create new knowledge and ensure that knowledge is properly integrated into business – Substan5al compu5ng power for graphics, complex calcula5ons – Powerful graphics, and analy5cal tools – Communica5ons and document management capabili5es – Access to external databases – User‐friendly interfaces – Op5mized for tasks to be performed (design engineering, ﬁnancial analysis) • Requirements of knowledge work systems Produced by Dr. Brian Janz 11 Knowledge Work Systems • Examples of knowledge work systems – CAD (computer‐aided design): Automates crea5on and revision of engineering or architectural designs, using computers and sophis5cated graphics soTware – Virtual reality systems: SoTware and special hardware to simulate real‐life environments • E.g. 3‐D medical modeling for surgeons • VRML: Speciﬁca5ons for interac5ve, 3D modeling over Internet – Investment worksta5ons: Streamline investment process and consolidate internal, external data for brokers, traders, porkolio managers Chapter 11: Managing Knowledge and Collabora5on Part 3: Intelligent Techniques for Managing Knowledge Produced by Dr. Brian Janz 12 Intelligent Techniques • Intelligent techniques: Used to capture individual and collec5ve knowledge and to extend knowledge base – To capture tacit knowledge: Expert systems, case‐based reasoning, fuzzy logic – Knowledge discovery: Neural networks and data mining – Genera5ng solu5ons to complex problems: Gene5c algorithms – Automa5ng tasks: Intelligent agents • Ar5ﬁcial intelligence (AI) technology: – Computer‐based systems that emulate human behavior Intelligent Techniques: Expert Systems • Capture tacit knowledge in very speciﬁc and limited domain of human exper5se • Capture knowledge of skilled employees as set of rules in soTware system that can be used by others in organiza5on • Typically perform limited tasks that may take a few minutes or hours, e.g.: – Diagnosing malfunc5oning machine – Determining whether to grant credit for loan Produced by Dr. Brian Janz 13 How Expert Systems Work – Knowledge base: Set of hundreds or thousands of rules – Inference engine: Strategy used to search knowledge base • Forward chaining: Inference engine begins with informa5on entered by user and searches knowledge base to arrive at conclusion • Backward chaining: Begins with hypothesis and asks user ques5ons un5l hypothesis is conﬁrmed or disproved Successful Expert Systems • Countrywide Funding Corpora5on in Pasadena, California, uses expert system to improve decisions about gran5ng loans • Con‐Way Transporta5on built expert system to automate and op5mize planning of overnight shipment routes for na5onwide freight‐trucking business • Most expert systems deal with problems of classiﬁca5on – Have rela5vely few alterna5ve outcomes – Possible outcomes are known in advance • Many expert systems require large, lengthy, and expensive development and maintenance eﬀorts – Hiring or training more experts may be less expensive Produced by Dr. Brian Janz 14 Intelligent Techniques: Case‐Based Reasoning (CBR) • Descrip5ons of past experiences of human specialists, represented as cases, stored in knowledge base • System searches for stored cases with problem characteris5cs similar to new one, ﬁnds closest ﬁt, and applies solu5ons of old case to new case • Successful and unsuccessful applica5ons are grouped with case • Stores organiza5onal intelligence: Knowledge base is con5nuously expanded and reﬁned by users • CBR found in – Medical diagnos5c systems – Customer support How Case‐Based Reasoning Works Produced by Dr. Brian Janz 15 Intelligent Techniques: Fuzzy Logic Systems • Rule‐based technology that represents imprecision used in linguis5c categories (e.g., “cold,” “cool”) that represent range of values • Describe a par5cular phenomenon or process linguis5cally and then represent that descrip5on in a small number of ﬂexible rules • Provides solu5ons to problems requiring exper5se that is diﬃcult to represent with IF‐THEN rules – Autofocus in cameras – Detec5ng possible medical fraud – Sendai’s subway system use of fuzzy logic controls to accelerate smoothly Intelligent Techniques: Neural Networks • Find paOerns and rela5onships in massive amounts of data that are too complicated for human to analyze • “Learn” paOerns by searching for rela5onships, building models, and correc5ng over and over again model’s own mistakes • Humans “train” network by feeding it data inputs for which outputs are known, to help neural network learn solu5on by example • Used in medicine, science, and business for problems in paOern classiﬁca5on, predic5on, ﬁnancial analysis, and control and op5miza5on • Machine learning: Related AI technology allowing computers to learn by extrac5ng informa5on using computa5on and sta5s5cal methods Produced by Dr. Brian Janz 16 How a Neural Network Works Intelligent Techniques: Gene5c Algorithms • Useful for ﬁnding op5mal solu5on for speciﬁc problem by examining very large number of possible solu5ons for that problem • Conceptually based on process of evolu5on – Search among solu5on variables by changing and reorganizing component parts using processes such as inheritance, muta5on, and selec5on • Used in op5miza5on problems (minimiza5on of costs, eﬃcient scheduling, op5mal jet engine design) in which hundreds or thousands of variables exist • Able to evaluate many solu5on alterna5ves quickly Produced by Dr. Brian Janz 17 Intelligent Techniques: Intelligent Agents • Work in background to carry out speciﬁc, repe55ve, and predictable tasks for user, process, or soTware applica5on • Use limited built‐in or learned knowledge base to accomplish tasks or make decisions on user’s behalf – Dele5ng junk e‐mail – Finding cheapest airfare • Agent‐based modeling applica5ons: – Systems of autonomous agents – Model behavior of consumers, stock markets, and supply chains; used to predict spread of epidemics Produced by Dr. Brian Janz 18 ...
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- Spring '11