08.ch8_knowledge_sharing.ppt

Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpeChapter 8Knowledge Sharing Systems:Systems that Organize and Distribute Knowledge

Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. Sharpe

Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpeChapter Objectives

To explain how knowledge sharing systems help users share their knowledge, both tacit and explicit:For tacit knowledgesystems utilized by communities of practice, particularly those that meet virtually For explicit knowledgeknowledge repositoriesTo present the different types of knowledge repositoriesTo demonstrate how sharing systems serve to organize and distribute organizational and individual knowledge


Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpeCorporate MemoryCorporate Memory (also known as an organizational memory) is made up of the aggregate intellectual assets of an organization.It is the combination of both explicit and tacit knowledge.The loss of Corporate Memory often results from a lack of appropriate technologies for the organization and exchange of documents or employee layoffs.


Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpeWhat Are KnowledgeSharing Systems?Systems that enable members of an organization to acquire tacit and explicit knowledge from each other.Knowledge markets that must attract a critical volume of knowledge seekers and knowledge owners in order to be effective.


Requirements for Set up of an Effective Knowledge MarketKnowledge owners will:want to share their knowledge with a controllable and trusted groupdecide when to share and the conditions for sharingseek a fair exchange, or reward, for sharing their knowledgeKnowledge seekers may:not be aware of all the possibilities for sharing, thus the knowledge repository will typically help them through searching and rankingwant to decide on the conditions for knowledge acquisition



Knowledge Sharing ToolsDocument ManagementElectronic storage medium with a primary storage location that affords multiple access pointsIndexing / classification taxonomyWorkflow Managementset of tools that support defining, creating, and managing the execution of workflow processesAnalysis and optimizationAudit of necessary skills and resources Replication and reuse of stored processes



Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpeRequirements for the Success of a Knowledge Sharing SystemCollection and systematic organization of information from various sourcesMinimization of up-front knowledge engineering Exploiting user feedback for maintenance and evolutionIntegration into existing environmentActive presentation of relevant information


Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpeBarriers to the Use ofKnowledge Sharing SystemsMany organizations, specifically science and engineering-oriented firms, are characterized by a culture known as the not-invented-here syndrome Organizations suffering from this syndrome tend to essentially reward employees for inventing new solutions, rather than reusing solutions developed within and outside the organizationStrong-tie networks rewarded more often than weak-tie


Reasons Why Knowledge Sharing Systems May FailIf they dont integrate humans, processes, and technologyunsuccessful if they are designed as standalone solutions outside of the process contextIf they attempt to target a monolithic organizational memorymust be both an artifact that holds its state and an artifact embedded in organizational and individual processesmust be decontextualized by the creator and recontextualized by the usermust tag an authenticity markerIf they dont measure and state their benefitsrequired of any business initiativeIf they store knowledge in textual representationsknowledge artifacts that are stored in textual format may lack the adequate representation structure, including long texts that are hard to review, read, and interpretIf users are afraid of the consequences of their contributionsprovide incentives for the employees contributions to the knowledge repository, there may be some organizational barriers that actively act against knowledge sharing If users perceive a lack of leadership support, lack an understanding of the generalities that would make their knowledge useful, or just dont feel its worth their time to make a contribution



Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpeSpecific Types of Knowledge Sharing SystemsKnowledge sharing systems are classified according to their attributes 1.Incident report databases 2.Alert systems 3.Best practices databases 4.Lessons-learned systems5. Expertise locator systems


Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpeTypes of Knowledge Repositories


Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpeLesson Learned Process


Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpePurpose of LLS: To Support Organizational ProcessesCollect the lessons:Passive, Reactive, After-Action Collection, Proactive Collection, Active Collection, Interactive CollectionVerify the lessons Store the LessonDisseminate the Lesson:Passive dissemination, Active casting, Broadcasting, Active dissemination, Proactive dissemination, Reactive disseminationApply the Lesson:Browsable, Executable, Outcome reuse


Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpeExpertise-Locator Knowledge Sharing SystemsHelp locate intellectual capitalThe main motives for seeking an expert someone who is a source of informationsomeone who can perform a given organizational functionsomeone who can perform a given social functionThe intent when developing these systems is to catalog knowledge competenciesinformation not typically captured by human resources systemscould later be queried across the organization


Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpeCharacteristics of Expertise-Locator Systems

ELS Categorization DimensionsCONNEX (HP)KSMS (NSA)SPuD (Microsoft)SAGE (FL Universities)Expert Seeker (NASA)Purpose of the systemTo share knowledge for consulting and to search for expertsTo staff projects and match positions with skillsTo compile the knowledge and competency of each employeeTo identify expert researchers within FL universities for possible research opportunitiesTo identify experts in the organization to staff projects and match positions with skillsSelf-AssessmentYesYes, supervisors also participate in data gatheringNo, supervisors rate employees performanceNo, uses funded research data as the proxy for expertiseBoth, self-assessment using competency assessment and database and Web content mining as proxy for expertiseParticipationOnly those who are willing to shareWhole personnelWhole personnel in the IT groupProfiles all researchers at universities (public and private) who are active in funded research in FLWhole personnelKnowledge TaxonomyU.S. Library of Congress; INSPEC Index; OwnDepartment of Labor (O*NET)OwnNone requiredOwn for competency assessment, none required for database and Web content miningLevels of CompetenciesNoYesYesNoYesData MaintenanceUser (nagging)User and SupervisorSupervisorFusion of universities funded research databasesOptional user maintenance for career summary and competency management, none required for database and Web content miningCompany CultureSharing, OpenTechnology, ExpertiseTechnology, OpenExpertiseTechnology, ExpertisePlatformHP-9000 Unix, Sybase, and VerityOS/2, VMS, and Programming Bourne shellSQL and MS AccessColdfusion and MS AccessColdfusion, MS Access, and multiple existing DB platforms


Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpeExpertise-Locator Knowledge Sharing SystemsGoal: to catalog knowledge competencies, including information not typically captured by human resources systems, in a way that could later be queried across the organization to help locate intellectual capital. Significant challenge in the development of ELS, knowledge repositories, and digital libraries, deals with the accurate development of knowledge taxonomies.Taxonomies, also called classification or categorization schemes, are considered to be knowledge organization systems that serve to group objects together based on a particular characteristic.


Knowledge Taxonomy at NASABecerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. Sharpe


Ontology and Knowledge TaxonomySignificant challenges to create ELSExpensiveTime-consumingComplexRequires collaborationRequires consensusPolitical concernsUsability / granularity



Ontology and Knowledge Taxonomy (continued-2)Tools to facilitate development of an ELSSemantic networksserve to structure concepts and terms in networks or webs versus the hierarchies typically used to represent taxonomies Authority fileslists of terms used to control the variant names in a particular field, and link preferred terms to nonpreferred terms control the taxonomy vocabulary Web text data miningwhat the employee knows based on what she already publishes as part of her job minimal user effort to maintain the accuracy of the records Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. Sharpe


Case Study: PostdocWeb-based document management collaboration system used by NASAUsed by three geographically distant teams of experts to collaborate in ways for which e-mail did not sufficeAllowed creation of a common vocabulary, adherence to agency security standards, source portability, and ubiquitous access using web standardsExpanded to be used by 30 NASA programs and other government agencies



Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpeCase Study: SAGEThe purpose of Searchable Answer Generating Environment (SAGE) is to create a searchable repository of university experts in the state of Florida.www.sage.fiu.edu


Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpeSAGE Architecture


Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpeTechnologies to Implement SAGE


Event Diagram for anActor Model-based ELSBecerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. Sharpe


Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpeCase Study: Expert Seeker Expert Seeker is an organizational expertise-locator KMS used to locate experts at NASAThe main difference between Expert Seeker and SAGE is that the former searches for expertise at NASA (KSC and GSFC), while the latter is on the Web and seeks expertise at various universities


Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpeExpert Seeker Architecture


POPS SystemExpands on Expert Seeker approach of reusing existing information sources with their integrationDisplays the social network between the user and the people who work on the same projects and people with the same skill sets and competenciesKnow-who systemAllows project managers to find intermediaries to talk about potential project members, their abilities, interests, qualifications Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. Sharpe


POPS System(continued-2)Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. Sharpe


Blue Reach Expert LocatorIBM needed a way to share knowledge across the multinational organizationBuilt on same time/lotus infrastructureBalance two needsExperts ability to control visibilityExperts accessibility to information seekersKnowledge taxonomyFull loggingLoad balancing on expertsBecerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. Sharpe


Blue Reach Expert Locator(continued-2)Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. Sharpe


Shortcomings of Knowledge Sharing SystemsMaking knowledge meaningful across organizationImproper contextualizationDisparate knowledge repositories/sourcesDevelop one-stop search functionalityDesign dynamic classification systemsEntice employees to find what they needBecerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. Sharpe


Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpeKM Systems to Share Tacit Knowledge To create a cultural environment that encourages the sharing of knowledge, some organizations are creating knowledge communitiesA community of practice is an organic and self-organized group of individuals who are dispersed geographically or organizationally but communicate regularly to discuss issues of mutual interest


Communities of PracticeDecreasing new employees learning curvesnew employees identify subject matter experts foster relationships with more senior employees mentor-protg relationships for career development understand the larger organizational context of their individual tasksEnabling the organization to respond faster to customer needs and inquiriesidentify experts that can address customer issuesrelevant codified knowledge can often be reusedReducing rework and preventing to reinvent the wheellocate, access, and apply existing knowledge in new situationscommon virtual workspace to store, organize, and download presentations, tools, and other valuable materialsMetadata is used to identify authors and subject matter expertscreate trust within the organization by helping individuals build reputations both as experts and for their willingness to help othersSpawning new ideas for products and servicesforum in which employees are able to share perspectives about a topicDiscussing diverse views within the community can often spark innovationprovides a safe environment where people feel comfortable about sharing their experiences



Becerra-Fernandez & Sabherwal -- Knowledge Management 2010 M.E. SharpeConclusionsIn this chapter you learned:What are knowledge sharing systemsDesign considerations for knowledge sharing systemsSpecific types of such systems: lessons learned systems, knowledge repositories, and expertise locator systemsCase studies of ELS:PostdocSAGE Expert Finder, to locate experts in FloridaExpert Seeker, to identify experts at NASA.POPBlue ReachCommunities of practice are important to share tacit knowledge


08.ch8_knowledge_sharing.ppt

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