Knowledge Mapping: An Overview Prof Elaine Ferneley

Prof Elaine Ferneley Revisiting the Definition of Knowledge Management (Skyrme’s) Knowledge Management is the explicit and Systematic management of vital knowledge - and its associated processes of creation, organisation, use & exploitation Surface assumptions, Codify what is known Don’t leave it to serendipity KM has its own tools & techniques Focus, resources are limited

Prof Elaine Ferneley Seven Strategic Levers [Skyrme, 2002]  Customer Knowledge - the most vital knowledge in most organizations  Knowledge in Processes - applying the best know-how while performing core tasks  Knowledge in Products (and Services) - smarter solutions, customized to users' needs  Knowledge in People - nurturing and harnessing brainpower, your most precious asset  Organizational Memory - drawing on lessons from the past or elsewhere in the organization  Knowledge in Relationships - deep personal knowledge that underpins successful collaboration  Knowledge Assets - measuring and managing your intellectual capital.

Prof Elaine Ferneley Practices & Processes Creating & Discovering n creativity techniques n data & text mining n knowledge elicitation n business simulation, content analysis Sharing & Learning n communities of practice, learning networks n share fairs, share best practice n cross functional teams, action reviews Organizing & Managing n knowledge centres, knowledge audits n expertise profiling, knowledge mapping n measurement of intellectual capital Our focus today

Prof Elaine Ferneley What is Knowledge Mapping nOngoing quest in an organisation (includes supply & customer chain): uDiscover knowledge location and ownership; uIdentify value and use of knowledge artefacts; uLearn roles & expertise of individuals; uIdentify constraints in flow of knowledge; uHighlight opportunities to leverage existing knowledge. nKnowledge mapping activities: uSurvey, audit and synthesis; uIdentify where knowledge is being acquired and lost; uPersonal and group competencies and proficiencies; uIdentify how knowledge flows through an organisation. nKnowledge mapping helps organisations: uAppreciate how loss of staff influences Intellectual Capital; uSelect teams uMatch technology to knowledge needs.

Prof Elaine Ferneley Key Principles of Knowledge Mapping nUnderstand that knowledge is transient; nExplain boundaries & respect personal disclosures; nRecognise knowledge comes in a variety of forms: uTacit ‘v’ explicit; uCodified ‘v’ personal; uShort ‘v’ long lifecycle. nLocate knowledge in processes, people, relationships, documents; suppliers, customers etc. nBe aware of organisational hierarchies, cultural issues, reward mechanisms, sharing & value, legal processes & protections (patents, NDAs, MoUs etc.)

Prof Elaine Ferneley What is a Knowledge Map & Why Use One ? nNavigation aid to explicit and tacit knowledge; nPortrays sources, flows, constraints and sinks of knowledge within an organisation; nEncourages re-use and prevents re-invention, saves search time; nHighlights expertise, discover communities of practice, helps staff to find critical resources; nImproves decision making, problem solving and customer response time by providing access to information; nProvides an inventory of intellectual and intangible assets; nThe start of a corporate memory or collective mind.

Prof Elaine Ferneley How & Where Should I be Looking? Active Knowledge Elicitation Techniques nFormal and informal interviews: uInterviewer asks the expert or end user questions relating to the specific topic uAdv: well known, comfortable for interviewees uDisAdv: time consuming, expensive, interviewer expertise required, interviewee cooperation required nVerbal Protocol Analysis: uExperts report thought processes involved in performing a task or solving a problem uAdv: rigorous uDisAdv: time consuming, hard to analyse nGroup Task Analysis: uA group of experts describes and discusses processes pertaining to a specific topic uAdv: multiple viewpoints, concensus building uDisAdv: how to validate nNarratives, Scenarios, Storytelling uExpert or end user constructs stories to account for a set of observations uAdv: rich insight, good for ill defined problems uDisAdv: reliance on self reports nQuestionnaires: uUsers respond to specific questions uAdv: usually quantitative, easy to code uDisAdv: low return rate, responses are difficult to validate

Prof Elaine Ferneley How & Where Should I be Looking? Active Knowledge Elicitation Techniques nFocus Groups uA group discusses different issues uAdv: allows exchange of ideas, good for generating complete lists uDisAdv: an individual may dominate, not good for discovering specific problems nWants & Needs Analysis: uUsers brainstorm about what they want/need from a system uAdv: exchange of ideas, determines areas for focus, allows prioritisation uDisAdv: wants and needs may not be realistic nObservation: uObserve users in their natural environment uAdv: see it as it really is (but not ethnography) uDisAdv: time, depends on observer note taking & observation skills nEthnographic Study: uUsers culture and work environment are studied via emersion uAdv: see it as it really is over a long time period uDisAdv: time consuming, hard to distance yourself from the domain nUser Diary uUsers record and evaluate their actions uAdv: real time (almost) tracking uDisAdv: invasive, possible delay in recording nConcept Sorting uUsers determine relationships between concepts uAdv: helps structure information uDisAdv: grouping is user specified, structure may be too elaborate

Prof Elaine Ferneley How & Where Should I be Looking? Passive Knowledge Elicitation Techniques nNews feeds: uDiscussion groups; uCompany magazines; uBulletins. nContact addresses uOrganisation charts; uHome pages. nNetwork transactions: u tags; uSemantic analysis. nHelpdesks and CRM systems: uInteraction logs; uProcess scripts. nAsset and HR databases (company CVs); nLAN directory structures: uWho has access to what; uWhy do they have access. nLibrary & record archives nProcess descriptions: uQA documents; uProcedure manuals. nMeta-data directories: uStandardisation documents; uMeta-tags on electronic data sources.

Prof Elaine Ferneley What do I do with the information? nCompile: uYellow pages/register of interests; uBest practice/lessons learnt databases; uPrototype ontology/taxonomy nIdentify: uKnowledge stewards/gatekeepers; uIsolated islands, narrow communication channels; uCritical sequences/dependencies. nExplore reuse opportunities: uAttempting to create a knowledge network of people, processes and data.

Prof Elaine Ferneley We Will Now Look at Some Specific Examples nSpreadsheets – great and simple to use, disseminate and for all to understand nCause and effect models uThe example we will use is from ISEEE

Prof Elaine Ferneley Simple Spreadsheets nExplicit model of who has what knowledge nValue of various knowledge items can be weighted nAllows transparency nEncourages people to state their knowledge and expertise nCheap and one of the most effective tools I’ve seen, everyone understands a spreadsheet

Prof Elaine Ferneley SBS Staff Expertise – figures are fictional!

Prof Elaine Ferneley Auditing Tools nTools that allow you to classify expertise, apply some sort of rating or ranking to knowledge domains; nUseful as brainstorming tools nStrongly encourage you to download Assistum: s/java/project.htm s/java/project.htm

Prof Elaine Ferneley Assistum Knowledge Editor -

Prof Elaine Ferneley STELLA Downloads.aspx

Prof Elaine Ferneley Examples

Examples

Mind Mapping – For Brainstorming, Knowledge Elicitation and Knowledge Mapping nMind Mapping is a technique developed by Tony Buzan to help individuals organise, generate and learn ideas and information nPictorial representation – detail and overview together nConsider spatial relationships and anticipate consequences nSupported by visual processing – improved recall, aids understanding nExplicit representation acts as a creativity trigger

Prof Elaine Ferneley Hand Drawn Mind Map

Prof Elaine Ferneley MindJet Mindmap

Prof Elaine Ferneley Why Mind Map Software – the Pro’s and Con’s nSupports continuous refinement nAllows variable granularity nBrings formality (validity?) to the process nIntegration with other tools nCross ref & re-assembly of elements of the knowledge base possible nSlow nHorde mentality (difficult to throw away early versions) nSemantics – in large implementations is the same vocabulary being used nCommon understanding nMaintenance – especially due to the transitory nature of the output

Prof Elaine Ferneley The Next Step nConsider further mechanisms to encourage: uRelinquishing of knowledge; uCreation of new knowledge; uBrainstorming tools; uCapturing of the brainstorming activity. nRepresenting knowledge in a highly structured database does not encourage this ….