KM activites, concepts and roles


One way to describe KM is to list the activites, tasks, competencies, roles and tools in this domain.

An ontology is a way of seeing the world, an Ontology for KM tools needs to start from the heart of knowledge management, i.e. knowledge related practices, it needs to accommodate knowledge types and forms and take account of key knowledge objects & assets An ontology can then be the basis for a tool selection decision.




KM technology ontology links:


Starting from technology, will deliver a product of limited usefullness. For a technology driven ontology see: Alex Goodall 1999 Technologies of Knowledge Management: The technologies covered include:

Agents, Bayesian Nets, Business Rules, Case-Based Reasoning, Constraint Solving, Data Mining, Intranets, Evolutionary Computing, Fuzzy Systems Induction, Knowledge Systems, Languages, Model-Based Reasoning, Groupware Ontologies, Text Processing, Natural Language, Neural Networks, Voice Recognition, Creativity Software

DM - I found their categorisation/profiling lists to be quite interesting too ... trying to do similar things to that which we have done already in WCSN. I am planning on contacting Alex later in the week to talk about collaboration possibilities.

A common way to approach KM tools is to consider two main classes: static publications and active conversations or transactions. This also leads to a push vs. pull mentality yielding little value as ideally both are required in some form or another.

Ruggles, 1997 follows a different breakdown, i.e., generation, codification and transfer which we find to be more useful. http://www.businessinnovation.ey.com/mko/html/toolsrr.html




Key knowledge practices

  • (1)Structuring: linking, classification, indexing, patterns, best practices
  • (2)Sharing: deep dialog, distinctions
  • (3)Creating: inquiry, reflection, synthesis
  • (4)Mapping: boundary objects, flows, dynamics
  • (5)Mining: text and numbers, rules, heuristics
  • (6)Learning: individual & group
  • (7)Storing: corporate memory,
  • (8)Acquiring: searching, elicitation

DM - This grouping sounds somewhat analagous of the Davenport/De Beer article in Sloan (Winter 97 I think) where the authors gave 8 differing categories of KM projects. I later abstracted that lot into a series of slides. Hmnnn - makes me think I should review my existing material on KM and revisit the Ontology. I had not thought of that as a way of categorising the technologies themselves.

Key knowledge types

  • (A)Best practices
  • (B)Lessons learned
  • (C)Heuristics: tips, tricks rules of thumb, intuition
  • (D)FAQ
  • (E)Decision rationale
  • (F)Stakeholder capital: customers, suppliers, partners, employees, owners
  • (G)Patterns

DM - The Knowledge Types categories are interesting ... when I think about how I categorised the existing content in the site I did not look into this way of thinking about the issues/cross-referencing the material. I would not have thought that this section is so relevant for categorising the technologies themselves - more the relevant perhaps for the project descriptions of the individuals.

Key knowledge objects

  • (a)Events:
  • (b)Activities: processes
  • (c)Relationships: personal, links
  • (d)Documents: forms, templates,
  • (e)Stories: cases, exemplars
  • (f)Perceptions: problems, issues, opportunities
  • (g)Profiles: directories, yellow pages

Another look at knowledge structures and activities: http://snow.utoronto.ca/Learn2/schema.htm

DM - In many ways what you have described here are the assumed types of content that we have on the site (and some we don't have). Again, the point is does this list help me categorise the underlying technological approaches of the products or is it that you see this as a way of describing the content that they manipulate, store or assist users in dealing with.

Let's take some examples and see where they shake out: our process is to look at the key knowledge practice which the technology supports first, then to see which knowledge type is targeted and finally to pick a key knowledge object. This yields a 3 level classification.


DM - Perhaps we should talk about the issues associated with categorising these technologies. Work out which ones we should concentrate upon and produce a review or two.

From the KnowledgeManagement egroups listserv post 06/27/2000 by Anand Satchit

Tool-set #1 Groupware and/or Project Management Software. Lotus Notes -or MSOutlook, an Intra-net, are critical. Look for PM softwares that link back to Notes or Outlook and that are collaborative in focus.

Tool-set #2 Document Management Software. Bringing source content through the enterprise means converting source documents into electronic form to ease storage access and transmission. Look at tools such as EDMS (Documentum), Datware II Publisher (Dataware), and Panagon (JetForm?).

Tool-set #3 Data Mining/ Cluster Identification/ Retrieval Systems. There's a wealth of product here. Verity and Excalibur are among a number of retrieval tools. There are various bayesian agents and clustering systems that attempt to scrape though the enterprise to identify kindred sets of data and documents, in an attempt to find "implicit" knowledge within the organization on a given area an individual knowledge worker is focused on. I think most of the tools assume an informed user who knows what he/she is looking for, and most of the clustering systems don't really work that well in practice.

Tool-set #4 .External Intelligence Systems I think these are key, and they ought to be wrapped right back into the corporate Intranet or Groupware. These consist of news and information aggregators News, reports, and more. The broader the reach the better. Getting useful content can be an issue here. too much stuff can be as big a problem as too little, as it can overwhelm the users. News Edge is great because they provide news and information and then peer review content to make sure its focused an appropriate plus, they add a KM consult to make sure the clients are getting the information they really need to do their jobs. Factiva is less expensive, but it requires someone in-house at the client site to own the process of pulling useful information out of the mix.

Tool-set #5 Intellectual Asset Management Systems. Software that helps track and manage the intellectual assets of an organization is fairly primitive at present, sad to say. Right now these are largely things like legal tracking systems regarding patents, and the like, usually custom and running out of some firms ERP system. Tools in this space should be expanded to systems measuring training effectiveness, and other means of valuing the intellectual assets found between the ears of the employee base. Several of the big six firms are exploring this as are some smaller guys like IXPartners out of Salem NH. NIOSH has developed an assessment model, as has various branches of the military. I'm dubious about all of them. I'd refer you to Ed Riolfi at McGraw? Hill, who has really spent a lot of time developing and assessing metrics in this area

A list of knowledge management activities. In partial answer to the question What the heck do you do in KM? Started by Denham Grey, 08/09/2001




Main activities:

  • Around technology
  • KM technology section
  • Design & manage a coporate memory repositories, documents, archives, corporate memory
  • Build Yellow Pages & [Knowledge Profiles
  • Structure & organize knowledge
  • Mine data looking for useful people, patterns, business rules, knowledge discover and knowledge mapping
  • Around people:
  • Mentor and coach knowledge practices
  • Facilitate knowledge sharing cybrarianship, conversations
  • Support communities
  • Assist with knowledge transfer AARPs, retrospects, BPs, LL
  • Around business:
  • Help with business intelligence scanning, newsfeeds, CI
  • Increase intellectual capital and quantify knowledge assets & portfolios
  • Enhance networking for knowledge exchange
  • Bootstap innovation distinctions, craft patterns & pattern languages
  • Conduct knowledge mapping & audits
  • Advise on eLearning
  • Match KM with strategy
  • Measure intellectual capital & knowledge
  • Evangelise for a knowledge focus
  • Promote & assist with change management

At a lower level

  • compile profiles
  • map concepts
  • pattern writing workshops
  • dialog faciliation
  • design of shared spaces
  • mining CS call logs
  • craft onologies & enterprize taxonomies
  • harvest insights & experience
  • construct FAQs

Collecting links on knowledge management competencies. Started by Denham Grey on 08/15/2001




Background:

Everyone is invited to edit these pages, to assist and help us write these guidelines and standards.

Links:


It applications, synthesis

  • Manging Information: achiving, bibliometrics, cataloguing, codification, content nmanagement, document management, indexing, informatics, information achitecture, information / document lifecycle, metadata, records management, taxonomies, text analysis, Thesauri

Collecting information on KM roles. Started 03/12/2000 by Denham Grey




Links


KmTechnologies

Collecting notes on KM technologies as a way to understand what is out there. Started on 08/09/2001 by Denham Grey




Key technology suites:

  • a) document / content management workflow, version control, file conversions, publishing to different media, security & access control, paper trails, search & idexing.
  • b) portals / intranets personalization, profiles, templates, access to legacy data & systems, search & navigation, content indexing, XML
  • c) knowledge exchanges / auctions personalization, payment schemes, rating systems, notification, personal profiles, category searches
  • d) collaborative and groupware asynchonous & synchronous conversations, privacy gradients, personal spaces, 'new' post notifaction.
  • e) knowledge discovery (data mining) scrubbing, visualization, analysis, statistical & qualitative models, simulation
  • f) business intelligence target profiles, push notifaction newsfeed analysis, search agents
  • g) intangible asset management capital calculators, notication, legal status indicators, alliance candidates
  • h) customer relationship management (CRM) profiles, personalization, sales heuristics, aggregation of contact and customer data, access to data in legacy systems
  • i) supply chain management (SCM) commitments, inventory levels, pert charts, notication, workflow, RFPs, EDI
  • j) intelligent agents profiles, permissions, ontologies, tracking, rules
  • k) learning management class tacking, authoring tools, assessments, class dialog, payment schemes
  • l) helpdesks and customer service escalation rules, ticket tracking, knowledge-base, FAQ, web interface, equipment diagnostics

Gathering a list of knowledge management tasks. Started by Denham Grey, 08/15/2001




Background: Tasks are the lowest level of the hierarchy task

> activity

  • practice. A task is the smallest unit of assigned work. It has a discrete & defined deliverable and is assigned to an individual or group.

KM tasks:

  • Construct a concept map
  • Extract a business rule
  • Mapping value exchanges

A collection of knowledge practices contrasting Knowledge Ecology KE (social networking aspects, people & personalization) with KM (information, codification & systems). Started by DenhamGrey 05/10/99 (he did a good job -Bart De Clercq)

One approach to working with knowledge is about using tools (including language), co-developing with technology, manipulating representations, applying inference, searching and using alternatives ways to grapple with knowledge. I hope to maintain a balance between the technological, the social aspects and introduce KE approaches to working with knowledge here.

So you wish to work with knowledge! you need a compendium of methods, tools, and resources that reflect an ecological view rather than the traditional knowledge management perspective. Together we can do more than anyone can achieve alone Your contribution & critique is welcomed.
Contrasting the KE & KM approaches to knowledge practices

  • Knowledge Discovery, mining transactional and sensing data for useful patterns. KE perspective: enhancing negotiation around meaning, relevance and applicability. KM perspective: increasing the effectiveness and efficiency of DM algorithms.
  • Knowledge Sharing, KE perspective: assisting with building shared meaning, improving understanding and increasing learning, emphasis on feedback and social negotiation. KM perspective: affording greater and faster access, supporting storage, search and publication.
  • Knowledge Innovation, cultivating, nourishing and protecting the wellspring. KE perspective: building community, collaboration and trust to increase idea exchange and learning. KM perspective: access to new and improved data sources, improved ways to store, search, diplay and distribute.
  • Knowledge Levels, looking at ways to leverage knowledge using knowledge. KE perspective, sharpen critical thinking, support deep dialogue, cultivate awareness. KM perspective: isolate knowledge claims, study evaluative criteria & process, extract rules and select most efficient method.
  • Knowledge Structuring, providing a framework to access and navigate knowledge stores, capture experiences (best practices & lessons learned) and facilitate learning. KE perspective: collaborative co-design, looking at relationships and reward structures to support use. KM perspective: choosing the most effective process and representation for effective storage, search and retrieval.
  • Stakeholder Knowledge, collect and organize a , holistic, simple, single, integrated view of your customers. KE perspective: combine and critique various views and synthesis of a composite senario. KM view: single interface, helpdesks for workflow & issue tracking, psychographics.
  • Knowledge driven inference, using domain knowledge to bound search, heuristics to shorten process and links or lessons learned to improve the likehood of success. KE perspective: sharing and culture as a means of leverage and competitive advantage, dialogue and construction in preference to automated reasoning to reduce the brittleness and ease the maintenance burden. KM perspective: choice of algorithm, machine limitations on processing & storage.
  • Support knowledge, helpdesks, customer service centers, sales assistance. Support knowledge is a valuable & critical resource. KE perspective: helping to critique and share solutions, building relationships and helping learning, preventing re-invention and doing root cause analysis. KM perspective: workflow to track and record solution process, measurements to support investments and justify costs, capturing effective solutions to reduce costs.
  • Knowledge by design, using language to bring forth a new world. Making distinctions to heighten awareness and to discriminate closely related topics in a purposeful way, languaging after Lissack. Not sure that there is a KM slant to this one.
  • Knowledge networking, applying social networking principles as a means to increase the reach and effectivity of knowledge transfer and learning. KE perspective: deliberate selection of thought leaders, 'nurturing' and 'feeding' the relationships. KM perspective: standards for knowledge exchange, protocols for communication, using the network to access remote sites and share scarce computing resources.

Information and Data Related Knowledge Practices:

  • Data Mining: text, numbers, rules, heuristics, patterns. Finding and combining data sets, cleaning and verification (data scrubbing), apply suitable algorithm, evaluate the utility.
  • Apply inference enabled extraction to activities, events, relationships, conversations, documents, stories, numbers seeking to extract patterns, concepts and speech acts. Inference may include: search, abstraction, clustering, pattern recogition, indexing and visual display...
  • Sort, search, summarize, cluster, categorize, display and link.
  • Acommodate/handle/display/manipulate different knowledge and data types (text or numbers, documents, problems, activities, events)...
  • Push, profile, anneal, annotate or publish.
  • Storing & building the corporate memory,
  • Acquiring: searching & retrieving, elicitation
  • Choosing & selecting appropraite ways to handle different knowledge representations (rules, cases, patterns, heuristics, graphs

Reflective tasks (METAPRACTICE) such as:

  • Knowledge levels
  • Competence assessment
  • Reasoning under limited time
  • Problem relaxation (solving a closely related but simpler problem if the original problem is unsolvable)
  • Combining multiple object-systems.

Cognitive and classification practices

  • Construct and move between knowledge at different levels (knowledge about knowledge)
  • Measuring alignment, innovation, commitment and consensus
  • Structuring: linking, clustering, classification, indexing, patterns, best practices
  • Mapping: boundary objects, flows, sources & dynamics KnowledgeMapping
  • Learning: individual & group
  • Identifying stakeholder capital: customers, suppliers, partners, employees, owners
  • Making meaning, annealing information (text & graphics) and developing key distinctions SeekingMeaning
  • Distinguishing a world, disclosing new worlds
  • Adjacent possible: combining worlds to create new possibilities
  • Reading the world
  • Inventing new futures through listening to human concerns

Meta practices

  • Evaluating knowledge claims
  • Collecting and categorizing best practices
  • Collecting and categorizing lessons learned
  • Collecting and categorizing heuristics: tips, tricks rules of thumb, intuition
  • Collecting,categorizing, and maintaining FAQ
  • Expressing and documenting decision rationale
  • Collecting and categorizing patterns to form a pattern language PatternPromises

Resources