KNOWLEDGE MANAGEMENT SUMMER 2012 Andrew Basden KM@basden.demon.co.uk Phone: +44/0 1928 734 308 CONTENTS 1. INTRODUCTION TO KNOWLEDGE MANAGEMENT? 2. WHY IS KM IMPORTANT? 3. ASPECTS OF HUMAN FUNCTIONING AND KNOWING 4. WHAT IS KNOWLEDGE? 5. KNOWLEDGE MANAGEMENT IN THE ORGANISATION 6. KNOWLEDGE IN DATABASES, KNOWLEDGE BASES, ARCHIVES AND FEEDS 6. OVERVIEW REFERENCES USED IN TEXT SOME LITERATURE: BOOKS AND PAPERS GLOSSARY AND ABBREVIATIONS NOTE: {*** Pieces in curly brackets and/or with asterisks indicate work that the student should do. They are advice rather than required work, but if you want to pass and get good marks, then I advise you to take notice of all these and do what they advise you to do. If you have any problems when doing them, then email me at the address above. ***} 1. INTRODUCTION TO KNOWLEDGE MANAGEMENT In this module, we learn about knowledge management (KM). Knowledge is increasingly important in today's businesses and organisations. Some commentators suggest that the world has gone through several major revolutions: » the agricultural revolution, when humanity first learned, thousands of years ago, to tame and use animals and sow crops; » the industrial revolution, when humanity first learned, three hundred years ago, to use fossil-fuel energy as a resource; » the information revolution, when humanity began treating information as a resource; » the knowledge revolution, when humanity began treating knowledge as a resource. Whether we agree with such an over-simplified picture or not, we must acknowledge that knowledge has become much more important to today's organisations since the 1990s: The amount of knowledge generated and transferred in one week in 2007 is greater than that amount during a person's whole lifetime in the 18th century. Peter Drucker, business guru, has said [1994]: "Knowledge has become the key resource, for a nation's military strength as well as for its economic strength ... is fundamentally different from the traditional key resources of the economist - land, labor, and even capital ... we need systematic work on the quality of knowledge and the productivity of knowledge ... the performance capacity, if not the survival, of any organization in the knowledge society will come increasingly to depend on those two factors." He says that the forces that make this so include: competition, culture, technology, worldview, fashion. Drucker treats knowledge as a resource. Is it valid, and even helpful, to do so? Should we treat knowledge primarily as a resource to be 'managed'? Or will that lead us into problems later on? There are signs that treating knowledge as a resource does lead to various problems, so we will take a rather wider view here. For example, 65% of corporate intranets fall into disuse in between 1-2 years [KPMG, Parfby, 2006]. However we will begin with the resource- oriented view, which is the most common. To understand why Drucker treated knowledge as a resource, we need to understand the business culture into which he wrote. The business culture of the time thought in terms of resources, which they assumed were things like labour, land and capital; it was these, they assumed, which determined the success of an enterprise or organisation. Drucker and others believed they needed to be persuaded that knowledge is also important, especially for innovation and in gaining competitive edge, so he tried to show that knowledge is as important as the other resources. We will start with the resource view but go beyond the resource view. 1.1 What is KM? What is Knowledge Management? First, we will treat it as a resource, and then open up later. Loosely, as a resource, we might see it as "doing what is needed to get the most our of knowledge resources." But that does not tell us much and, as we shall see, is limited to one view of knowledge and KM. Skyrme [1999] defines KM as: "Knowledge management is the explicit and systematic management of vital knowledge and associated processes of creation, organization, diffusion, use and exploitation." Notice some important words there: » 'explicit' - knowledge is explicitly recognised » 'systematic' - knowledge is too important to be left to chance » 'vital' - KM focuses on what is important » 'processes' - KM speaks of a dynamic, active situation in the organisation, not just a storing of knowledge; in this way, KM differs from management of information or data. The main processes that are recognised in KM include: » creation or generation of knowledge - for example by research, thinking or discussion that stimulates » organisation, codification and storing of knowledge - in some kind of database or knowledge repository » sharing or diffusion or dissemination of knowledge - so that all those who need the knowledge have access to it » using, capitalising on, or exploition of the knowledge - so it brings benefit, rather than just sit in people's heads or discussions. KM involves: » people » processes » information » information technology. A knowledge management system is: » social and structural mechanisms for promoting any or all of the processes above » information technology to support those mechanisms. 2. WHY IS KNOWLEDGE MANAGEMENT IMPORTANT? 2.1 The Importance of KM Knowledge management is important today because: » Information. Most of our work (in Western societies at least) revolves around information. Example: {*** Is this true of other cultures? Discuss. ***} » Complexity. Products and services are increasingly complex. To produce them, maintain them or use them requires more and more knowledge. To gain and have this knowledge available, it must be managed. Example: » Life-long learning. Increasingly (in Western societies at least) staff in organisations are expected to keep on learning. Knowledge must be available for this. To ensure it is available, it must be managed. Example: » Organisations compete on the basis of knowledge. All organisations have some knowledge. But the organisation that uses its knowledge better is more likely to succeed. So the knowledge must be managed. Example: » Staff reductions. Early retirement and staff moving (and pressures to down-size staffing levels) means that the knowledge those people had is lost to the organisation. So it needs to be captured before the staff leave. Example: » Time pressures. The amount of time available for gaining experience and acquiring knowledge is diminished. So knowledge must not be wasted (reinventing the wheel). So it must be managed. Example: In brief, knowledge and information have become the medium in which business problems occur. Consequently, managing knowledge offers a primary opportunity for achieving substantial savings, improvement in human performance and competitive advantage. 2.2 The Knowledge Organisation So the notion of the knowledge organisation has become popular. The knowledge organisation is an organisation that values the knowledge its members possess above all, and in which the way it works is governed by the needs of knowledge management. Here are some management-level slogans about it: » Its people think actively rather than passively, and think ahead rather than behind. » Its senior management actively consider knowledge as well as markets, products, brands, rather than leaving it to others. » It derives knowledge from many sources: product, customer, financial situation. » It always seeks to learn and improve its knowledge. » It is not content to do more of the same better, but to seek new and innovative ways of doing things that are made possible by the knowledge it possesses. These slogans are shallow, but they do contain some useful insight. One important insight is that knowledge management is not just making more information available, but covers a lot more. 2.3 Views and Aspects of KM Originally, KM treated knowledge merely as an asset, as a resource to be stored, shared and exploited. But more sophisticated versions have arisen. Earl [2001] categorised seven KM approaches. Earl grouped his categories into technocratic, economic and behavioural, and then subdivided those. However, we will see them as different aspects, different ways in which knowledge and KM can be meaningful (see chapter 3). For each, we will consider: what it sees as mainly meaningful (its 'philosophy'), its focus, its aim, and the principal IT element. We will also consider strengths and weaknesses, and give examples of organisations that employ this approach. (Information from Elaine Ferneley, used with permission.) 1. Systems Approach to KM (technocratic) » Meaningful: Codify all knowledge (so it is available to others) - Lingual aspect » Focus: Technology » Aim: Knowledge bases (the knowledge itself) » Principal IT element: Knowledge-based systems » Strengths: - Explicit knowledge - Verifiable knowledge » Weaknesses: - Maintaining and updating the knowledge - Some knowledge difficult to codify (esp. tacit knowledge) - Very domain-specific - Difficult to generalise from (but see Data Mining and Case Based Reasoning) - Needs a reward mechanism to encourage people to keep knowledge uptodate » Example organisations: - Skandia database to support underwriters decision making - Airbus CD-ROMs for airplane maintenance - Dell - Access Health 2. Cartographic Approach to KM (technocratic) » Meaningful: Connect people ('yellow pages' of who knows what, so they will supply knowledge to each other) - Social aspect » Focus: Maps » Aim: Knowledge directories (who knows what) » Principal IT element: Profiles and directories (on intranet) » Strengths (esp. compared with Systems Approach): - Continuous self-editing - Cheaper than systems approach - Can benefit from tacit knowledge » Weaknesses: - How to assess the knowledge - Knowledge directories might be misused - Who regulates the regulators? » Example organisations: - McKinsey & Co: Every employee must state 3 areas of expertise - WS Atkins - inclusion of personality traits e.g. 'good negotiator' 3. Engineering Approach to KM (technocratic) » Meaningful: Capabilize workers (by giving them information and best-practice knowledge) - Formative aspect » Focus: Processes » Aim: Knowledge flows » Principal IT element: Shared databases » Strengths: - Empowered workforce - False departmental walls are broken down » Weaknesses: - Information overload - Employees can become sceptical - Information can be taken out of context, where it's not relevant (see later) » Example organisations: - Hewlett-Packard open-access databases - Story-telling 4. Commercial Approach to KM (economic) » Meaningful: Commercialize (make money from the knowledge; exploit intellectual assets) - Economic aspect » Focus: Income » Aim: Knowledge assets; the 'knowledge value chain' » Principal IT element: Register of intellectual assets, and system to process this » Strengths: - Quick win - Corporate knowledge gets included on the balance sheet » Weaknesses: - Ongoing management of the knowledge assets - Employees can feel exploited - Resistance to knowledge-sharing » Example organisations: - Dow Chemicals - exploitation of its patents portfolio (25,000 patents that cost $30m p.a. maintain, but they made only $25,000 income; they increased revenue to $125m through sales and licensing) - Cap Gemini - rented technical subcontractors to health and local authorities. 2003 revenue £87m. 5. Organisation Approach to KM ('behavioural') » Meaningful: Collaborate (use organisational structures to pool and share knowledge) - Social aspect » Focus: Networks, Communities of Practice » Aim: Knowledge pools » Principal IT element: Groupware, intranets » Strengths: - Breaks down organisational barriers - Members are there because the choose to be » Weaknesses: - Will only work if there is a tradition of sociability and networking among employees (true of BP and Ford) - Requires moderators and knowledge brokers - Danger that IT is used to regulate rather than enable collaboration » Example organisations: - BP Amoco - developed drilling platform expertise global community via Lotus Notes and video conferencing, saving $27m in one year {*** But what about the oil spill 2009-10? ***} - Ford - knowledge and best practice forums; self-regulating, anyone can join 6. Spatial Approach to KM ('behavioural') » Meaningful: Contact each other (and share knowledge informally) - Spatial and social aspects » Focus: Spaces where people meet (e.g. the 'water cooler') » Aim: Knowledge exchange » Principal IT element: Knowledge representation tools » Strengths: - People prefer conversations to documents or IT - Meet people you would not normally encounter - Informality encourages innovation » Weaknesses: - Can seem expensive (to auditors): Yahoo drank the bar dry! - Other metrics take over (auditors cannot see the financial benefit from facilities) so facilities are withdrawn - Resentment from the have-nots » Example organisations: - Yahoo's Kitchen, Bar and Bean Bag environment - British Airways cafe, shops, etc. 7. Strategic Approach to KM ('behavioural') » Meaningful: Consciousness-raising (KM as the 'what this organisation is all about': vision) - Pistic aspect » Focus: Mindset » Aim: Knowledge capabilities » Principal IT element: Eclectic (i.e. any) » Strengths: - Vision drives all else » Weaknesses: - ? » Example organisations: - Skandia: embrace all the above and view development of intellectual capital as their core mission 3. ASPECTS We have mentioned aspects as a notion to help us deal with diversity of key issues in ISD. But what are aspects? Why are they helpful in understanding knowledge and its management? We will have to use philosophy to consider this, but it should be reasonably easy to understand, since the philosophy we use is what might be called a philosophy of everyday life. It was first devised by the Dutch thinker, Herman Dooyeweerd (1894-1977), and aspects were central to his thinking. To understand knowledge, we recognise » knowers - the human beings who do the knowing, » knowings - the act or process of knowing as human functioning, » knowns - what is known. This gives us a rich view that escapes but incorporates the resource- oriented view. Each has many aspects. 3.1 What are aspects? In everyday language, the word 'aspect' has several meanings. It can be used for the facets of a jewel, or to different views of a building. But the main use in everyday language is that aspects are different ways in which a situation is meaningful or different ways of looking at things. For example we might talk about the financial and legal aspects of a business; we might talk about the biological, psychological, social, financial, ethical and religious aspects of our everyday lives. Suppose you are writing a letter. Many different kinds of question may be asked about what you are doing, such as: How many words, paragraphs, sections are written? How large a sheet of paper is being written on? Is the writing fast or slow? Might the writing (ink) fade over time? Do I write badly when ill? How do I feel while writing? Is light too dim to see what I am writing? Is it clear what I want to write about? Do I have a plan and structure? How can I best express what I want to say? What phrasing suits the intended readers? What connotations will the words carry? Do I have to keep to a word limit? Is my writing interesting or boring? Does what I say all hang together? Am I doing justice to the topic? To the readers? Do I write with goodwill and generosity? Do I believe in what I am writing? Is it important? Each question indicates an aspect of your writing activity. An aspect is a way of looking at something, a way in which things can be meaningful. (Stafleu [2003] calls aspects 'relation frames'). Analysis of any human or other activity tends to separate out aspects. Whenever we delineate a set of things that should be taken into account separately and not reduced to each other, we are separating out different aspects. Usually we do so informally, as managers do as an aid to keeping things in mind, such as the time- cost-quality triple beloved in management. When it is important to make sure our distinctions are clear and valid, we do so more formally, as Dahlbom & Mathiassen [2002,p.135] do when they distinguish three types of quality: functional, aesthetic and symbolic. Eventually thinkers devise philosophical ontologies of aspects, such as physical, chemical, biological, technical, social [Bunge, 1979]. Maslow's [1943] famous hierercny of needs can be seen as a suite of aspects. It is very natural for human beings to think aspectually, and when we do so we adopt a suite (set) of aspects. 3.2 What Aspects Are There? Dooyeweerd delineated fifteen aspects: » quantitative - to do with amount, counting of things » spatial - to do with space, spreading out in a continuous way » kinematic - to do with movement » physical - to do with energy, mass, forces, etc. » biotic / organic - to do with life functions » psychic / sensitive - to do with sensing, response, feeling, emotion » analytic - to do with distinction and clarity » formative - to do with our ability to shape things, concepts, organisations, etc., to do with achievement, goals, skills and techniques; and to do with technology and history » lingual - to do with symbolic signification: documentation, programming, etc. and providing the basis for communication » social - inter-personal relationships, roles in social institutions and structures, and respect between people » economic - to do with frugality, resources, and management of these, including of course money and time » aesthetic - to do with harmony (as in music), play, fun, interest, enjoyment, art, etc. » juridical - to do with 'what is due' to all, and legal rules and enforcement » ethical - to do with self-giving, generosity, beyond what is due » faith / pistic - to do with beliefs, vision, commitment. Later are two tables with more details of the aspects, to which you can refer during the module. {*** To Learn and Prepare for Assessment: Please study and get to know these aspects right from today, because they are all important during the module. Each gives you a key issue in KM. Try to identify in which aspects you have experience in life. Write down what you come across in a notebook or electronically and then use all this in your individual assignment. ***} Each of these is a sphere of meaning, and of law (good and bad). They are taken from the suite of aspects by the Dutch philosophy, Herman Dooyeweerd, and may be explored on: Maslow's famous hierarchy of needs is a suite of aspects. Checkland's '5 Es' (Efficiency, Effectiveness, Efficacy, Elegance, Ethicality) form a suite of aspects. There are many more. Arguably the best suite of aspects currently on offer comes from a Dutch philosopher, Herman Dooyeweerd (1894-1977). His suite is more compehensive than the others, is better thought out, and is grounded in careful philosophical thought. So this is the one we will use in this module to understand knowledge and knowledge management. To be more precise (as philosophers like to be) aspects are spheres of meaning: aspects are ways in which things can be meaningful. Aspects are ways in which human life can be worth living. Aspects are also spheres of law: aspects provide different ways in which things can be good or bad, beneficial or detrimental, successful or failure. To give attention to aspects is to recognise the diversity of everyday experience at work and home. To say aspects are spheres of meaning is to say that each aspect centres on some kind of kernel meaning (or central meaning), but is surrounded by a lot more - sometimes it is called a constellation of meaning. For example the kernel (or centre) of the psychological aspect is feeling, sensing, responding - but around this are lots of different kinds of feeling such as feeling of achievement, feeling of beauty, feeling of justice or injustice, and so on. Aspects are also spheres of law - which is to say: ways in which life can be Good and Bad, beneficial or detrimental. For example, in the biotic (biological) aspect, health is good, disease is bad, in the social aspect, friendship and respect are good, enmity and disrespect are bad, in the economic aspect, prosperity and carefulness are good, poverty and waste are bad. {Link with Other Thinkers. Many other thinkers have recognised this diversity and discussed aspects, though they usually called them 'levels' (Newell, 1982), 'system levels' (Bunge, 1979), 'strata' (Hartmann, 1984). Dooyeweerd went deeper and also his suite of aspects is much wider than theirs. That is why we use Dooyeweerd's aspects here. For a comparison of Dooyeweerd's aspects with these and others, see .} DOOYEWEERD'S ASPECTS: SPHERES OF MEANING Aspect: Properties Some Things To do with (Examples) (Examples) MATHEMATICAL ASPECTS Quantitative aspect Much, few Numbers, Ratios Discrete amount. More, less Fractions Spatial aspect Near, far Shapes, Overlaps Continuous extension. In/outside Dimensions Kinematic aspect Fast, slow Path/Route Flowing movement. PRE-HUMAN ASPECTS Physical aspect Heavy, Hard Atoms; Causes Fields, Energy, mass. Hot, Soluble Solids, liquids, gases Biotic/organic aspect Healthy, ill Organism, body, organs Life functions, organism Old, young Food, Species Sensitive/psychic Blind, deaf Stimulus, response Sensing, feeling and emotion. Afraid, happy Memory, Neurone HUMAN ASPECTS Analytical aspect Clear, confused Categories Distinction, concepts (In)consistent Abstraction, logic Logical Formative aspect Skilled, lazy Plans, goals Deliberate shaping, Structured Techniques, control Technology, skill, history Out-of-date Technology Lingual aspect Understandable Languages, vocabulary Symbolic signification. Phrases, paragraphs SOCIAL ASPECTS Social aspect Polite, rude Friends Relationships, roles Condescending Organisations Economic aspect Careful, Cost, Budget Frugality, resources; Spendthrift Money Management Excessive Waste Aesthetic aspect Delicate, ugly Art, fashion, Games Harmony, delight Funny, boring Nuance, allusion Stylish SOCIETAL ASPECTS Juridical aspect Appropriate Law, policy, contract 'Due'; Rights, responsibilities Just, unjust, Legal Police, judges Ethical aspect Generous, mean Gift, Covenant Self-giving love; Attitude Selfish Love Pistic/Faith aspect Divine God, idol Faith, belief, commitment Faithful Belief, ideology Vision of who we are Certain Hope, Dignity DOOYEWEERD'S ASPECTS: SPHERES OF LAW Aspect: Functioning Example Repercussions To do with Good/ / Bad Benefit / Detriment MATHEMATICAL ASPECTS Quantitative aspect Being-amount Numeric order Discrete amount. Spatial aspect Spreading Simultaneity Continuous extension. Kinematic aspect Moving Dynamism Flowing movement. PRE-HUMAN ASPECTS Physical aspect Causality Persistence Fields, Energy, mass. Biotic/organic aspect Living Health, Growth, Reprod. Life functions, organism Ill-health, Disease Sensitive/psychic Sensitivity Interaction with world Sensing, feeling and emotion. Sensory deprivation HUMAN ASPECTS Analytical aspect Distinction Clarity Distinction, concepts Blurring Confusion Abstraction, logic Formative aspect Industry Achievement Deliberate shaping, Laziness Mess Technology, skill, history Lingual aspect Truth-saying Understanding Symbolic signification. Deceit Misunderstanding SOCIAL ASPECTS Social aspect Respect Friendship; Organisations Relationships, roles Hostility Enmity Economic aspect Frugality Prosperity Frugality, resources; Profligacy Superfluity / destitution Management Aesthetic aspect Harmonising Beauty, Fun, Interest Harmony, delight Frenzy Grotesqueness, Boredom SOCIETAL ASPECTS Juridical aspect Responsibility Just society 'Due': Rights, responsibilities Oppression Injustice Ethical aspect Generosity Goodwill Self-giving love; Attitude Selfishness, Greed Defensiveness Pistic/Faith aspect Faithfulness, courage Trust, Dignity Faith, commitment, belief; Disloyalty Distrust Vision of who we are Idolatry, (fashion?) Decline 3.3 Knowers, Knowings and Knowns Knowers are human beings and, perhaps, organisations (see later). These function in all aspects, and indeed it is our functioning in all aspects that constitutes our very living. Knowing. Consider your own knowing of something. It is probably true to say that, from your course you know something about, for example, research methods. That is, you can tell an examiner about the three paradigms (positivist, interpretivist, critical), and about various methods like questionnaire, interview, field trial, and so on. But it is also true to say that you know how to write. However, your knowledge of writing is not something that you can tell an examiner, or anyone else. Also, you know your friend, but you probably cannot tell anyone about it. You know your mother's voice. You know ... many things in many different ways. Adam [1998,p.180] writes of "a number of aspects of knowing" to differentiate embodied skill from propositional knowledge. Dooyeweerd provides an explanation for this, by saying there are many aspects of knowing. Or, to put it a different way, many ways or types of knowing. Here are some of them: » biotic / organic knowing - When a plant in a room bends towards the light, it can be said to 'know' where the light is. » psychic / sensitive knowing - When we recognise or remember a tune or a face, but perhaps cannot identify it clearly, this is a psychic aspect of knowing. Psychic knowing is the kind of knowing that animals engage in: instinct. » analytic knowing - When we form concepts, identify things, think about them, that is analytic knowing. » formative knowing - When we develop a skill, that is formative knowing. » lingual knowing - When we write something down as a records that is the lingual way of knowing. When we call a library a collection of knowledge, we are referring to the lingual aspect or knowledge. When we talk about 'a body of knowledge', such as in a library or a number of journals, we often mean lingual knowledge. » social knowing - When we share background assumptions with others, especially those that are specific to our own family or culture, that is part of the social aspect of knowing. » economic knowing - When we manage our knowledge, being careful about what we let ourselves know, that is economic way of knowing. » aesthetic knowing - A sense of the whole that is more than the sum of the parts is an aesthetic way of knowing. » juridical knowing - A sense of the rightness or appropriateness of knowledge is a juridical way of knowing. » ethical knowing - When we open ourselves to new experience without reservation, that might be an ethical (self-giving) aspect of knowing. » faith / pistic knowing - When we are reasonably certain of something, that is pistic aspect of knowing. {*** Discuss. Knowing that there are three main paradigms of IS is analytic knowing. What are the aspects related to all the others in the paragraph earlier? ***} There is also aspects of what is known. For example, we can know analytically about many topics, such as food or literature. We can also develop skills in many topics, such as eating or writing. A library holds knowledge of many topics, such as gastronomy and literature. A community shares background assumptions about many topics, such as what foods or literature are deemed worthy. And so on. Those topics can be of any aspects; food is of the biotic aspect, literature of the lingual aspect. This applies to KM. KM is one complex part of human life. The key idea on which this module is based is that if we function well in all the aspects, then KM will go well, but if we function poorly in any aspect, then KM might go badly. The aspects, as spheres of meaning, help us separate out what it is meaningful to focus on in trying to achieve good KM - aspects help us see the key issues in knowledge management. {*** Look up: http://www.dooy.info/shalom.html ***} 3.5 Irreducibility, Dependency, Coherence The aspects are irreducibly distinct from each other, such that the meaning and laws of one aspect can never be explained in terms of others without damaging it. This is called irreducibility or sphere sovereignty. But they also relate to each other, such that good functioning in one aspect depends on good functioning in earlier aspects. This is called foundational dependency. For example: » To function well socially, we usually need to communicate well, i.e. function well lingually. » To function well lingually, we need to construct our sentences well, which is formative functioning. » To construct our sentences well, we need to be clear about what we want to say or write, which is analytic functioning. » To do this, we need to see and/or hear, and our brains need to function satisfactorily, which is psychic functioning. » and so on. Now we begin to use the aspects to help us identify and think about key issues in information systems development. Each aspect tells us of several different key issues. There is also anticipatory dependency, which refers to the fact that usually no aspectual functioning is done just for its own sake, but is for the sake of others, especially those later than is. For example: » We function lingually in order to function socially. This gives a coherence to the aspects. It was crucial to Dooyeweerd that the aspects are not just distinct categories, but that all work together. {*** If you want to find out more about this, read Chapter III of Basden (2008), Philosophical Frameworks for Understanding Information Systems, available in library. ***} 4. KNOWLEDGE? Knowledge management is management of - what? If we are to manage knowledge, we need to understand it. According to Collins [1993] knowledge can be: » Embrained knowledge: Knowledge that is dependent on conceptual skills and cognitive abilities. Example: this list. » Embodied knowledge: Knowledge that expresses itself in action and contextual practices. Example: You read, so you must know how to read. » Embedded knowledge: Knowledge that resides within systematic routines, roles and the structures within which we live and work. Example: the 30 mph speed limit embeds knowledge that driving fast in a built-up area is dangerous. » Encoded knowledge: Knowledge that is conveyed in signs and symbols (books, manuals, data bases, etc.). » Encultured knowledge: Knowledge that is shared understandings. Example: in UK we read from left to right. This is helpful, but does not go far enough. {*** Discuss. Can you see which aspect is most important in each of the above views about knowledge? If so, what does this imply about Collins' view? Can you expand it into other aspects? ***} (Notice: The use of alliteration - each thing beginning with the same letter or sound - is an aid to memory. This is very useful for managers, who believe they must be given quick-and-easy ways to remember things. So, if you expand it into other aspects, why not try to find suitable words starting with 'En' or 'Em'? For example might pistic knowledge (certainty) be called 'Emphatic knowledge'?) In this chapter, we look at what the field of KM has discovered about knowledge: » Types of knowledge » Knowledge in individuals » Knowledge in organisations » Knowledge in documents, databases, archives. 4.1 Types of Knowledge 4.1.1 Procedural, declarative and prescriptive knowledge Procedural knowledge is knowledge of how to do things and what to do. Declarative knowledge is knowledge about things. Sometimes called descriptive knowledge. Prescriptive (normative) knowledge is knowledge of good and evil, knowing what is good and bad. 4.1.2 Theoretical and pre-theoretical attitudes In the theoretical attitude, we stand back from a situation, focus on one aspect of it, and separate that aspect from others and from the situation, to obtain theoretical understanding. Theoretical attitude is 'head knowledge'. It gives primacy to analytic knowing over all the other modes or ways or aspects of knowing, and indeed often arrogantly ignores the others completely. In the pre-theoretical attitude, we engage with the situation, 'mucking in', and learning as we go, and using our knowledge to achieve things. Pre-theoretical attitude is 'knowing with the heart'. It is intuitive. Sometimes it is called instinct, but that is better reserved for psychic knowing. The early Greeks believed that theoretical knowing was the 'true' or 'best' route to knowledge, and disdained pre-theoretical knowing. 4.2 Knowledge in Individuals It used to be said that "knowledge is justified true belief" - in order to distinguish 'knowledge' from mere opinion. This definition might be useful in the biological sciences but does not work for business knowledge that is relevant to KM. Because much of the most important business knowledge cannot be justified. Example: The President of Sony had a hunch that a portable cassette-tape player would be a sellable product. His advisors gave many reasons why it would not work, but he pushed it through - and the Sony Walkman was born. People would carry it while walking, listening to music. After that, of course, the iPod and MP3 players became popular, replacing the Walkman - but only because the Walkman had created the appetite for listening to music while walking. The knowledge that the President had was never 'justified true belief', but hunch. That is one reason why the traditional definition is not useful. Another is tacit knowledge. 4.2.1 Tacit versus explicit knowledge Tacit knowledge became important when Michael Polanyi pointed out that "We know more than we can tell." Polanyi M, (1967), The Tacit Dimension, Routledge and Kegan Paul, London U.K. Michael Polanyi is, perhaps, the 'father' of the idea of tacit knowledge. Tacit knowledge is knowledge that is not explicit. Or knowledge that cannot be made explicit. Examples are: » muscular knowledge, such as how to ride a bicycle » knowledge that is difficult to explain, because it was gathered via experience and has not yet been made general, for example knowledge of how you keep your child happy when s/he is frustrated » knowledge that is difficult to explain, because it was learned a long time ago and has been buried deep in one's memory, e.g. knowledge of how you learned to write reasonable text » knowledge that is taken for granted, so that when asked about something, your don't think about telling this knowledge, even though it is important. For example, in a technical field, the social or faith aspects are often taken for granted. Explicit knowledge is knowledge that you can talk or write about. It is knowledge that you can, therefore, give to others by such talking and writing. Explicit knowledge can be communicated. In an organisation, most knowledge management relies on knowledge being explicit - though the Knowledge Directory approach and some Social approaches can cope with knowledge that is not explicit: tacit knowledge. According to Boisot [1999], there are three types of tacit knowledge: » Things that are not said because everyone can understand them and takes them for granted. » Things that are not said because no one can fully understand them, and » Things that are not said because, even though few people understand them they cannot costlessly articulate them. 4.2.2 Expertise, Understanding and Experience Knowledge management wants expert knowledge. What is an expert? What is expertise? An expert: » masters the requisite knowledge; can solve problems well » contributes makes better decisions, and contributes better to decision making by others » is consulted for their expertise; can explain » knows what is important and what is not, so can be selective in information: c.f. aspects as ways of being meaningful or important » can benefit from experience. An expert has both experience and understanding: » A garage technician might have experience of certain types of car, and can tell what certain noises mean, but might not understand why the engine makes that noise. Experience is linked with skills. Experience enables us to solve problems in the domain, and to do so efficiently in contexts we are used to. Experience is the answer to "How?" and "What?" » A recent graduate in mechanical engineering might have understanding of why certain types of noise emerge, but cannot yet apply that knowledge in practice. Understanding is the answer to "Why?" Understanding enables us to give explanations of the domain, to make sense of any new experience we obtain, and to tackle new kinds of problems. It can be used in new contexts. NOTE: Understanding can include scientific theory, but it is not restricted to that. It can also include 'informal' understanding. » An expert has both experience and understanding and can use them working together. That is the best kind of knowledge for KM. The expert can both solve problems efficiently, give explanations, tackle new kinds of problems and work in new contexts. In KM all three are important: experience, understanding and linking them together. Experience on its own is little use because you don't know whether it applies in a different context or not. And you cannot use it for new types of problem nor to create new ideas. Understanding (especially 'book knowledge') is little use because it is too general. The link between Experience and Understanding is as follows, and will be important when we consider knowledge acquisition: Understanding can be informed from experience enriches through theory formation. Experience (E) may be seen as understanding (U) applied by a process of context-depending problem-solving (CPS): E = U + CPS CPS involves three things that are specific, unique to the situation: » person » context » problem-solving strategy. These three make experience very specific, and difficult to generalise from, or to apply to new contexts, by different people or when using a different problem-solving strategy. To find U from E one must separate out the elements of CPS. For more on this see: Basden, Watson & Brandon 1995 ('Client Centred'), or Attarwala & Basden (1985). Wisdom is ability to employ both kinds of knowledge appropriately in many situations. Wisdom means taking account of many aspects. Two types in Greek thought: » Sophia: wide-ranging head knowledge » Phronesis: acting well in all situations, in a wide range of aspects. Understanding v. experience might seem like theoretical v. pre- theoretical knowing, but not quite. Two types of both: Types of understanding: » Theoretical understanding: Theories. Tries to be precise. But only in one aspect. » Pre-theoretical understanding: Intuition. Is not precise, but is broad and multi-aspectual. Intuitive grasp of the meaning and goodness of aspects. e.g. we understand to some extent that justice is important, and grasp what justice is, but can never define it. Types of experience: » Theoretical experience: Methods and instructions in books, to which we refer to solve problems or do things. Instruction manuals. » Pre-theoretical experience: When we do not need to refer to manuals, but do things well without thinking about it. 4.2.3 Learning Learning is the gaining of knowledge. There are many ways of learning, and many theories of learning. The lecture will briefly touch on learning. Full learning involves not just learning by rote, or head knowledge, but much deeper knowledge. Let us consider the following aspects of learning, or having learned: » Biotic learning: Our muscles and other tissues have developed through practice; the neurons in our brains have grown dendrons to make links. » Sensitive / psychic learning: Instinct; trained reflexes, responses and feelings. We form long-term memories. » Analytical learning: Conceptualising; having clearly distinct ideas, which we reflect upon. » Formative learning: Having developed skills, e.g. being able to fix a car, to wire an electric plug safely, to write, to listen, to sing. » Lingual learning: Lingual learning is when we gain knowledge through what we hear or read - that is, explicit knowledge. » Social learning: Knowing people well; also knowing what kinds of things it is appropriate to say in each situation and to each type of person; developing a shared background understanding within a culture. » Economic learning: Knowing what is needed and what is not; an expert is selective, not wasteful, and knows what to leave out. » Aesthetic learning: Harmonising what we know and learn - making it coherent with all else we know - is an aesthetic aspect of learning. » Juridical learning: Knowing what is appropriate and what is inappropriate. Warrant and justification. » Ethical learning: (Not sure about this one: maybe giving of self to what is known, or loving knowledge, but that does not feel correct.) » Pistic learning: Becoming certain, believing, valuing it, so that we put our trust in knowledge. Often what is called 'knowledge' is just the analytic aspect of knowledge: conceptual knowledge. Explicit knowledge is analytical knowing, transcribed into lingual form. Most of the rest are tacti. 4.3 Knowledge in/of the organisation What does it mean for an organisation to have knowledge? An organisation does not have a brain, so it cannot know in the way individuals know. It can mean either or both of the following: » There are people within the organisation who possess certain knowledge or expertise, which is useful to, and used within, the organisation. » The organisation possesses archives or databases in which knowledge has been written. » The organisation possesses data that can be 'mined' to discover knowledge. 4.3.1 Tacit and Explicit knowledge in organisations Tacit and explicit take on different meanings when applied to knowledge of an organisation than when applied to knowledge of an individual. » In the individual, explicit means the individual can tell or explain the knowledge, and usually knows they possess that knowledge, while tacit knowledge means that the individual cannot tell it and probably does not know they possess it. » In the organisation, explicit knowledge is what the organisation knows it has, that is, has a record of; so knowledge written into archives is explicit, and so are directories of people who possess certain knowledge. In the organisation, tacit knowledge is knowledge that the organisation does not know it possesses. It is knowledge that some people possess, but the organisation does not know that. Knowledge that is tacit to the organisation might be either explicit or tacit to the individual: that is, the individual might know they have the knowledge and be able to tell it, but the organisation does not know they have it. See 'Tacit Knowledge in Organisations' [Baumard, 1999]. 4.3.2 Sharing Knowledge in an Organisation For knowledge in the organisation to be any use, it must be shared. Sometimes this sharing is called 'knowledge transfer'. Remember that 'knowledge' does not just mean understanding, but it also means experience, and how the two work together as expertise and wisdom. Here are some examples of knowledge sharing: » X writes to Y explaining something. » X writes a report, manual, audio tape or video that explains something, and places it in the archive or library. Y reads it and learns from it. » X tells a story about something that happened, from which Y learns. » X and Y chat about something, and during which Y learns something. » Y overhears X saying something, and learns. » X demonstrates to Y how to do something. » X trains Y in doing it. » Y observes X doing it, and learns, even though X might not be aware of it. Notice how the first two in each group are formal and deliberate, and the last in each group is accidental or informal. Accidental knowledge sharing is very important, and is made more likely by the Spatial Approach to knowledge management. Why is knowledge-sharing important? This is especially important for tacit knowledge. Different people have knowledge in different areas, so by sharing the knowledge with other people in the organisation they can gain more knowledge, other people learn something, the organization can know what assets they have and what to do in future. Knowledge sharing helps to improve the ability of staff and contribute to the formation of teams' creativity and spirit of cooperation. It can stimulate innovation. It is also important when new staff arrive, and when staff who have knowledge leave. The problem is how to encourage all members in an organization to share and exchange knowledge. There are some barriers, with some suggestions for what to do to encourage knowledge-sharing. » The most obvious reason is that people don't realise that the knowledge they possess might be helpful to others. This might be because of misplaced modesty, or just lack of thinking. Especially this is so when the other people are in a different situation. » Some employees might fear that sharing their knowledge will make them less uniquely valuable to the organisation (Bennett, 2004) - but that fear is often overplayed and too 'obvious' because does not the uniqueness of people lie in their character and personality and attitude as much as in their specialist knowledge? However, if the attitude that pervades the organisation is one of harshness and competitiveness then people might have that fear. So one important facilitator of knowledge sharing is to engender a generous attitude throughout the organisation, whereby all employees feel valued, rather than a competitive one in which employees feel they have to prove themselves against others. (This is ethical aspect of knowledge sahring.) » Lack of trust between the knowledge recipients and providers (Davenport & Prusak 1998). People might fear that others will take wrong advantage of their knowledge, either by undermining them, or by passing of the knowledge as their own. Again, attitude is important, the ethical aspect. » Another barrier is lack of motivation to share. Those with knowledge to share lack time, because of high workload. A remedy for this is to ensure lower workloads - but how? » People will not share knowledge unless they can articulate it well. Tacit knowledge is very difficult to articulate, though it can be demonstrated. (Lingual aspect of knowledge-sharing.) » Differences between people. For good knowledge sharing, people need to possess common understandings. They must see the same kinds of thing as meaningful and important (share similar vision and view of life: pistic aspect) and have shared background knowledge (lifeworld: social aspect of knowledge sharing). » Organizational structure can either encourage or discourage knowledge sharing. If staff cannot break out of their positions, they will not share knowledge, but if they are encouraged to cross departmental boundaries they are more likely to. (A social aspect of knowledge sharing.) There are also problems associated with the recipient: » Another is lack of knowledge absorption capacity. Some people resist strange things. (This is an aesthetic aspect of knowledge- sharing.) » Pride in the recipient, so that they do not like to ask advice but believe they can do things without new knowledge, also hinders knowledge-sharing. }*** In 2011, Prachi Saxena investigated aspects of barriers to knowledge sharing. She found most aspects gave different barriers. See if you can see why each of the above barriers are important, in terms of the aspects. ***} 4.3.3 The SECI Model The challenge to knowledge-sharing is that some of this knowledge is tacit within individuals. So it is not easily shared, because it has not been made explicit. Nonaka & Takeuchi [1995] suggest there are four stages in knowledge-sharing, which in fact make a cycle that continues round and round in a good knowledge organisation: the SECI model: Socialization, Externalization, Combination, Internalization: » Socialization: Tacit-Tacit. Transferring tacit knowledge in one person to tacit knowledge in other person. For example apprentices learn by working with their mentors by observing, practicing and doing the same thing which mentors do, not through communication or language. Children learn basic things by observing their parents/ guardians not through language. » Externalization: Tacit-Explicit. Making tacit knowledge explicit or process among individuals within a group. One case is expressing one's own tacit knowledge like ideas or images in words. The other case is extracting and translating tacit knowledge of others into explicit, understandable form. For example, I can recognize my friend's face; this is usually cannot be articulated, but expressing through language is called externalization. It can be done by expressing her features. » Combination: Explicit-Explicit. Combination is conversion of explicit knowledge into more complex sets of explicit knowledge. Reconfiguring of existing information through the sorting, adding, re-contextualizing of explicit knowledge will lead to new knowledge. For example, students have their own knowledge and will gain some knowledge from professor's lectures and will add this knowledge to their own knowledge and combine this knowledge will become a new knowledge. » Internalization: Internalization is defined as converting explicit knowledge into tacit knowledge and is somewhat closely related to "learning by doing". For example, getting the explicit knowledge by an individual through reading, listening, and observation is converted into tacit knowledge. 4.3.4 Communities of Practice A community of thought is a group of people that share similar thoughts about a topic. They discuss the topic together, and develop it. They might be local or spread throughout the world. Example: those who discuss Dooyeweerd's philosophy. A community of practice is a community of thought, but more. It includes practice as well as thinking: A CoP is an association of people who share a practice that involves knowledge and thinking and putting that into practice, and learn from each other, and develop the knowledge and practice. CoP is very important in KM. It was Etienne Wenger [1998] who first introduced the idea of community of practice: "Members of a community are informally bound by what they do together - From engaging in lunchtime discussion to solving difficult problems - and by what they have learned through their mutual engagement in these activities, A community of practice is thus different from a community of interest or a geographical community, neither of which implies a shared practice." A Community of practice defines itself along three dimensions; » "What it is about: It's Joint enterprise as understood and continually renegotiated by its members." » "How it functions: Mutual engagement that binds members together into a social entity." » "What capability it has produced: Shared repertoire of communal resources (routines, sensibilities, artifacts and vocabulary styles) that members have developed over time". A CoP has four 'dimensions'. » The organisational dimension, has to do with the role of COP within and between the organisation where it exists. » The cognitive dimension, deals with the particular knowledge domain and practice. » The economic dimension, has to do with the benefit rate, costs, and economic performance. » The technological dimension, which deals with the technological enablement. "A community can be made up of tens or even hundreds of people, but typically it has core participants whose passion for the topic energizes the community and who provides intellectual and social leadership" [Wegner & Snyder 1999]. In a CoP, knowledge is shared freely. The members of a CoP share a common goal. They learn together. "Communities of practice also move through stages of development characterized by different levels of interaction among members and different kinds of activities." [Wenger 1998]. Knowledge can be explicitly learnt from individuals who hold tacit knowledge because of the opportunity of socializing and interacting with such person. A CoP involves loyalty to the group, or else it will gradually fizzle out. According to Wenger (1998), "doing whatever it takes to make a mutual engagement possible is an essential component of any practice". People within a CoP share their knowledge in a free flowing way that leads to new approaches to problems being developed. CoPs result in "solving problems, generating new lines of business, developing people's professional skill, promotes the spread of best practices as well helping companies recruit and retain talents" [Wenger & Snyder, 1999]. "A community of practice involves much more than the technical knowledge or skill associated with undertaking some task. Members are involved in a set of relationships over time and communities develop around things that matters to people", [Wegner 1998]. A CoP is not a formal organisation, nor is it merely a network. It is something between the two. "A community of practice is different from a team in that shared learning and interest of its members are what keeps it together. It is defined by knowledge rather than by task and exists because participation add value to its members" [Wegner 1998]. CoP is "a group of people informally bound together by shared expertise and passion for a joint enterprise" [Wenger & Snyder 1999]. Example: engineers who engage in deep water drilling or consultants who specialize in strategic marketing etc. CoPs tend to cross organisational boundaries. In any knowledge organisation, some of its members will belong to a CoP but that CoP will also involve members of other organisations. For example: Corrosion scientists in ICI form a CoP with corrosion scientists all over the world. They attend conferences together, meet each other, etc. CoPs have always been in existence; for example metal workers ,potters etc., but it is a different issue today because "instead of it being composed primarily of people working on their own, they often exist within large organizations" [Wenger & Snyder, 1999]. "Communities of practice exist in any organization, because membership is based on participation rather than on official status, these communities are not bound by organizational affiliations, they can span institutional structures and hierarchies" [Wenger 1998]. 5 KNOWLEDGE IN DATABASES, KNOWLEDGE BASES, ARCHIVES, FEEDS At the centre of knowledge management is knowledge in lingual form, that is knowledge that has been expressed or stored on some persistent medium. Some is within the organisation, other is outwith it. This includes knowledge held in: » books and libraries » videos, films » web sites » databases » knowledge based systems » feeds from outside » reports » and many more. To understand management of such knowledge, we need to understand: » Data, information and knowledge » Unstructured storage of knowledge (text, videos, sound) » Structured storage (representation) of knowledge » Data warehousing » Data mining, report mining, etc. 5.1 Data, information and knowledge The difference between data, information and knowledge is most marked for people who are searching databases etc. They seem to start with data, then it seems to become information, then it seems to become knowledge. Many articles and chapters have been written about this. For example, Alavi and Leidner [2001,p.109] suggest "data is raw numbers and facts, information is processed data, and knowledge is authenticated information". Checkland and Holwell [1998] review a number of views to suggest an extra link: capta. Then "the attribution of meaning in context converts capta into something different, for which another word is appropriate: the word 'information'" [p.90]. Information then contributes to "larger-scale, slower-moving knowledge". ('Capta' was actually used by Langefors [1966] to denote something different: what is 'captured' from perceptions, and which then becomes information.) Some then try to add 'wisdom' as above knowledge. What Actually happens with Data, Information, Knowledge? But is this a true picture? Surely, what seems to be 'mere data' in a database actually already refers to something even before the recipient begins accessing the database. In fact, it is already knowledge. There is no such thing as 'mere data' that is not information or knowledge. We must realise that data is nothing 'in itself': it did not just 'happen'. It got there by human functioning, and it is then searched and used by human functioning. Consider first how the 'data' got there. Example: a patient database used by nurses and doctors, with details of treatment and state of patients. A nurse examines a patient and their drips etc., and so knows something about the patient. She uses this knowledge to enter information of how the patient is progressing, rate of drip, current blood pressure, and various information of a qualitative kind. She does not enter all she knows about the patient, but only that which she believes is relevant and necessary. The information is stored in the database as values of attributes or fields, and in that state we might consider it data. Data is raw pieces of information, such as numbers (which happen to be blood pressure readings). But the data began with knowledge, and in fact 'is' knowledge. Suppose later on a medical researcher or manager obtains such data (without names etc.) for many patients. To them, they have numbers. They then begin processing them in ways that are appropriate to the type of information. Example: Blood pressure readings. These are pairs of figures, diastolic and systolic readings, with the second higher than the first. - An appropriate way to process them: plot the first figures over time per patient, and the second figures, and this would show whether the BP was going up, down, or peaking, and when it peaked. - An inappropriate way to process them: add together all the figures, both first and second. That would be meaningless. So processing of data to get information already presupposes we know what the data refers to and how to process it appropriately. Knowledge would come when the time of peak BP was compared with other information about patient, especially events like taking a particular drug, having exercise, having an operation. So the database is rather like a book: one person writes it, another person reads it. Whether writer or reader, all data is also information is also knowledge. Aspects of Data, Information, Knowledge A better way of understand the difference is that data, information and knowledge (and capta) are different aspects of the same thing: » Pscyhic aspect: 'Bits' and 'states' refer to Shannon's and Bar- Hilell's views of what they call 'information', to differentiate it from its biotic or physical medium. They concern pre- conceptualised bits, with signals and signal paths. On screen this is pixels, on paper this is visible deliberate marks, and heard, this is sound. This is 'capta'. » Analytic aspect: 'Data' may usefully refer to what we have called raw pieces of data. » Formative aspect: 'Information' may usefully refer to data as part of something which has been processed or structured. » Lingual aspect: 'Knowledge' may usefully refer to what the information is about. (Note: 'knowledge' here is used in a narrower way than the multi-aspectual knowledge of the individual above.) » Social aspect: Connotations and cultural implications of the 'knowledge'. We will call it all 'knowledge', but recognise that it is stored as information, data and capta, as three other aspects of it. 'Wisdom' may be added; refer to our taking all aspects into account when we consider what the information is about [De Raadt, 1991]. Deriving New Knowledge Take the example of patient data, including BP readings. » What nurse knows: the state of the patient there and then. » What researcher comes to know: how patient BP peaks when some event happens. The first is of interest to nurses, but not to researcher. The second is of interest to researcher, but maybe not to nurses (though nurse might find it useful knowledge, and indeed might have already guessed there is some correlation). In KM, such inferences happen, especially in data mining. 5.2 Data Mining and Researching Data The above example is simple researching of data. But when this becomes large, and especially automated, it is called data mining. The database is seen as a mine from which raw materials can be 'dug out'. Data mining is the process of extracting patterns from data. In the above example the pattern is how the rise and fall of BP matches that of the event history of the patient. And then that type of pattern for many kinds of patients are combined to provide a new overall pattern, such as that a particular event raises BP in 70% of cases. In KM, in organisations, the patterns of interest are unlikely to be BP and medical events, but things like rise and fall in stock markets, linked with political events, legal events, weather events, wars, takeovers, etc. And rise and fall in the organisation's parameters such as profits, morale among staff, etc. Other examples: » supermarket sales data linked with loyalty cards » airline passenger trip data » census data. Critical relationships are sought in the data. Patterns are sought, and then correlations with other patterns. How Data Mining Works Preparation » Decide what is meaningful to you. What kinds of things do you wish to find out? What kinds of relationships are you looking for? For example relationship company profits and a small number of other factors. [NB aspects are ways of being meaningful, but they have not been used in data mining yet. That is an opportunity for research.] » Assemble your target data set. The data of interest is likely to be far too voluminous to be processed in reasonable time. So you must select a subset of the data. » Clean your data set. That is, find which data are inaccurate, incomplete, irrelevant, and then remove, replace or modify that data. Detect where data are missing. Or data might be duplicated. Statistical methods can be used to find some of the anomalies. Knowledge of how the data was generated is crucial in data cleaning. For example, if the data is typed in, then some errors are because of mis-keying, so taking account of the properties of keyboards can help (e.g. neighbouring keys, or sometimes finger 'bounces' on key, entering it twice). Detection of duplicate data is not always easy. » Convert the cleaned data into feature vectors, which are records with fields showing things of interest. Example: from the mass of blood pressure data (in the example above), find where the peaks are in diastolic and systolic measurements, and where the peaks are in the difference between diastolic and systolic. This gives three possible peaks, and the times of these three peaks make up the BP feature vector. Example: photograph of face, in which you are interested in the eyes: use an algorithm (or even human interaction) to find position, size, colour of eyes, then store that data in the feature vectors, rather than the photograph itself. » Divide the set of feature vectors into two sets. One is used to train the data mining algorithms and find possible patterns, the other is the test set that is in order to test the patterns found. Data Mining for Patterns » Now data mining itself starts. The following methods are not steps, but methods one can use as necessary. They are applied to the training set. » Cluster the data. Using the training set, look for groups of vectors that are 'similar' in some way. Make sure this process is not contaminated by your expectations of what groups might be found. Neural nets and other methods can be used for this. » Classification. This is when groups are found in the data by using your own expectations about what is important. » Regression. This tries to find a model or graph that fits the data, to explain it and find relationships. For example, plot the BP peaks against events in the patients' lives. » Association discovery. This also tries to find relationships. Amazon, for example, tells prospective buyers of a book what other books people buy that buy that one; this information can be obtained through association discovery. Validation » Validation of patterns. This tests whether the patterns found in the training set are valid in the test set. This removes patterns that are erroneous. » If the possible patterns found in the mining are not found in the test set, then the data mining must be re-done with different parameters. » Once patterns have been found in the training set that are also in the test set, then these can be considered valid. These valid patterns are then interpreted. For example, if it is found that BP peaks after meals with potatoes, then one must decide what to do about meals with potatoes. Problems with Data Mining » The source data might not be in useful formats. See below. » The subsets of data might not show the patterns and critical relationships that occur in the whole data. » Data cleansing might remove the 'interesting' data, which indicates the critical relationships. Often critical relationships are 'unusual', so data associated with them might seem unusual and cleansing algorithms might assume they are errors. » Converting to feature vectors: this is very dependent on what features you decide to place in the feature vectors. For example, in the BP, we have assumed that peaks are the features of interest, but in fact it might be troughs that are of more interest, or times when the BP is rising or dropping fastest. So a lot depends on what we deem meaningful. [Aspects should be able to help here, but the research has not been done. It would be a good project for a PhD, to research how Dooyeweerd's aspects can assist the quality of data mining.] Spatial and photographic data is very difficult to mine, because converting to feature vectors is highly dependent on what one chooses as features. » Splitting into training and test sets. If the training set is too small, then the algorithms are not well trained and will miss important patterns. If the test set is too small erroneous patterns won't be removed. » Clustering, regression and association discovery can go wrong with too small a training set. » Classification can go wrong if you use misleading or meaningless classifications. » Testing can go wrong if the test set is too small, or if it is not selected properly. » Spatial data tends to be huge, especially geospatial data. » The main challenge to data mining is the volume of data to be mined. The time it takes is proportional to the square, cube or fourth power of the number of pieces of data, because the algorithms usually work by looking at each piece of data in turn and, in doing so, they compare it with all the other pieces of data, perhaps several times and in several ways. Variants on Data Mining We have used as an example the detection of patterns in blood pressure and patient events. But other kinds of data mining also occur, including: » finding patterns in behaviour of individuals (e.g. terrorists) » finding relationships to an individual » spatial data mining, involving spatially referenced data such as from GPS, from location-sensitive devices like iPhones. Variants on data mining include: » report mining » web mining » screen scraping » and more. Report mining is similar to data mining, but geared to looking at text found in reports. Web mining is similar to data mining, but its data is web pages. The beauty of this is that the HTML tags often give useful information that can guide the searches. For example header and title tags usually indicate headings that have been carefully worded to express something important, so the web mining algorithm might pay more attention to it. The pre-processing and classifying might involve finding all the pieces of text bracketed with HTML tags. Screen scraping is similar to data mining in which the data comes from screens. This is often useful for connecting with legacy systems, especially those from the 1970s, the user interfaces of which are character graphics - the characters are captured and analysed by data mining techniques. The pre-processing can involve converting a linear stream of characters into lines and columns, and screenfuls (often such UIs were 24 lines of 80 characters). But for more modern, graphics oriented screens, the data is not characters but bitmaps: pixels. This requires much more processing, with time- consuming algorithms that do visual pattern recognition and optical character recognition. Now we look at data storage formats. 5.3 Knowledge Representation and Data Storage Formats # Key question: How do we represent our knowledge in symbolic form? Two main ways: » Formal languages and information structures that express a way of seeing the world ('ontologies') » Informal: text, graphics, video, sound, etc. (e.g. the web) Formal languages and structures can be more reliably searched, to find most that is relevant and exclude most that is irrelevant. But this way is usually cumbersome and even insufficient for ill- structured knowledge, especially that which includes a lot of taken- for-granted assumptions or which requires interpretation. Informal means, such as the Web, can more easily enable ill- structured knowledge to be stored. Taken-for-granted assumptions are what we expect in free-format text and pictures, and humankind is well-used to this. But free-format storage cannot be so easily searched, and you either miss a lot that is relevant, or get a lot that is irrelevant (c.f. google searches). 5.3.1 Structured storage (representation) of knowledge For example: » Tables » Relational databases and SQL » Object orientation » Semantic nets Example: Sports club membership. Only club members may use the facilities, not least because insurance only applies to them. The data of club membership is held in simple database tables. The following fields are held: FIELDNAME: WHAT IT REFERS TO NAME: Member's name ADDR: Address DOB: Data or birth SEX: Sex DAPP: Data of application for membership RECM: Who recommended as member APPS: Stage in process of applying to become a member START: Membership start date YEARS: Number of years a member CURR: Whether currently a member (Y or N) This is a simple structure (only one table). The idea of a formal language. This set of fields provides a very simple 'language' in which to express the fact that someone is, or is applying to be, a member. For example, the following 'sentence' tells us that a certain person is a member: (This is similar to XML format.) Now consider the following challenges. All are concerned with applying the data, but the issues raised affect storage decisions. Challenge 1: Operational/tactical search of DB: You want to send out a mailshot to all members over age of 60; can you easily do this? And what might be the problems in doing this? {*** Exercise and Discussion This can be done with a simple SQL search. What SQL query would you make? Problems: a) Look at the address in the example above: is that suitable for a mailshot? b) What about possible errors in DOB? c) Try to think of other problems. Discuss: To what extent does this include most who are relevant, and exclude most who are irrelevant? This is hardly KM; it is little more than data handling. ***} Challenge 2: Strategic search of DB: You want to know whether the club has been more, or less, attractive to those over 60, increasing or decreasing over the past 10 years. How would you do this? {*** Exercise: More difficult but still possible with SQL search. Think about how you would judge whether over 60 in each of last ten years. This is more like real KM, but is rather crude as such. ***} Challenge 3: Deduced knowledge: You want to find out how attractive the club has been to people from non-British cultures. How would you do this? And what are possible problems? {*** Exercise and Discussion: Even more difficult, but still possible. Hint: think about surnames. An investigation of surnames would probably have to be done manually, with people considering each, and asking whether a British name or not. Problems. One obvious problem is: does surname really indicate whether British or not. Another is: what do we mean be 'British' - those who are British citizens, those who have lived in UK for a time, or those who 'think' who British way, or what? This is more like real KM. It illustrates how KM requires 'judgment'. This is also an example of how you might want to use the data in ways you did not anticipate at first. ***} Challenge 4: A nearby hotel wants to negotiate short-term membership for its guests. Is this database sufficient to hold information? Or how would the structure need to be changed in order to allow this? {*** Exercise and Discussion: Think about why temporary membership might require different treatment. For example, if you just added hotel-members, your list of members would get clogged up with hotel-members, and for example mailshots would go to lots of irrelevant people. So you need a way of distinguishing 'real' from 'hotel' members. The current set of fields does not allow this. What fields might you need to add to the database? Discuss: Suppose it is impossible to change the database structure. How would you cope with the request from the hotel? e.g. could you keep paper records of hotel members? Note: The latter is part of knowledge application. In this way, you can see that the four activities of KM cannot be divorced from each other. ***} Challenge 5: You are expanding. You want to put in a range of facilities that might be of interest only to certain members. You want, therefore, to provide a way of holding information about this. {*** Exercise and Discussion: Discuss: For what reasons might you need to hold such information? The reasons indicate what kinds of information you need to hold. For example: a simple 'TypeOfInterest' flag, or a free-form description, or a flag for each facility to say whether they are allowed and/or likely to use it, ... For the latter, it is better not to use individual flags in the main record [Think: Why not?]. Better to set up two new tables: » Facilities table: recording what facilities are available (and incidentally this allows to store much more information about the facilities, such as insurance conditions, maintenance costs, etc. » Allowance table: Holds primary keys of a member and a facility. An entry in this table shows that that member can use that facility. [Think: Or would it be better to have entries for which members are NOT allowed to use which facilities?] Problem: What about long-term existing members? Discuss: How to fill up the Allowance table to allow which members to use which facilities? The message here: KM involves expanding the knowledge available from time to time. Doing so raises storage problems. ***} Challenge 6: Now you want to hold information about the various facilities, such as invoices for purchase and maintenance, and maintenance logs. {*** Exercise and Discussion: DIscuss: How would you hold these in a way that their data fields can be searched? (i.e. merely holding scanned pictures of paper invoices etc. is not an option.) Problem: different invoices and logs have very different formats. ***} As we can see, formal structures make some searching easy, some searching possible, but also makes it difficult to use the information to inform us in ways not anticipated before, and to expand the information or knowledge held. Both these are needed. {*** Discuss: The structure was a simple table, of relational form. Would the searches and changes above have been easier if the database had been in object-oriented form? ***} During 1980s, 1990s, a lot of research went into finding better structures for information, and better languages in which to express information. XML became increasingly popular as a language. Ontologies # The need to categorise knowledge [Lecture 14] » taxonomies to separate things out e.g. Dewey Decimal » Taxonomies can help web searching » multi-level: need way of separating things out at whichever level we are » these tend to be hierarchical » challenge: how something can be in several categories; e.g. library coding with ':' Difference between Language and Structure The above example shows a simple structure but the sentence to set up a member was in XML, which can deal with complex structures. » The structure determines how easy it is to make sophisticated searches and to modify the types of knowledge held. » The language determines how easy it is to express the knowledge entered, and to express the kinds of searching needed. Whatever the language, the structure of the above presupposed distinct variables or attributes, each of which can be identified by name and given a value. The above methods focus on the analytic aspect of information: the ability to distinguish clearly between the different objects and attributes. But some knowledge is not amenable to such treatment, so free-format methods have become popular. 5.3.2 Unstructured storage of knowledge (text, videos, sound) From the 1990s, the World Wide Web has provided an alternative way of holding a wide variety of information. Its success is that it focused not on the analytic aspect (distinguishable attributes) but on the lingual aspect of information or knowledge, i.e. what it means. To enter this knowledge, people would write it rather than design it. HTML is an excellent language in which to store such information, because: » It allows free-format text. » But it enables the author to indicate what they think are special parts of the text by means of 'markup' tags, such as headings, bullets, emphasis, citation, etc. » It provides a table mechanism for information that is more structured. » Hyperlinks enable lots of relationships between 'entities' that were not thought of beforehand. » It also allows inclusion of graphics and other media alongside text. » (If your information is linked to Internet, then that link gives extra benefits - but you might hold on an internal-only WWW, an Intranet.) » And many more. In this way, it allows free text but enables us to bring some structure to it. Searching becomes more of a challenge, because you are not sure what information is where, and in what words it is expressed. Types of search: » Lexical: Search for combinations of words (maybe allowing a bit for mis-spelling). Problem: can miss lots of relevant stuff e.g. because wrong words used. Also can get a lot of irrelevant stuff. e.g. searching on 'nirvana' will get a lot about Buddhism and a lot about a rock band of that name; Usually you want one or the other, not both. » Syntactic, Structural (e.g. google search): Take account of the structure of the information, such as the number of hyperlinks from other pages to this one. This can get more irrelevant stuff, but it can present them in a more intelligent order, i.e. things hopefully more relevant first. » Semantic: Search for things of similar meaning. This is very useful when you are not sure of the words that authors might use for a particular concept. For example, I might search for "climate change deniers", but want also "climate change sceptics", "global warming sceptics", etc. For example, to get stuff on the Nirvana rock band, you can say "Search on 'nirvana' but exclude anything to do with Buddhism." » Pragmatic (not yet in full operation): Pragmatics refers to the fact that what a person means when they say or write something usually contains 'illocutionary' meaning in addition to the semantic meaning. » For example climate change deniers do not like the term 'deniers' because it sounds too like "holocaust deniers", which is a term of abuse, so they prefer to call themselves "climate change sceptics". So, whether a text uses the term 'deniers' or 'sceptics' is often an indication of which stance the author takes. » Another example: Idioms like "Were you born in a tunnel?" Such connotations and idioms are part of the 'pragmatics' of language, and can provide useful information. In fact, a wide range of terms have connotations and idioms. {*** Consider each of the challenges above and think about how each might be met using free text with HTML. » Challenge 1: finding names and addresses of all members over 60 [more difficult unless written carefully in a single structure] » Challenge 2: looking at whether the club has become more attractive to over-60s over the last ten years [much more difficult] » Challenge 3: looking at how attractive the club has been to non-British people [about the same difficulty, because the kinds of surname processing done is the same] » Challenge 4: Adding information necessary for hotel- members [easy: just write it] » Challenge 5: Allowing for different facilities and which members can use them [much easier to add the information but maybe laborious if you need to update every existing member] » Challenge 6: Can easily add scanned invoices etc. and get them converted to character text by OCR. 5.3.3 Spatially referenced data Much business information is spatially referenced; that is, it is related to positions, such as geographical positions. For example, people sometimes have to travel to a meeting, so you arrange somewhere convenient to all participants (arranging meetings is important in KM!). In conventional databases and knowledge bases, the knowledge is referenced by primary keys or identification numbers (anaaytic aspect), or by links from other knowledge (formative aspect) or by text matching (lingual aspect), or by who knows it (social aspect). But it must also be spatially referenced (spatial and kinematic aspects). This is supplied by the technology of Geographic Information Systems, which became established as a technology of its own during the 1990s, but is increasingly being integrated with knowledge management systems. Google Maps and Google Earth are examples that are very widely used. {*** Research idea for Dissertation: Research how KM systems increasingly employ spatial reference. ***} 5.4 Data Warehousing Suppose you wanted answers to the following questions about a certain customer (Thanks to wikipedia 24 April 2010 for this list, and for some of the other information below): » How much did we sell to customer? » How much time did sales people spend on the customer? » Was the customer happy with the service or goods we provided? » Did the customer pay their bills? » How have these customers fared over last few years? The information to answer these questions is all over the place, in different databases held by different departments - sales, customer relations, orders, etc. It is usually difficult to bring them together. Also, different databases might give conflicting information; for example, sales and finance departments might have different views of how much a certain customer contributes to the organisation, based on different information: which is correct? KM storage is concerned with how to integrate all the knowledge and information that is held. One way to do this is to use a data warehouse. A data warehouse is a repository of the organisation's information, held in an integrated, consistent and consolidated form. The importance of data warehouses is that the organisation needs data that is consolidated and integrated, and which can be relied on. When considering the topic of data warehousing, we must not only think about storage and organisation of information (this section), but also extraction and transormation, aggregation, for sake of analysis (sections on knowledge sharing and application). Data warehouses are good for analysis of performance against goals, analysis of trends (e.g. which products fared best over last few years), and exception reports. Data warehouses, therefore, often receive data from all the other databases in the organisation (and elsewhere too), and store it in a way that is consistent (see aspects of challenges to integration below). There are two main approaches to designing a data warehouse: » bottom up (Gary Kimball) - different 'data marts' are formed from data that is actually needed by the business processes, and after a time these marts are merged. Problem: shambles. » top down (Bill Inmon) - the entire data warehouse is designed, according to a top-level enterprise data model of the organisation. So the warehouse fits the stated aims of the organisation and its ways of working. The data warehouse is a 'corporate information factory' that delivers business intelligence. Top-down design has proven more robust when the business changes. But it can be inflexible when departments in the organisation change. Often hybrid versions occur. Data warehouses are optimised for speed and for analysing data. For this reason, data warehouses often hold data in denormalised form (the opposite of a relational DB that has many tables). Advantages: » Providews common data model, which makes it easlier to analyse. » Inconsistencies between information held elsewhere are detected and resolved. » It can act as an archive of the other systems in the organisations, espeically when one might be closed down and its data lost. » dAnalysis of information does not slow down the operations systems. Disadvantages: » Not so good with unstructured information: see 'free text' information below. » The information inthe data warehouse might be out of data, because of the need to copy data from other sources. » High costs, especially up front. » Duplicate data. 6. OVERVIEW OF KNOWLEDGE MANAGEMENT (Here are some notes that you might find useful. They are not yet complete; you can complete them.) » Knowledge management in an organisation is ... - making best use of the knowledge as asset - creation, codification, sharing, use of the knowledge held by the organisation - encouraging sharing of knowledge » Knowledge held by the organisation - in its individuals - in its databases, documentation, archives - elsewhere - Affected by knowledge of communities and society » Knowledge in individuals - What is Knowing? - Knowing what? Does the variety make any difference? - How do individuals gain knowledge? - How do individuals use knowledge? » What is knowing? » Knowledge of the community of thought / practice - Lifeworld, WVs, Ground-motives, » Sharing of knowledge - SECI » Learning » CoPs - Creation of knowledge - Critical discourse » Tacit versus explicit knowledge » Understanding versus experience: Understanding, skills, wisdom » Theoretical and pre-theoretical attitudes » Procedural, declarative knowledge » Descriptive and prescriptive (normative) knowledge » Data, information, knowledge; » Processes in KM. - Knowledge acquisition - Knowledge visualization » Knowledge in individuals - What is Knowing? - Knowing what? » Knowledge held by the organisation - creation, codification, sharing, use. » Knowledge of the community of thought / practice - Lifeworld, WVs, Ground-motives, Knowledge is a complex topic! LITERATURE 1. References Cited In Text Bennett, R. M. (2004) Knowledge power: intellectual property, information, and privacy. Colorado, Lynne Rienner Publishers, Inc. Baumard P (1999) Tacit Knowledge in Organisations, London: Sage. Boisot, M. H. 1999, Knowledge Assets: Securing Competitive Advantage in the Information Economy, Oxford University Press, Oxford. Davenport, T. H., Prusak, L. (1998) Working knowledge: how organizations manage what they know. Harvard Business School Press. Nonaka, I., & Takeuchi, H. (1995). The knowledge creating company. Oxford: Oxford University Press. Polanyi M, (1967), The Tacit Dimension, Routledge and Kegan Paul, London U.K. Wenger, E. (1998). Communities of Practice. Cambridge University Press. See also Wegner,E. (1998) Communities of Practice: Learning as a Social System. System Thinkers. HYPERLINK "http://www.co-i- I.com/coil/knowledge-garden/cop/Iss.shtml". Wegner,E. & Snyder,W. (1999) Communities of practice: the Organizational Frontier. Harvard Business Review. pp.139-141. 2. Bibliography: Other Helpful Sources Bali, Rajeev K., Wickramasinghe, Nilmini; Lehaney, Brian. (2009). Knowledge Management Primer. Taylor & Francis. Collinson, C., Parcell G. (2001). Learning to Fly: Practical Lessons from one of the World's Leading Knowledge Companies. Oxford: Capstone. Has a view of KM that might be different in some ways. Very readable. The company is BP - so see if you can detect why BP went wrong in 2009! 3. Acknowledgements Thanks are due to the following previous students, whose studies contributed to some of the material and references on knowledge-sharing and communities of practice: Srilatha Attuluri, Olufunke Adebiyi, Tony Tang, Raghuma Vakiti, Manikiranchowdary Katragadda, Angela Adili, Prachi Saxena. Andrew Basden 6 June 2012