1 This document is an earlier version of a paper subsequently published according to the following citation: Kock, N.F, McQueen, R.J. and Baker, M. (1996), Learning and Process Improvement in Knowledge Organisations: A Critical Analysis of Four Contemporary Myths, The Learning Organisation, V.3, No.1, pp.31-41. [Publisher: MCB Press, Bradford, England]
Learning and Process Improvement in Knowledge Organisations: A Critical Analysis of Four Contemporary Myths
Nereu F. Kock Jr.* Robert J. McQueen* Megan Baker** *Dept. of Management Systems, University of Waikato Private Bag 3105, Hamilton, New Zealand
[email protected] **Dept. of Business Management, Monash University PO Box 197, Caulfield East, Victoria 3145, Australia
[email protected]
ABSTRACT This paper initially discusses the concepts of knowledge, information and data. This is followed by a description of the relationship between these three concepts and organisational competitiveness. The concept of knowledge organisations is then analysed, with the focus on its reliance on knowledge workers and intense information flow. Based on the previous discussion, four contemporary myths are critically analysed. These myths are that: (1) process improvement should focus on activities; (2) process improvement should itself be a top-down process; (3) organisations should be learning systems; and (4) fragmentation should be avoided. Myths (1) and (2) are related to the business process re-engineering movement. Myths (3) and (4) come from the learning organisations movement. We argue, in this paper, that these myths are particularly deceiving and potentially dangerous due to their incompatibility with the concept of knowledge organisations and the way these organisations operate. KEYWORDS: Learning Organizations, Knowledge Organizations, Business Process Improvement, Business Process Re-engineering, Myths
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2 INTRODUCTION Knowledge, information and data are concepts that are often confused. The clarification of the distinctive nature of these three concepts is important in understanding the nature of organisational competitiveness. Organisations are becoming increasingly dependent on knowledge and information, and are moving toward what is referred to here as the "knowledge organisation" paradigm. In the next sections the concepts of knowledge, information and data are discussed, with an emphasis on their distinctive characteristics. Next the relationship between these concepts and competitiveness is discussed. Knowledge organisations are then defined. This provides a basis for the critical analysis of four myths, brought about by two contemporary management movements - business process re-engineering, and learning organisations. The four myths, discussed in the last sections of this paper, generally state that: (1) process improvement should focus on activities; (2) process improvement should be a topdown process; (3) organisations should be learning systems; and (4) fragmentation of organisations should be avoided. Myths (1) and (2) are related to the business process reengineering movement. Myths (3) and (4) come from the learning organisations movement. These myths are particularly deceiving and potentially dangerous due to their incompatibility with the concept of knowledge organisations and they way these organisations operate. KNOWLEDGE, INFORMATION AND DATA Three interrelated concepts which are often confused are knowledge, information and data. In fact these concepts have been confused by several prominent thinkers, even the ones who, themselves, pioneered the idea of knowledge and information-based organisations. Drucker[10], for example, when describing the emergence of the information-based organisation, uses the concepts of knowledge and information interchangeably: "... the business, and increasingly the government agency as well, will be knowledge-based, composed largely of specialists who direct and discipline their own performance through organized feedback from colleagues and customers. It will be an information-based organisation" (p. 207), and proceeds, in the same line: "Today's typical organization, in which knowledge tends to be concentrated in service staffs perched rather insecurely between top management and the operating people, will likely be labeled a phase, an attempt to infuse knowledge from the top rather than obtain information from below" (p. 208). 2
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Knowledge and information are not the same, nor are either of them synonymous with data. Although these concepts are interrelated and have no useful existence without each other, they are not in the same category of things. Knowledge is not composed of information, nor information is composed of data. Data storage and transfer systems, such as operating systems, cannot be confused with information systems, such as database management systems, which are at a higher systemic level. These cannot be confused with knowledge management systems, such as expert systems, either. The concepts of knowledge, information and data, and their distinction, are explored in the next three sections. These concepts form the basis on which an emergent type of organisation is based - the knowledge organisation. Data vs. Information Information can be described as data in context. The number “43”, standing by itself, is data. When some words are wrapped around the number, such as “43 cars have been sold this month”, the statement is still essentially data. However, when context is added, such as “43 cars were sold this month, compared to 29 last month, and compared to 15 a year earlier”, the statement clearly becomes information, and perhaps can form the basis for action to be taken, such as drinks all round for the sales staff. Most computer systems process data, turning data in one form into another form. Data on an order form is processed into picking lists, packing sheets, invoices, stock status reports and similar forms of data, and entries in several databases (inventory, customer accounts, etc) may be updated. Information , with potential action alternatives may be generated as a by-product of the data processing, such as “this customer is over his credit limit - should we increase the limit or cancel the order?” or “there have been no orders for product X in the last three months - should it be discontinued or the price lowered?”. Data may be considered as a convenient way to store and transfer part of what constitutes information. A person sitting with a frown on their face, arms crossed on their chest and leaning back in their chair, and saying the words “I agree” is transmitting three pieces of data. The words “I agree” have an absolute meaning when interpreted on their own, and the body position and facial expression can be coded into symbols. However, when all three pieces of data comprising words, facial expression and body position are combined with the context of who the person is speaking to, what has just been discussed, and implications of what has been discussed for the person involved, we can arrive at information about what is really meant by the data received. Therefore, the information that results about how well that person supports the proposition is only partially determined by the data transferred by words or coded symbols, and must be derived from the contextual background of the situation. Data is transferred from one user or process to another through media such as pieces of paper, data files on floppy disk, and network file transfers. Data can be stored on storage media such as printed pages, hard disks, floppy disks, tapes, and CD ROMs. These media 3
4 carry data as symbols, which in turn can assist in the transfer of information, if the contextual interpretations which can be applied to the data by sender and receiver are both known. Examples of symbol sets used to code data codes might include the English alphabet, the Chinese language and the ASCII character set. It is far more difficult to store information, as all of the context for a given situation must be wrapped around the data. This may limit its usefulness in changing contexts, so it may be much more appropriate to store the data alone, and generate information each time based on the present context. Information vs. Knowledge Knowledge is understanding about a domain. A person with knowledge about a business situation can understand the implications of incoming information, and data, and draw on that understanding to either take action or ignore the stimulus. Knowledge has an essential predictive capability. If “piece of information W” is present with “context X”, and action “Y” has been appropriate in past similar situations, then it is 80% likely that action “Y” will be appropriate in this situation unless “Z” is present. While information can be interpreted as data in context, such as "the stove is hotter than other objects in the room", knowledge allows us to predict the future (“I will burn my finger if I touch the stove”), based on information about the past or present [3]. Several different ways of expressing knowledge have been devised, among them interrelated conditional clauses and neural networks. The more traditional one, which evolved from classical logic studies, is by means of interrelated conditional clauses. A conditional clause is illustrated in Figure 1. Conditions 1, 2, 3 ... n, are the "n" facts that will trigger the "m" consequences 1, 2, 3 ... m, facts themselves ("n" and "m" are positive integers). if condition 1, condition 2, condition 3, ... condition n then consequence 1, consequence 2, consequence 3, ... consequence m Figure 1. The Conditional Clause
A whole new field of information systems study has been built around the conceptualisation of knowledge illustrated in Figure 1 - the field of expert systems. Expert system researchers and practitioners have been moderately successful in storing the knowledge of experts, such as engineers and physicians, into software systems with builtin "inference engines". The inference engine of an expert system enables it to perform activities that resemble human reasoning. Such systems may be able of, for example, suggesting solutions to engineering problems or diagnosing diseases, based on facts provided as user inputs. The modest practical success of expert systems is a result of the complexities associated with modeling and storing knowledge, and, mainly, developing
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5 efficient inference mechanisms. Usually, inference engines perform reasoning based on search methods, such as forward or backward chaining on hierarchical structures of conditional clauses - also called production rules or, simply, rules. These inference engines have been built in software that mimics certain types human expertise, such as chess expertise. However, similar attempts in other areas, such as medical and psychiatric diagnosis, have yielded controversial results [24]. The conditional clause - "if tomorrow is likely to have a temperature above thirty degrees Celsius, then we should raise the freezer control to its maximum freezing capacity to preserve the food inside", is an example of how knowledge can be expressed. It is knowledge, not information, and has both interpretative and predictive components. The weather forecast - "tomorrow the maximum high temperature has a seventy per cent chance of being above thirty degrees Celsius", is information, not knowledge. It describes an interpretation of the weather today in the context of regional weather patterns, and what the likely weather tomorrow will be. The knowledge held by the workers who operate the freezer is what enables them to set it up properly, so the food inside will not rot on days where the temperature is too high. This, in turn, is based on accurate information about present and future outside temperatures, together with understanding about how the freezer machinery will cope under those conditions. The “knowledge” can be represented as the likely consequences of a set of conditions which are used as the context to interpret incoming data and information. The “rules” incorporate the knowledge as well as a desired outcome (the contents of the freezer remain frozen). This example highlights the importance of knowledge and information in organisations. Knowledge and accurate information play an important role in the definition of organisational competitiveness. Lack of knowledge and information most invariably leads to lack of quality and productivity. The analysis of information provides the basis upon which knowledge may be generated by induction. For example, recurrent pieces of information lead to general laws - which are knowledge. These two concepts and their relationship have been extensively studied in fields like artificial intelligence, cognitive science and expertise theory. The knowledge held by a domain expert can sometimes be partially stored by rules, such as “if X is present with context Y, then take action Z”, but the rule may not completely describe the entire decision making process of the domain expert. Knowledge, represented by a set of rules, may be extracted by a knowledge engineer from a domain expert, which can then be incorporated into an expert system which can guide a person who is not a domain expert into making better decisions and interpretations of incoming information and data. Knowledge, or understanding about a domain may also be automatically extracted as a set of rules from datasets through processes known as machine learning and data mining. For example, a market research company may use machine learning to analyse the huge amounts of data generated by supermarket checkout scanners to find relationships
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6 between purchasing patterns of non-competing products, such as bananas and shampoo, and if such a relationship is found, propose advertising that will link the products and hopefully increase sales. A telephone company may use machine learning to extract rules from toll call records, and be able to target promotion of special deals to classes of customers exhibiting specific usage patterns. COMPETITIVENESS The single most important factor that ultimately defines the competitiveness of an organisation is its ability to acquire, evaluate, store, use and discard knowledge and information. It is also vitally important that the organisation spreads information to the market in order to survive and thrive. These are activities in which information technology has played, and will continue playing, a very important role. Knowledge and information must be managed in an effective and efficient way, so improvements in quality and productivity can be achieved as a result of adaptation to an increasingly changing marketplace, and the organisation is able to convey an image of excellence in its industry. Knowledge and information enable effective action. This is illustrated in Figure 2, where data (e.g. productivity figures) is interpreted by a division manager in a context (e.g. a meeting with the manager of one of his division’s plants) as information (e.g. the plant’s productivity is low). This information is, in turn, combined with knowledge (e.g. if we have a new lathe in operation, then the productivity will go up) within a domain (e.g. the plant’s assembly line), leads to effective action (e.g. introduce a new lathe into the plant’s assembly line).
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Data (e.g. productivity figures) Context (e.g. meeting with plant manager) Information (e.g. plant productivity is low) Domain (e.g. plant's assembly line)
Knowledge (e.g. if we have a new lathe in operation then productivity will go up)
Action (e.g. introduce a new lathe into the plant's assembly line)
Figure 2. Knowledge and Information Enable Effective Action
Without relevant and accurate knowledge and information, action loses its potential of creating wealth, whatever means are used to measure it, e.g. profit, investment power, shareholder dividends, excellence in the service provided to other organisations, or human well being. But this relationship can be deceiving. For example, a manager at a furniture manufacturer may wrongly believe that the secret for the company's success is to continuously build on internal knowledge about all its operations. This emphasis on continuous learning, mainly obtained from former experience, defines what is today considered, by some management scholars, the ideal organisational paradigm - the "learning organisation" [27]. In the example of the furniture manufacturer, however, the secret to success is to maximise the return on investment in knowledge and information, which often means to outsource those operations in which the organisation cannot excel. Farming out some of its operations, though, requires other types of knowledge and information, such as information about suppliers of furniture parts, freight, and accounting services, and knowledge about the best ways to deal with these suppliers. These no longer comprise knowledge and information concerning certain internal operations, which prevents former investment in learning about those operations from yielding the previously expected benefits. All traditional types of business processes, from production to marketing and sales, rely increasingly on specialised knowledge and information. The increasing specialisation of the
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8 work force is a clear sign of this. So is the increasing component of skilled labour - i.e. knowledge workers - in the final cost of sophisticated goods and services. On the other hand, as business process complexity increases, both knowledge and information needs to become more decentralised, empowering the worker closest to the activity to make appropriate decisions and take appropriate actions without consultation or direction from upper levels of the management hierarchy. This decentralisation of knowledge and information pushes management away from the coordination and decision model, towards a new approach, with emphasis on motivation and team work. This occurs simply because the old model has become inefficient and, worse, ineffective, as managers can no longer hold the specific knowledge to execute activities, nor control the information flow to accomplish them. The scientific management model, made popular by Taylor[29], no longer suits the structure of a new type of organisation - the knowledge organisation. KNOWLEDGE ORGANISATIONS Knowledge organisations have several characteristics in common. One of them, fundamental to support our argument in this paper, is that they employ a high proportion of knowledge workers. Therefore, in order to provide a basis for discussion, we define a knowledge organisation as: any organisation that has more than 50 per cent of knowledge workers among its work force; whether they are employed part or full-time, in management or execution activities. Knowledge workers hold expertise, composed of knowledge and skills1, in specific fields. Knowledge workers create knowledge, in addition to using their knowledge to interpret incoming information [21] (p. 510). They are typically more productive and better paid than non-experts. The level of expertise of knowledge workers is not a measure of how difficult is their replacement, which may seem paradoxical at first glance. There are always people with different types of skills in the market, who can replace each other in different organisations. The mobility of knowledge workers becomes higher, as an industry gets more fragmented. One of the variables that can be used to measure a worker’s expertise is the amount of time required for him or her to learn how to perform specific activities, to a certain degree of quality and productivity, in a specific industry (this variable can also be used to evaluate the cost of expertise). This time span, though, can vary widely for different types of activities2. There are a number of different and sometimes interrelated indicators that could also be used to measure a worker’s expertise. Examples of such indicators are: 1
Knowledge is often retrieved and used in a conscious effort. Skills, on the other hand, are often developed by repetition, and used without conscious effort. A discussion on the distinctive characteristics of these concepts is out of the scope of this paper, and these concepts are used interchangeably. 2Simon and Chase (1973), for example, suggested that a chess player cannot reach the grandmaster level in fewer than 9 or 10 years.
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9 scores in specific knowledge and information assessment tests; university degrees and diplomas; previous work experience; and ability to solve real and simulated problems. These indicators are often used in combination by recruitment professionals and managers, to predict the output of prospective workers. Knowledge organisations have become part of everybody's day-to-day life and deeply affected both western and eastern society. However, it happened due to at least three phenomena. First, new and affordable technologies and related processes have been developed to automate mechanical activities, i.e. with low knowledge content, in all industries. Second, the rise in the cost of labour and the movement towards better work life conditions have made investment in new technologies and related processes, which themselves require expertise, a matter of survival for most organisations. Third, the globalisation of the economy and, in consequence, the intense international competition, especially from the 1970s on, pushed organisations into specialisation. The new technologies and related processes referred above were made possible by cheap computer and networking technologies. These new technologies are focused on the processing of data from one form to another, the storage and retrieval of data from databases, and the assembly of contextual background data. These data are then turned into information for decision making, as in Management Information Systems and Executive Information Systems. These processing technologies, including both hardware and software systems, have come to be known collectively, and somewhat vaguely, as Information Technology (IT). IT is being more frequently asked to provide information, as well as being efficient processors of data. However, there are relatively few examples of effective knowledge based systems in productive use. The reason for this is that it is much simpler, and cheaper, to manage information and data than knowledge. Moreover, the reasoning process, which allows conclusions to be reached based on facts and conditional clauses, is still not well understood [16]. The need for specialisation is today so emphatically embraced, especially in the developed countries, that even those businesses which have not been traditionally considered knowledge-intensive now have become the domain of experts. Certain types of traditional restaurants, for example, have given way to fast-food restaurants run by franchisees. These franchisees have to absorb, early on in their operation, massive amounts of knowledge concerning optimised food preparation, storage, and customer service processes. They also must know how to manage their establishment as owners, i.e. they must be knowledgeable about commercial law, accounting, and financial operations, to name a few. In short, they must combine previous expertise with new and structured knowledge, learning much in the same way as it is done in the school, instead of learning from experience. The result of this in the fast-food restaurant industry, was a rise in the quality and productivity standards, that forced old-fashioned establishments out of business. This phenomenon can be seen as a knowledge-driven, rather than a natural, selection process.
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10 The specialisation of work leads, in several areas, to an increasing need for knowledge workers. Knowledge workers hold a reasonable amount of knowledge and understanding about a domain, which makes them valuable to certain types of organisations. Knowledge workers are able to play certain roles in the organisational structure better than other workers. These roles are often played in one or more business processes, where they interact with other knowledge workers. In this type of organisational structure, what flows most between knowledge workers is information and data. It is not goods (even in manufacturing companies), nor services, nor knowledge. This has been observed in previous studies both in service and manufacturing organisations, where the flow of four elements - namely goods, services, information, and software - between departments and functions was mapped and analysed using a graphical tool called "quality flow matrix" [19]. New knowledge created by the knowledge worker results from the analysis of these information and data flows, and this new insight and understanding comes from the understanding of how the current information differs from (or is similar to) previous information. CURRENT MYTHS Management scientists and philosophers have foreseen the emergence of knowledge organisations and their increasing importance in our society ([30]; [10]). This has, however, been accompanied by the emergence of several myths. Below we list four currently widespread myths which share a common characteristic: while they result of attempts to create organisations that would supposedly be highly competitive, they are inconsistent with the concept of knowledge organisation. These myths are, therefore, deceiving and potentially harmful. Improvement Should Focus on Activities Business processes have been identified, both by the total quality and the business process re-engineering movements, as the basic units of organisations. Business processes are traditionally seen as sets of interrelated activities, which can be re-organised so quality and productivity improvements can be achieved ([15]; [13]; [7]). This mechanistic view, however, is inconsistent with the concept of knowledge organisation, and leads to dilemmas due to two main reasons: it does not appropriately deal with the complexity found in business processes in knowledge organisations, and it does not provide an effective way to understand the information flow in such organisations. The increasing complexity of business processes, in knowledge organisations, makes it difficult to model and understand business processes as sets of interrelated activities. Graphical representations, such as flow charts, easily embody hundreds of activities. These representations are difficult to analyse, let alone be used towards process improvement. While simplifications can be achieved by using multi-level schemes (e.g. multi-level IDEF0
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11 diagrams), that difficulty points to the need of new conceptualisations of business processes. Moreover, management processes have been found to be extremely difficult to be described as sets of interrelated activities in real life situations [18]. The main problem with viewing business processes as sets of interrelated activities, though, is that this conceptualisation neglects the main component of those business processes - the information flow. In fact, several widely advertised results of the amazing potential held by business process improvement approaches, such as business process reengineering at Ford and IBM Credit ([12]; [13]), and, more recently, Dow Corning [1] and CIGNA Corporation [4], have been obtained chiefly by the redesign of how information flowed, rather than how activities were carried out. This points to the need to change the way managers and consultants view business processes. This new view should describe clearly how information and data flows within the organisation, and how knowledge is created and utilised. Improvement Should be a Top-Down Process The assumption underlying this myth is that knowledge can be concentrated on the top of the organisation pyramid, while the bottom should concentrate on operating optimal processes designed by a group of management experts3. While this myth has been identified before [8], based on the fact that this top-down approach can lead to lack of commitment at lower levels, the main reason to refute the top-down approach to improvement is the specialisation of work. Forerunners of the total quality management movement have already identified the need to shift part of the improvement initiatives to the hands of the front-end workers, because they are the ones who know better how things are really done, e.g. machines are operated, parts assembled and customers serviced ([9]; [17]). Those workers, mainly in knowledge organisations, hold specific knowledge that is not held by managers. The business process re-engineering movement, on the other hand, generally supports the top-down approach to improvement as the only way worth pursuing, maintaining that specialised workers in functional areas lack a broad understanding of how the whole organisation deals with external agents, such as clients, suppliers and stakeholders. Those workers, therefore, are not able to propose process improvements with enough breadth and depth to generate bottom-line results for the organisation as a whole [14]. While the problem of lack of broad knowledge is true for most organisations, the solution proposed by the business process re-engineering movement does not seem to be appropriate, which is evidenced by the high failure rate in business process re-engineering projects - above 70 per cent [5]. The business process re-engineering approach suggests that core business process improvement should be assigned to groups of re-engineering experts, typically top 3This
was one of the tenets of the scientific management approach, proposed by Taylor (1911).
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12 managers and consultants. Rather than spreading information about how business processes are integrated into the whole organisation structure, this solution implicitly maintains that knowledge is at the top and there it should remain. This is perceived by knowledge workers as disdainful to what they deem as their most precious asset - their knowledge - which often generates lack of commitment and sometimes strong opposition. Besides, business processes are radically improved in bursts, often with a lot of pain and to the damage of internal morale. These improvements, in the low percentage of cases where they are carried out successfully, often lead to short-term gains in productivity, but are not, per se, likely to yield quality and competitiveness improvements in the long run. The reason is that these improvements, although painful, are normally simple. They are, therefore, easily copied by other organisations. This, eventually, turns what was a competitive gain into a mere (and certainly undesirable) reduction in profitability, as the competition catch up. On the other hand, knowledge-based improvements obtained with the participation of knowledge workers are more specific, customer-oriented, and likely to generate sustainable competitiveness gains in the long run [11]. Organisations Should be Learning Systems This is a myth brought about by the "learning organisations movement". This movement is more in tune with the knowledge organisation concept, than the business process reengineering movement. It states that the organisation should be able to learn continuously, particularly from its own experience. The learning organisations movement is also aligned with the fact that knowledge workers work in teams running business processes, which form one of the basic units of organisations. The concept of learning organisation is much broader than what we operationally define here as knowledge organisation. The problem lies on its applicability, which is not so general as some might have wanted it to be. Moreover, many of the most competitive organisations in the past and today are not learning systems. The belief that all organisations should be learning systems is not supported by the reality. Some of the most successful organisations today, such as Walt Disney, Avis and McDonald's, are not learning organisations in the strict sense of the word. Nonetheless, those organisations are widely regarded as very competitive. These are organisations that possess a certain amount of knowledge and information on how to carry out certain business processes. These success formulas are replicated across several business units or franchisee operated businesses, and used over and over again. There is very little corporate learning involved in this process. There is, though, a huge amount of knowledge transfer between holding and business units, or from the franchiser to the franchisees. In fact, these organisations are distinguished from learning organisations, by McGill and Slocum[22], who refer to them as "knowing organisations". The main asset of those organisations is their knowledge, not their ability to learn. They are, against all odds, true knowledge organisations. Learning requires investment and time. In today's word of fast communications and low external coordination costs [2], organisations should not focus on becoming "learning 12
13 systems". They should preferably strive to become effective and efficient knowledge and information users. Moreover, organisations should focus on obtaining and using knowledge in a limited number of business processes, and farming out those processes that can be best performed outside ([23]; [25]). Learning, if taken as a main guiding principle in a vertically integrated organisation, can even lead it to bankruptcy, as the accumulation of knowledge is likely to foster the establishment of feuds, poorly aligned with main organisation goals (e.g. the old data processing departments - the kingdom of computers experts). Finally, a note of caution about the obsessive desire for learning. This obsession manifests itself by a need to absorb large amounts of information, in the hope of refining previous or either creating or absorbing new - knowledge. This leads to information overflow and, in consequence, stress and lack of productivity, and has been a recurrent theme in the management literature ([26]; [31]). There is little evidence to support the fact that every type of learning is, in itself, good. Even in the early childhood stage of human beings, often used as an example of our natural inclination to learning, learning is selective to the types of knowledge that are more important in that stage. For a child, for example, is extremely difficult to understand the functions, and even utility, of sexual organs. There is no need for this type of knowledge at that stage, and our brain knows that. Fragmentation Should be Avoided The fragmentation phenomenon is characterised by breaking things into pieces in order to better deal with them. This occurs with problems, knowledge, and organisations alike. Complex problems are broken into manageable pieces, which are solved separately. The partial solutions then are combined in order to solve the problem. For example, the problem of designing a new car model is broken into smaller problems - the design of the parts - which are latter integrated into the final product. Knowledge is, since the basic school, split into disciplines. Organisations are divided into business units, departments, and specialised functions. According to the current thinking about learning organisations, fragmentation is seen as one of the basic problems of organisations [27]. Specialised workers are seen as working in specialised teams who deal with their specific problems, and nobody cares about the organisation as a whole. Kofman and Senge[20] contend that: "Our enchantment with fragmentation starts in the early childhood. Since our first school days, we learn to break the world apart and disconnect ourselves from it. We memorize isolated facts, read static accounts of history, study abstract theories, and acquire ideas unrelated to our life experience and personal aspirations. Economics is separate from psychology, which is separate from biology, which has little connection with art. We eventually become convinced that knowledge is accumulated bits of information and that learning has little to do with our capacity for effective action, our sense of self, and how we exist in our world" (p. 8). 13
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Why, then, do we keep fragmenting knowledge? Why do we classify things? One straightforward answer is: because, in order to understand concepts we have to break them into classes. An apple is a fruit, which is a vegetable. Apple, fruit, and vegetable, are concepts that form a hierarchy of classes and subclasses that enable us to look at an apple and understand what it is. This is valid for all components of the real word. The only way to understand the components of the world is to classify them, and our ability to retain knowledge and information depends on the quality of those classification schemes [32]. The understanding that classification schemes are at the basis of knowledge and information management, has also recently led to a new and revolutionary approach in systems analysis and design - the so called object-oriented approach. Another reason to why we classify knowledge and information is because human beings have a limited capacity of acquiring and retaining knowledge and information. It takes time and requires a lot of effort, which makes it impossible for workers to specialise in several areas at the same time. This limitation is not related to brain capacity, it is related to time constraints. In order to find their way in society most people have to develop specialised skills. Even generalists, such as CEO's, are known by their special political skills and knowledge of how to deal with people. The development of these skills takes time, which drives people into taking decisions such as "what to do in their lives" or "what career to pursue", i.e. which type of knowledge to acquire. As discussed before, expertise is the basis for competitiveness. Information must be classified in order to be stored. Should there be no classes such as economics, politics, psychology, etc., there would be no way to store information and, more important, to search for it. Why do we store information in computer databases? Because we cannot hold large amounts of information in our minds, at least not in a way that allows us to retrieve that information when we need it. Why do we classify that information? So we, using our database management software query capabilities, can find it latter. Knowledge specialisation is inevitable. Without it there would be no such a thing as a knowledge organisation. The "primacy of the whole" cannot be justified by the fact that systems have specific characteristics, which cannot be understood as a function of isolated components [6]. This fact simply calls for a specific type of knowledge - systems knowledge. This type of knowledge must be incorporated by those who have the primary responsibility of ensuring that different teams work in harmony in organisations - the managers. CONCLUSION Knowledge organisations are organisations with a high proportion of knowledge workers - i.e. workers with special types of expertise, which enable them to perform roles in business processes better than others. These organisations have become the basis of our society. The competitiveness of knowledge organisations is proportional to their ability to 14
15 acquire, store, and used knowledge and information. The understanding of the key concepts of data, information, and knowledge is fundamental to understand how knowledge organisations function. The understanding of the roles played by knowledge and information in knowledge organisations, prevents us from being influenced by myths brought about by new management approaches and theories. Four such myths, listed below, were discussed in this paper. These myths come from two contemporary ideas in management - the business process re-engineering approach to corporate renewal, and the learning organisations paradigm. 1. The myth that improvement should focus on activities. 2. The myth that improvement should be a top-down process. 3. The myth that organisations should be learning systems. 4. The myth that fragmentation should be avoided. The first myth - improvement should focus on activities - is refuted based on the fact that most of the flow of products (i.e. goods, services, information, and software) in knowledge organisations is formed by information. Therefore the focus of improvement should shift from activities to information flow. The second myth - improvement should be a top-down process - is refuted based on the fact that, in knowledge organisations, the knowledge needed to carry out operations is decentralised. Management in knowledge organisations generally lacks the needed knowledge to command improvements that will lead to long-term competitiveness gains. The third myth - organisations should be learning systems - is refuted based on the fact that the main asset of most of the successful organisations is knowledge, not learning capabilities. Knowledge and information can often be pursued outside, or brought in by knowledge workers. Also, learning takes time and investment, and its indiscriminate pursuit can lead to lack of competitiveness. Finally, the acquisition and optimisation of knowledge should focus on core-competencies. The fourth myth - fragmentation should be avoided - is refuted based on the fact that knowledge cannot be built without fragmentation, by means of classification schemes. This leads to specialisation in the work force and certainly pose problems on integration of skills. Efforts should be channelled to prepare managers to understand organisations as systems and coordinate the work of specialised teams of knowledge workers, rather than trying to build organisations where all workers are generalists. The pursuit of the first two myths is likely to reinforce the re-engineering fame of failure. Re-engineering efforts will continue to face strong opposition from employees, particularly
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16 knowledge workers. Re-engineering projects will continue to be expensive, due to the complexity involved in trying to understand organisations as sets of interrelated activities. This, as clients of consulting companies have come to realise, leads to complex methodologies and complex tools, which require specialised and expensive experts to be carried out. Moreover, re-engineering will keep yielding results that, yet radical, are simple. These results can, therefore, be copied without much effort, which is made even easier by the companies widely advertising the results of their re-engineering experiences. The pursuit of the last two myths will almost certainly discredit the learning organisation movement, except for a group of faithful followers. As soon as organisations try to turn themselves into learning systems, they will start to lose competitiveness and realise that the knowledge acquisition is what is important, not learning. Other initiatives, such as private and government investment in education and research centres, and their integration with organisations, is likely to yield better and more efficient results in the search of organisational knowledge. Also, the approach of "integrating the parts into the whole", based on the denial of fragmentation as a necessary approach to classify knowledge and information, will bounce on the very nature of work specialisation. Knowledge workers do not want to become generalists, and cannot do so, without losing their expertise. Maintaining expertise requires continuous knowledge and information updates and takes time. Moreover expertise is, at the end of the day, what makes knowledge workers valuable to organisations. Knowledge workers naturally gather together in groups, which often conflict with other groups. The real challenge facing organisations today is not to turn these knowledge workers into generalists, or philosophers, but to make them cooperate efficiently and effectively. ACKNOWLEDGEMENTS We would like to thank Anne Rouse, from the Department of Information Systems, Monash University - Australia, for her comments on the structure of this paper. REFERENCES 1. Bell, R.K., Case Study: Dow Corning, The Workflow Paradigm, E. White and L. Fischer (Eds.), Future Strategies, Alameda, California, pp. 267-285, 1994. 2. Brynjolfsson, E., T.W. Malone, V. Gurbaxani and A. Kambil, Does Information Technology Lead to Smaller Firms?, Management Science, V.40, No.12, pp. 16281644, 1994. 3. Camerer, C.F. and E.J. Johnson, The Process-Performance Paradox in Expert Judgment, Toward a General Theory of Expertise, Ericsson, K.A. and J. Smith (Eds), Cambridge University Press, Cambridge, MA, pp. 195-217, 1991. 4. Caron, J.R., S.L. Jarvenpaa and D.B. Stoddard, Business Reengineering at CIGNA Corporation: Experiences and Lessons Learned From the First Five Years, MIS Quarterly, V.18, No.3, pp. 233-250, 1994. 5. Champy, J.,Reengineering Management,Harper Business, New York, 1995.
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