Forster N. - Maximum performance (2005)(en)
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of knowledge will be, at best, ad hoc and inefficient. Creating and sharing knowledge is not just a matter of making more information available or telling employees that they should talk to each other more often. Effective knowledge management stems from a conscious strategic effort to tap into employees’ implicit and subjective experiences, insights, intuitions and hunches, and make this knowledge available to anyone who might want to use it.
Without repeating all the suggestions made in Chapter 9 for fostering employee creativity and learning, this primarily means challenging employees to examine what they take for granted (common sense), particularly during times of rapid change or when long-established knowledge is becoming outdated or redundant. This implicit knowledge then has to be made explicit and converted into information that the whole organization can then use, be this through personal contact, human networks, technological systems or some combination of all three. To achieve this, many of the questions that underpin organizational learning and innovation also have to be asked within the context of knowledge management. These include the following:
•Why are we doing what we do now?
•Where are we going in the future?
•What does our organization know now?
•What will it need to know in the near future?
•What will we need to (un)learn in the future?
•Where will we obtain the new information/knowledge we need?
•How will we share, disseminate and use this information/knowledge?
•What competencies, skills and knowledge does our workforce have now?
•What competencies, skills and knowledge will it need in the near future?
Mapping internal expertise can take many forms, such as the creation of personal websites that list each employee’s areas of knowledge and expertise, creating virtual communities of practice, or setting up specialist knowledge groups in organizations. For example, Unichema International ‘undertook a Knowledge Management Program’, designed ‘to use the creativity of its people. Formal “What if” experiments were introduced, and a common approach to problem solving. Company activities were divided into four levels of complexity: access to knowledge, adding value to knowledge, modelling knowledge and knowledge discovery. The intention was to create a managerial process for the accumulation and use of technical knowledge – a version of scientific creativity. Unichema is just one example amongst many’ (James, 2001: 17). The
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principal objective of this process of self-analysis was to ensure that employees both created and accessed information, and then used this to improve their daily decision-making and problem-solving processes.
Knowledge transfer is not simply about ‘communicating more’ or ‘disseminating information’: it requires assimilation by those who are using it. In other words, you can have the best IT systems and databases in the world; but if people don’t understand their purpose, or don’t make effective use of these, you will be left with a very expensive albatross (with the well-paid consultants who put the system in for you in the first place now long gone). While technology can be a powerful catalyst in knowledge management, the transfer and use of knowledge is primarily a cultural and people management issue. In other words, the reward systems and working practices of the organization must support these activities in a very explicit way. As Nonaka puts it succinctly, ‘Often, the most important factor in managing knowledge is the way a company organises its units and people. Human links, not electronic ones, are the key’ (1991: 17). So the primary challenge for leaders and managers is to create an innovative and learning culture, create personal or codified networks of knowledge workers, leverage their collective knowledge across the entire organization, and then convert this knowledge into usable ideas.
People-based knowledge management systems primarily involve dialogue and communication. This may come in the form of breakfast or lunchtime presentations, weekly team meetings and project briefings or via mentoring relationships. Merely having these kinds of forums can send an important message to staff: we are explicitly fitting time into the working day for you to learn from each other and share your knowledge and expertise. For example, in consulting firms such as Bain, Boston Consulting and McKinsey, knowledge is shared primarily through interpersonal dialogue and brainstorming sessions. In the 1990s, all three companies invested heavily in building informal knowledge networks amongst their employees. Bain did this ‘by transferring people between offices, by supporting a culture in which consultants are expected to return phone calls from colleagues promptly, by creating directories of experts and by using “consulting directors” within the firm to assist project teams’ (Hansen et al., 1999: 109). These companies recruit top-tier MBA graduates, to use their creative and analytical skills to solve business problems. They look to employ people who will be able to use their ‘people-to-people’ knowl- edge-sharing approach effectively. To make sure they get the right kind of staff, they spend an inordinate amount of time screening new recruits, who can go through as many as eight interviews. One of their top selection criteria is ‘communication skills’ (ibid.: 110).
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These knowledge management initiatives then have to be built into the culture of an organization and the way it thinks collectively. This means that knowledge management initiatives must be driven by the organization’s core values, vision, business plans and strategic goals. The organization also has to allow individuals to be comfortable with the need to use knowledge more effectively, and provide them with the appropriate skill levels they need to understand and interpret the information that they have access to. The culture must support employee mind-sets and working practices that encourage the sharing and dissemination of knowledge and intellectual capital as a collective exercise. A knowledge-generating company cannot allow individuals or groups of employees to hoard knowledge for their personal benefit. This means that the organization’s reward systems have to encourage and reward knowledge sharing. For example, Ernst and Young monitor the level and quality of employee contributions to their electronic databases, and this is one of five dimensions of their annual performance reviews. Bain Consulting include factors such as ‘direct help to colleagues’ and ‘information sharing’, and the quality of these contributions can account for as much as one-quarter of an employee’s annual compensation. Hence an employee who hoards knowledge and information in these companies soon discovers that their pay and remuneration suffer in very tangible ways. Personalized knowledge management has to become a way of behaving, indeed a way of being, in which everyone becomes knowledge workers and shares their knowledge freely with whoever wants it. The organization’s culture has to support the sharing of information, because unless implicit knowledge becomes explicit it cannot be leveraged by everyone. This mind shift means that knowledge management must come to be regarded as an organic, rather than a mechanistic, process (Ruggles, 1998: 86).
When a knowledge-sharing culture has been created amongst employees, the next stage is systematically to measure, sort and store what have been variously described as ‘tangible’, ‘codified’, ‘structural’ or ‘explicit’ knowledge assets. These can come in many different forms, including mission statements, policy documents, employee surveys, customer and client surveys, books, papers, studies, personnel files, technical reports, software, data bases, emails, CDs, DVDs, patents, copyrights and registered or copyrighted intellectual property. The purpose of this audit is to create an organized, formalized, systemic and codified inventory of an organization’s entire stock of ‘hard’ knowledge assets, and then storing this on electronic databases. The purpose of creating systems that encompass an organization’s implicit and explicit knowledge is to allow a company’s employees to share information in a timely and efficient way that is not entirely dependent
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on face-to-face information sharing. Hence capturing, documenting and encouraging the proactive use of this information becomes the next operational priority. Unlike implicit or tacit knowledge, this explicit knowledge then becomes independent of its creators and, ideally, can be transferred, utilized and adopted simultaneously and at great speed by other users of an organization’s information networks (Hansen et al., 1999).
To achieve this, a ‘people to documents’ and a ‘documents to e-docu- ments’ approach to knowledge sharing has to be encouraged, whereby all potentially useful knowledge is immediately transferred to databases. Also known as ‘data warehouses’ or ‘data mines’, where employees can go digging for information, these repositories contain the full range of codified knowledge that has been ‘captured’ by the organization. These may consist of collections of passive information and resources or, as is increasingly the case, semi-intelligent systems that can help direct users to the most relevant information that they are seeking (sometimes known as ‘complex adaptive systems’). These systems can have a formal, centralized control hub or may be created by ad hoc and informal groups of employees within an organization. Whatever their level of sophistication, these repositories must be designed to capture knowledge and information that everyone in an organization can contribute to and access with ease. Over time, these databases then come to form the bedrock of an organization’s corporate memory and intelligence gathering capabilities. Synonymous with these are group or shareware systems that allow the free flow of ideas and information between disparate individuals and groups within organizations.
The most recent codified knowledge management systems are based on Weblogs and K-Logs. These are user-friendly websites on which bloggers can post entries on any topic. A K-Log tool leverages a company’s intranet and deals with information specific to the company that is sponsoring it. One company, Userland, makes two products: ‘Manila’, a centralized server-content management system, and ‘Radio User Land’ (RUL), which provide easy weblogging from desktops. RUL is a weblog tool that automatically builds, organizes and archives information, and publishes content. It requires no knowledge of HTML, FTP or graphic design. In addition to publishing a weblog, K- Log tools allow users to publish pictures, documents and links to other resources on an intranet, where these can also be archived, searched and browsed. The benefits of using K-Log systems in organizations include better documentation of processes, shorter audit cycles, the instantaneous creation of archives of contributions by employees, and a knowledge induction tool for new organizational members (abridged from Gengler, 2002b).
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However, it must be stressed that all codified systems, including those described above, remain for now passive repositories of information. They still need human beings to contribute to them, make use of them and then convert this information into actionable ideas. This means that, not only should ownership of knowledge management initiatives be put in the hands of employees, but what goes into these databases must also be relevant and useful. As Seeley-Brown and Duguid have observed,
Most databases, like most business processes, are top-down creations. Managers fill them up with what they think will be useful for the people they manage. And – surprise, surprise – the people don’t actually find them so. Yet even when individuals fill databases with their own ideas of what’s useful, they aren’t much help either. Often what one person thinks useful, others find flaky, idiosyncratic, incoherent, redundant or just plain stupid. The more a database contains everyone’s favourite ideas, the more unusable it becomes.
(Seeley-Brown and Duguid, 2000: 79)
Hansen and colleagues cite the example of Rank-Xerox who tried to embed the know-how of its technicians into an expert system installed in their copiers, hoping that they could deal with repairs with guidance from this system. However, they found that technicians came across problems that could not always be solved by accessing this. When the designers of the system looked into it, they discovered that the technicians had got round this problem simply by telling each other stories about how they had fixed the machines. In other words, the expert system could not replicate the subtle nuances, detail and in-depth personal knowledge that were exchanged in face-to-face conversations or by mobile phone (Hansen et al., 1999: 115). This means that organizations must be able to evaluate the effectiveness of codified knowledge management systems and whether these are actually any better than existing personalized knowledge sharing systems.
Before turning to look at some potential problems that have been encountered when introducing knowledge management initiatives, there follow a few examples of early successful codified systems.
Ernst and Young
Ernst and Young created a huge company-wide database (the K-Web) run by 250 staff at their Centre for Business Knowledge. This data resource contains 40 ‘Areas of Practice’ run by specialist staff in each one. Setting this IT system up cost E&Y $US500 million on hardware and software and recruiting/training new staff to support their knowledge management systems. This judicious use of information has enabled E&Y to grow at an average rate of about 20 per cent a year in recent times and, in one three-year period, increase their consulting
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revenues from $US1.5 billion in 1995 to $US2.7 billion in 1997 (Hansen et al., 1999: 114).
Price Waterhouse Coopers
Under the guidance of their Chief Knowledge Officer, Ellen Knapp, PWC created a powerful intranet called ‘Knowledge Curve’ in 1998. Here employees could find repositories of consulting methodologies, case studies of company clients, repositories of best practice, tax and audit rules, online training packages and directories of experts, plus an almost infinite set of links to other repositories of information outside the company. In 1999, the site received about 18 million hits a month from PWC employees. Even so, according to George Bailey, the company’s US ‘Innovation Leader’, this is still underutilized: ‘everybody goes there sometimes, but when they’re looking for expertise, most people go down the hall’ (cited by Stewart, 2000b: 392). Quite independently of these developments, a UK-based PWC consultant, Jon Bentley, set up another network, ‘The Kraken’, in order to ‘collaborate so as to be more innovative’. It was given this name some time after it was set up because a colleague observed that innovation in PWC was like the mythological sea-monster in the Tennyson poem, who ‘Lives far, far beneath in the abysmal sea and sleeps his ancient, dreamless, uninvaded sleep.’ Anyone could join this, and in 2000 about 500 ‘self-selected creatives’ were members. Bailey regarded this ad hoc community of practice as the firm’s premier forum for sharing new knowledge. Bentley and Knapp believed that there were a number of reasons for its success:
First, it’s demand driven: eighty percent of Kraken traffic starts with a question – Does anybody know? Has anybody ever done? Often a question provokes a four or five page response, with real research having been done for no reward other than the satisfaction of having helped. Also, the Kraken gets at tacit knowledge, provoking responses from people who didn’t know that they had something to contribute. It tolerates fuzzy questions better than do formal data bases, where one often needs a bit of expertise even to begin.
In all these ways, Kraken differs from Knowledge Curve. The latter supplies explicit knowledge on a site you have to go to. It preserves knowledge more than creates it. It’s a compendium not a conversation. There’s an ancient debate over whether knowledge management happens by design or by emergence. Says Knapp, correctly, ‘I find myself coming down dead centre in the middle of the argument. The Kraken is about learning, Knowledge Curve is about teaching. You can’t have one without the other’.
(Stewart, 2000b: 392)2
Hoffman La Roche
Their internal knowledge management system took the form of a Yellow Pages directory: a catalogue of staff, ranked according to expertise, questions and issues (Ruggles, 1998: 85).
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General Motors
GM launched a secure, customizable, four-million-page intranet in December 2001 called, appropriately, My Socrates. The aim was to get all 180 000 of its US employees to access this site in order to move towards GM’s vision of a ‘web-savvy workforce’. With 75 per cent of its employees already owning PCs, the company was aiming to get all its employees on-line by December 2003. It succeeded in this objective.
Hewlett-Packard
HP created one of the first business intranet systems in the early 1990s, with 2500 servers handling one and a half million email messages a month. It was used to support information, including sharing amongst design teams and cross-functional dialogues. Its intranet-based ‘Electronic Sales Partner’ was created to foster a tighter connection between HP and its primary customers. By allowing customers to access relevant information and interact directly with HP, customer knowledge was enhanced by a constant flow of information both within and across organizational boundaries (Ruggles, 1998: 82).
Potential problems with knowledge management initiatives
In 1998, Rudy Ruggles examined the results of an Ernst and Young survey of 431 American and European companies. This analysed what these firms had been doing to manage knowledge, what they felt were the greatest barriers to the introduction of knowledge management initiatives, and what they thought they should be doing in the future. When this book was published, the study was already six years old, but its findings still resonate today, and they are particularly relevant for organizations that are thinking about introducing knowledge management initiatives for the first time. Ruggles looked at nine areas of knowledge management in these companies: how new knowledge was generated; how knowledge assets were measured; how knowledge was accessed from outside the company; how knowledge was used in decision making; how new knowledge became embedded in business processes, products and/or services; how this information was stored; how the companies’ cultures encouraged or discouraged the sharing of knowledge; how knowledge was transferred throughout the companies; and how knowledge management initiatives were measured and evaluated (Ruggles, 1998: 81; unless otherwise indicated, all page references in this section are from this article).
On a positive note, many of the companies did have knowledge management initiatives under way. For example, 72 per cent had introduced, or were planning to introduce, an intranet; 57 per cent had introduced, or
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were planning to introduce, data warehouses or knowledge repositories; 53 per cent had introduced, or were planning to introduce, decision support tools; 44 per cent had introduced, or were planning to introduce, group or shareware systems; 39 per cent had created, or were planning to create, networks of knowledge workers; 38 per cent were planning to map sources of external expertise and 28 per cent were planning to establish new knowledge roles (p. 83). However, it was also apparent that most of these organizations were having problems with their knowledge management initiatives. As Ruggles comments, ‘the executives who responded to this survey did not hold high opinions of their organizations’ performance in any of these areas’ (p. 81). For example, only 13 per cent thought that they were adept at transferring knowledge held by one part of the organization to other parts. Even ‘generating new knowledge’, the process about which respondents had the highest confidence in their organizations’ capabilities, still received above-average ratings from fewer than half (46 per cent) of these executives (ibid.).
They believed that the biggest obstacles to introducing knowledge management initiatives were ‘people’s behaviour’ (56 per cent), ‘organizational culture’ (54 per cent), ‘top management’s failure to signal its importance’ (32 per cent) and ‘lack of ownership over the process’ (28 per cent). Furthermore, only 34 per cent of these executives said that they were able to ‘access valuable information from external sources’; 30 per cent used ‘accessible information in decision making’, and just 29 per cent were able to ‘embed knowledge in processes, products and/or services’; 27 per cent stored knowledge in ‘documents, databases, software etc’ and just 19 per cent believed that their organizational cultures and incentive schemes ‘facilitated the growth of knowledge’. A meagre 14 per cent measured and evaluated the effectiveness of their knowledge management initiatives or the value of their knowledge assets (p. 82).
Significantly, even though many of these companies were aware that it was the human response to their knowledge management initiatives that was the biggest source of resistance, most still relied on technological solutions. Yet, when they had been asked whether their competitiveness was based on ‘people’, ‘process’ or ‘technology’, half reported that they believed that ‘people’ was the most important factor, with ‘process’ and ‘technology’ being rated at 25 per cent each. As Ruggles notes, ‘While “people management” issues may be endemic to any change management initiative, knowledge management initiatives seem to bring them out in abundance’ (p. 86). Furthermore, ‘IT personnel’ were twice as likely as ‘senior managers’ to be the leaders of knowledge management initiatives in these organizations. His findings are
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important, because they add considerable weight to many of the suggestions made earlier in this book, particularly those relating to change, learning and innovation in Chapters 8 and 9. Ruggles’ message is straightforward: no organization can rely solely on technological solutions when introducing knowledge management initiatives into an organization. Given the explosion of new IT systems and software packages in the 1990s, and the massive hype that accompanied these in the media, it isn’t surprising that many companies initially resorted to technological solutions when they were faced with these new challenges. However, as we have seen, this has never been sufficient in practice. As Ruggles has observed succinctly and accurately, ‘It is inevitable that the technology will not be enough. In fact, if the people issues do not arise, the effort underway is not knowledge management. If technology solves your problem, yours was not a knowledge problem’ (p. 88).
Which are the best systems to use? What is the best blend or balance of people, process and technology strategies to employ when introducing knowledge management initiatives into an organization? As with many other facets of leadership and management, this decision is contingent on the nature of your business, what you want to do with your organization’s knowledge, the competitive environment in which you operate and the demands of the customers and clients you deal with. Hansen and his colleagues suggest an 80/20 split, commenting that ‘Executives who try to excel at both strategies risk failing at both’ (1999: 112); by this they mean that organizations should have either 80 per cent codified/20 per cent personalized systems or vice versa. Many commentators would disagree with this prescriptive formula. For example, Ruggles suggests a 50 per cent ‘people’, 25 per cent ‘process’ and 25 per cent ‘technology’ mixture, which is a much more sensible starting point for most organizations. Whatever knowledge management systems an organization might choose to introduce, this decision has to be driven by the company’s core values, vision, business plans and operational strategies, never the other way round. As Stewart puts it, ‘that’s an important lesson for knowledge management types: if your baby ain’t tightly linked to the business model, it won’t do squat’ (2000a: 129).3
Accessing knowledge from outside an organization’s boundaries
In 1985, product developers at the Matsushita Electric Company in Osaka were hard at work building a new home bread-making machine. But, no matter what they tried, they could not get the machine to knead the dough properly and always ended up with a soggy middle and an over-cooked crust. They tried everything they could think of to sort the problem out, including X-raying their dough and the dough kneaded by professional
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bakers. After many months, a software developer called Ikuko Tanaka suggested that they go and have a look at the head baker at the Osaka International Hotel, who had a reputation for making the best bread in the city. Tanaka worked with the head baker for several days and noticed that he had a distinctive way of stretching the dough. After a year of trial and error they were able to come up with special ribs inside their machine that mimicked the bread-maker’s technique, and the quality of the bread that she had learned to make at the hotel. The result: Matsushita’s unique ‘twistdough’ method and a product that in its first year set a record for sales of a new kitchen appliance [ ] This one experience was informally conveyed to other Matsushita employees and they now use this knowledge to formulate equivalent quality standards for other products, be this kitchen appliances or white-goods.
(Abridged from Nonaka, 1991: 98)
This quotation illustrates some important things about knowledge acquisition from outside an organization’s boundaries. First, this group of product developers did not have sufficient in-house knowledge to solve the problem that they were facing, and it took a lateral leap of imagination to consider looking for solutions that might exist outside their immediate organizational environment. Second, it took Tanaka several days to tap into the implicit knowledge that the head baker possessed. Third, the knowledge they acquired, and what they had learnt from this experience, was quickly and freely shared with other employees. Like innovation and learning, one of the most important drivers of knowledge management is the recognition that there are, potentially, almost infinite sources of information beyond the boundaries of an organization or the mind-set that it operates within. In the last chapter we identified the growing importance of scenario mapping, as a method that helps organizational leaders trace possible future paths from the systems and trends that are shaping the world at the moment (for example, globalization and new technologies). This allows the creation of ‘What if . . .’ thinking. In turn, this permits the creation of contingent strategic plans that can be implemented if, or when, one of these anticipated scenarios emerges. Companies that have tested their strategies against these scenarios, and things that could go wrong, are in a much stronger position to deal with these when they arise.
A study by the research and consulting firm Gartner has suggested that an important part of this process is obtaining competitive intelligence and knowledge. They argue that this has now become so important that it can often make the critical difference between success and failure, particularly for small companies. Gartner also suggest that at least 60 per cent of the world’s most successful companies have strategies in place to keep a close eye on their competitors, as well as using thirdparty information to help identify new business opportunities. On