Knowledge Management: The new challenge of the 21st century

This is my own reflection after studying in the “Gestion des connaissances” class, UOKM Winter 2017, with my modern, cosmopolitan and inquisitive professor, Pierre Levy

Parujee Akarasewi (Writer)


@plevy (Twitter) – University of Ottawa

Before I wrote this work, a memory of having to buy a new external drive last December at Best Buy popped into my mind. I honestly had ZERO knowledge about data and knowledge management at that time, and almost bought a 5Tb drive for my catalogue of 5,000 selfies. Seriously! Fortunately, at that time I was able to consult some IT-savvy friends to give me advice, complete with all the technical language and explanations that that left me without a clue as to what he was talking about. All I know is that 1 Tb is enough for my narcissistic collection of 5,000 selfies.


Once I started to take in the material of the Knowledge Management class with Professor Pierre Levy, I was exposed to many types and techniques of knowledge and data management on many platforms using many types of storage… I realized that we can save and manage data in more ways than just a USB flash drive or saving it to external storage.

In class, I learned to save, manage and share the knowledge that we have learnt during class through the class’s accounts using the #UOKM hashtag on the Twitter and Facebook social networks, complete with emojis, videos and links to articles. Frankly, this is a creative, intellectual way to use social networks, better than just clicking “Like”, and scrolling up and down to see pictures from friends.

My curiosity went into overdrive from the beginning of the class in January until the last class. I came to know that data curation is the key to Google’s management of massive quantities of data, “Big Data”, organized and put into different categories.

We cannot deny that we are all now in the Cyberspace Era, which has come into existence through multiple technologies. It requires collective intelligence and a community effort to help individuals towards a better and uniform understanding of new technologies like Bitcoin (a new E-currency for investments) and Blockchain (security for banks and money transfers) through well thought-out innovations in education, such as the use of social media, teach-back design, crowdsourcing, learning approaches through videogames, formative analysis, future learning, translanguaging, data journalism, open science, and so forth.

In fact, from what we learned in class, knowledge management, or KM, has many definitions: it is the whole process dealing with knowledge that is established in an organization in order to create, capture, manage, share and apply this knowledge in order to carry out the strategy to achieve the organization’s objectives; or the process of multi-disciplinary management by regrouping initiatives, methods and techniques that allow the perception, analysis, organization, addition to institutional memory and sharing of this knowledge by members of an organization. The knowledge can be the product of the organization itself, or learnt from external organizations in order to achieve its business goals.

At the organizational level, the behaviors, processes and technologies are filled in

  1. to allow personnel to understand each of these things individually and know how they apply to everyone, and
  2. see the big picture that applies to the entire organization, in order to be able to adjust, so that
  3. the organization can find out what they do not know and what they can apply for.


Humans can think, communicate and present things that are hard to present, but they can also create knowledge and, using symbols for better communication, transfer it from generation to generation.

Big Data

big data

 I used to contradict my father, telling him that mathematics are not important…

I have since come to realize that I was wrong, because mathematics now run the world. In order to manage the data at the foundation of knowledge, they have become the tools of statistics and analysis.

Imagine Big Data as a wizard: he must be a powerful and brilliant one. He can predict the future, identify phenomena, and create statistical models of the results. He can also identify causes for phenomena through statistical analysis and the application of the wonderful magic tricks of algorithms. This wizard will interpret all the data, and will always play an important role in verifying hypotheses and scientific models, and communicating the results to others. We cannot refuse this great wizard control over us in the cyberworld.

On the other hand, the advance of technology has been retarded in many companies: they have blindfolded themselves, which is why their evolution will inevitably be found to be lacking in enormous opportunities for the future, and even for the present.

None of this should scare us, because Big Data, with its statistics and algorithms, is still a million years from replacing the intelligence of human analysis, the experience of scientists, peer validation, the evolutionary comparison of data, etc.

In the book “Free Culture”, Lawrence Lessig defends “free culture” as freedom balanced with control. He sees culture as the growth of creativity when built upon permission when using the intellectual property of others.


He defines “piracy” in two ways: stealing for one’s own benefit, cutting into the income of creators; and striking a balance between the interests of IP owners and society to create a new business model. He makes a compelling case for the balance between owners and society so that we may have freely available culture that encourages new creation.

The author’s intention in making this book freely available and accessible to everyone was to create, to “kick start”, so to speak, a culture of freedom in copyright law so that tomorrow’s culture may grow without hindrance out of today’s.

When we take into account Big Data, though, the law might not be able to protect any intellectual creations in the cyber world, because of sharing and the wizard algorithms that I will explain in a later section.


Data Curation

This fancy expression is a new term and new method applied in modern technology and social media when saving data, information and knowledge. We can find old data, qualify it, and keep the right or favorite content for all time with tools like, Evernote, etc. We use hashtags and emoji to express our feelings toward the content we read. (For example, when I posted in the class Facebook page that I would be late due to the traffic my bus was facing, the professor commented on my post with an angry face emoji. That prodded me to run to class after I got off the bus, to try not to be that late. Ha-ha+) *** But please note: according to my KM-savvy Prof. Levy, Facebook, Twitter and other social media type are information platforms, not the sources of knowledge: the content that we post and repost is the real source. The more we share, the more data is stored on the platform, which is why those wizardly mathematical algorithms know what we like and keep sending us what we want to see on our page.

Data curation can manage all the data in the data schema through tags that categorize the data as a model. It comprises collective and artificial intelligence in order to make the data accessible and systematic.

Collective intelligence


Collective intelligence is intelligence distributed everywhere using everyone, constantly valued, and coordinated in real time, which results in an effective mobilization of skills, the aim being to channel knowledge to think together, according to the book that professor Levy assigned to me, “The Knowledge-creating Company” by our kind Japanese “grandpa”, Ikujiro Nonaka. In short, in this book I found that a person acquires implicit knowledge (procedural or understanding) through her efforts in research and development (R&D) for new product development.

Nonaka also stated that “organizational knowledge creation is like a ‘derivative’ of new product development.” Or in other words, knowledge is created in the interactions between frontline employees and so forth. This book helped me to realize that the next generation of wars will be fought with data, not harmful weapons. The more data we have, the more chance to win, as happened in the Japanese revolution.

Artificial intelligence



Finally, What is Artificial intelligence? Artificial intelligent is the simulation of human intelligence processes by computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction.

All in all, a semester tackling knowledge management with a scholar such as Professor Levy is tremendously valuable for my future professional and daily life. I hope my readers can learn as much as I have, and see the importance of knowledge management for the future, because the future war is a war of knowledge, not of weapons…





Book Review: The Knowledge-Creating Company

By KitchenMickey (Parujee Akarasewi)

This book is a part of my presentation in UOKM class at University of Ottawa, This Book Review will mainly focus on the content extracted from the book.

The main author is  Ikujiro Nonaka he is Professor Emeritus of Hitotsubashi University Graduate School of International Corporate Strategy.He finished his  PhD and an MBA from the University of California at Berkeley and his B.S. in political science of Waseda University, Tokyo, Japan. He was selected to be The most influential persons on business thinking from WSJ and also Management Ideas and Gurus from Economist Magazine

Next the co-writer est Hirotaki Takeuchi, he is Professor in the Strategy Unit at Harvard Business School (Elective course). He finished his BA from International Christian University in Tokyo, Japan, and an MBA and PhD from the University of California, Berkeley. He is the

Winner of the 2012 Apgar Award for Innovation in Teaching for his role in developing the Harvard Business School IXP (Japan branch)

Now I think you all know  little more about the author, Let me give you the general idea about Japan in the Global Market who see the crisis as the opportunities.

After 1945 the end of world war 3, Japan was under Uncertainty condition apres avoir faillite de la bataille : From  World War II, Korean War 1950 (N and S by America) , Vietnam War 1955, economic crises  (oil shocks 1970, Japanese asset price bubble 1986 – 1991)

  • Difficulty in a competition with western companies because they Arrived later than Western companies,So they didn’t have proven track records or “the usual burden of success (satisfaction and pride)” and the last factor that you need to know about Japan during that time, ·      they keep Continue innovate more advance idea and knowledge. Because of the Uncertainty ,it has forced Japanese companies to look outward and convert external knowledge into internal knowledge.

Next let me tell you the differences between the Japanese VS the western innovation.

Japanese “see reality typically in the physical interaction with nature and other human beings” (Zen Buddhism, Confucius)

Western Thought: More self-centered and focused on knowledge as explicit and quantifiable.

Eastern Thought: Knowledge is more tacit than explicit — needs to be translated and converted for others to understand and benefit

For instance, using Matsushita’s development of the Home Bakery, they show how tacit knowledge can be converted to explicit knowledge: when the designers couldn’t perfect the dough kneading mechanism, a software programmer apprenticed herself with the master baker at Osaka International Hotel, gained a tacit understanding of kneading, and then conveyed this information to the engineers.

This book base on Polanyi’s philosophy. Polanyi’s interest in epistemology shows in appreciation of “role played by inherited practices” for knowledge, and also passing knowledge via apprenticeship, through observation and guidance of a master. This type of knowledge was called implicit. Implicit knowledge could be further divided into technical implicit knowledge, corresponding to know‐how, and cognitive implicit knowledge. The latter presents the wealth of beliefs, presumptions and experiences that are shared typically within a cultural group (nation, company, family, etc.) and are not commonly articulated as they are assumed to be familiar to all (all word processor users know what this symbol stands for). These types of implicit knowledge are functionally distinct from explicit knowledge.

Explicit knowledge refers to books, manuals, printed procedures and guides that express information clearly through language, images, sounds, or other means of communication. Explicit knowledge also refers to the type of information or knowledge that western management style has traditionally been involved with. For instance, Nonaka and Takeuchi mention Taylorism and rational management theory of Herbert Simon (1945, March & Simon, 1958) as examples of how explicit knowledge and procedures can be used to govern an organization.

we assume that a person has acquired implicit knowledge (procedural or understanding) through her efforts in research and development (R&D) for NPD. It is stated that “organizational knowledge creation is like a ‘derivative’ of new‐product development.” Or in other words, knowledge is created in the interactions of the front‐line employees.

Knowledge is defined as a meaningful, action‐ oriented commitment, which extends the traditional ‘justified true belief’ notion prevalent in Western thinking the spiral process starts at socialization where knowledge can be shared with another person through dialogue, observation, imitation or guidance.

All in all for this point, the experiences of the Japanese companies discussed below suggest a fresh way to think about managerial roles and responsibilities, organizational design, and business practices in the knowledge-creating company. It is an approach that puts knowledge creation exactly where it belongs: at the very center of a company’s human resources strategy.

Japanese Knowledge Conversion


Japanese Knowledge Conversion

  • Socialization: Informal social environments (Honda’s brainstorming camps)

The authors defined Brainstorming Camps as “informal meetings for detailed discussions to solve difficult problems in development projects” (Nonaka & Takeuchi, 1995, p. 63).

According to the authors, socialization activities for a company could also involve research or consultation of users, and they list tama dashi kai (Honda brainstorming boot camps) as one form of socialization. This means that in addition learning or transfer of knowledge, socialization boosts creation of knowledge through combined perspectives. Explicit knowledge appears after socialization in the externalization phase.

  • Externalization: Use of metaphors, analogies, concepts, hypotheses, models (Honda’s “Automobile Evolution”)

In 1978, top management at Honda inaugurated the development of a new-concept car with the slogan “Let’s gamble.” The phrase expressed senior executives’ conviction that Honda’s Civic and Accord models were becoming too familiar. Managers also realized that along with a new postwar generation entering the car market, a new generation of young product designers was coming of age with unconventional ideas about what made a good car

The business decision that followed from the “Let’s gamble” slogan was to form a new-product development team of young engineers and designers (the average age was 27). Top management charged the team with two—and only two—instructions: first, to come up with a product concept fundamentally different from anything the company had ever done before; and second, to make a car that was inexpensive but not cheap.

They asked the question on “How is the slogan “Theory of Automobile Evolution” a meaningful design concept for a new car?” And yet, this phrase led to the creation of the Honda City, Honda’s innovative urban car

The phrase described an ideal. In effect, it posed the question, If the automobile were an organism, how should it evolve? As team members argued and discussed what Watanabe’s slogan might possibly mean, they came up with an answer in the form of yet another slogan: “man-maximum, machine-minimum.” This captured the team’s belief that the ideal car should somehow transcend the traditional human-machine relationship. But that required challenging what Watanabe called “the reasoning of Detroit,” which had sacrificed comfort for appearance.

At this stage, the possibly vague metaphorical dialogue or non‐conceptual observations are turned into explicit knowledge that becomes external to the subject. For instance, in a computer database, service manual or visual assembly guide. After explicit knowledge has been created, it can be refined further.

Combination is a process of systematizing concepts into a knowledge system. This mode… involves combining different bodies of explicit knowledge. ” (p. 67) Nonaka and Takeuchi stress that different computer systems can play an important role in this process.

  • Combination: Combining different bodies of explicit knowledge through documents, meetings, instant messaging (Asahi’s Super Dry Beer’s taste, richness concepts)

Why is a beer can a useful analogy for a personal copier? Just such an analogy caused a fundamental breakthrough in the design of Canon’s revolutionary minicopier, a product that created the personal copier market and has led Canon’s successful migration from its stagnating camera business to the more lucrative field of office automation

  • Internalization: Learning by doing (Matsushita’s reduction of work hours to increase individual creativity — explicit policy tried out for one month)

In 1985, product developers at the Osaka-based Matsushita Electric Company were hard at work on a new home bread-making machine. But they were having trouble getting the machine to knead dough correctly. Despite their efforts, the crust of the bread was overcooked while the inside was hardly done at all. Employees exhaustively analyzed the problem. They even compared X-rays of dough kneaded by the machine and dough kneaded by professional bakers. But they were unable to obtain any meaningful data.

Also, It is the counterpart of socialization and refers to the successful transfer of knowledge to a person from a book or database to another person. Once the person gains the ability to utilize novel knowledge, this knowledge becomes successfully internalized. As example, the authors mention GE new NPD staff “re‐experiencing” customer difficulties from help center transcripts or “prototyping” 1,800 hours work time goal at Matsushita for one month.

This emphasizes that internalization goes beyond facts, into sharing feelings, experiences and know‐how and could this way be easily connected to numerous design approaches presently popular in interaction and product design thinking

The authors explain that these four modes of knowledge creation penetrate through the ideal organization. Even though the knowledge is created at the individual level, it should be passed on to other levels of organization (externalization) in order to be exploited widely (internalization and combination).

spiral model

spiral model

This process is depicted as a spiral model  as New knowledge that always begins with the individual. A brilliant researcher has an insight that leads to a new patent. A middle manager’s intuitive sense of market trends becomes the catalyst for an important new product concept. A shop-floor worker draws on years of experience to come up with a new process innovation. In each case,individual’s personal knowledge is transformed into organizational knowledge valuable to the company as a whole of knowledge creating organization shown on the following figure:

The organization needs to support the spiral process. The writers introduce five organizational enablers of knowledge creation.

These are

  1. Intention and commitment in the organization
  2. Autonomy at all levels (cross‐functionality, self‐organization))
  3. Fluctuation and creative chaos (breakdown of patterns and standards, reflection in action, cf. Schön [1983])
  4. Redundancy (internal overlaps and competition)
  5. Requisite variety (along Ashby, 1956; meeting external complexity with internal diversity, staff heterogeneity)

In this description of the organizational support, Nonaka and Takeuchi come closer to realizing their model in actual organizations. The five enablers mainly describe how the company R&D should be organized to ensure success in knowledge creation. They further go describe a five step model, which is somewhat a derivate from the rugby team metaphor (all players constantly moving and looking ways to turn the game for their team advantage) used to describe successful Japanese industry units

Hypertext Organization: it is the Best knowledge-enabling corporate model is a synthesis

Which Interconnected layers or contexts”

Nonaka (1994) has coined a new organisational architecture which combines the efficiency and stability of a hierarchical bureaucratic organisation with the flexibility of the flat, cross-functional task-force organisation. This new architecture is intended to combine the advantages these structures have on knowledge creation effectiveness. The Hypertext organisation consists of three layers.

  1. Business System Layer
  2. Project Team Layer
  3. Knowledge Base Layer

The bottom layer of the Hypertext organisation is known as the knowledge-base layer, in which tacit and explicit knowledge are embedded. This tacit knowledge can be associated with organisational culture and procedures, while the explicit knowledge has taken form in documents, filing systems, or digital databases. The layer on top of the knowledge-base is called the business-system layer. This is where routine operations are carried out in a hierarchical, bureaucratic organisation. This layer has all the characteristics of a top-down organisation. The top layer is known as the project-system layer. Multiple knowledge creating self-organising project teams make up this layer. The teams are loosely linked to facilitate an interconnectedness that improves the knowledge creation process. They share the same corporate vision that underlies the knowledge creation efforts.

Knowledge in Practice

  • Matsushita’s Home Bakery bread-making machine : Matsushita’s unique “twist dough” method and a product that in its first year set a record for sales of a new kitchen appliance.
  • Engineers worked as baking apprentices (socialization)
  • Creative chaos due to shift from household appliances to high-end products
  • Integration of different divisions (Rice Cooker, Heating and Rotation) created requisite variety
  • Home Bakery success led to Human Electrics Division

In the knowledge-creating company, all four of these patterns exist in dynamic interaction, a kind of spiral of knowledge. Think back to Matsushita’s Ikuko Tanaka:

  1. First, she learns the tacit secrets of the Osaka International Hotel baker (socialization).
  2. Next, she translates these secrets into explicit knowledge that she can communicate to her team members and others at Matsushita (articulation).
  3. The team then standardizes this knowledge, putting it together into a manual or workbook and embodying it in a product (combination)
  4. Finally, through the experience of creating a new product, Tanaka and her team members enrich their own tacit knowledge base (internalization). In particular, they come to understand in an extremely intuitive way that products like the home bread-making machine can provide genuine quality. That is, the machine must make bread that is as good as that of a professional baker.

In conclusion, Nonaka and Takeuchi are arguing that creating knowledge will become the key to sustaining a competitive advantage in the future. Because the competitive environment and  customer preferences changes constantly, knowledge perishes quickly. With The Knowledge-Creating Company, managers have at their fingertips years of insight from Japanese firms that reveal how to create knowledge continuously, and how to exploit it to make successful new products, services, and systems. So that the future belongs to companies that can take the best of the East and the West and start building a universal model to create new knowledge within their organizations” , “Nationalities will be of no relevance with success” and “Success in the new ‘knowledge society’ will be judged on the basis of knowledge-creating capabilities