What Is Knowledge Management?
Knowledge management (KM) is the process(es) used to handle and oversee all the knowledge that exists within a company. Knowledge management relies on an understanding of knowledge, which consists of discrete or intangible skills that a person possesses.
The field of knowledge management identifies two main types of knowledge. Explicit knowledge is knowledge or skills that can be easily articulated and understood, and therefore easily transferred to others (this is also called formal or codified knowledge). Anything that can be written down in a manual - instructions, mathematical equations, etc. - qualify as explicit knowledge. Tacit knowledge, by contrast, is knowledge that is difficult to neatly articulate, package, and transfer to others. These are usually intuitive skillsets that are challenging to teach, such as body language, aesthetic sense, or innovative thinking. (A third knowledge type is implicit knowledge, which is information that has not yet been codified or transferred, but that would be possible to teach. Implicit knowledge is different from tacit knowledge, which is unlikely to be able to be codified. For this article, however, we will primarily discuss explicit and tacit knowledge.)
You can break these knowledge types down further into four categories:
- Factual Knowledge is measurable, observable, and verifiable data.
- Conceptual Knowledge relates to perspectives and systems.
- Expectational Knowledge is knowledge rooted in expectations, hypotheses, or judgments.
- Methodological Knowledge deals with decision-making and problem-solving.
Knowledge management enables organizational learning, a concept where companies are invested not only in the reliable, expert production of a product or service, but in the knowledge that underlies these production processes. Companies devoted to organizational learning are interested in maintaining and building upon internal knowledge at an organizational level - not just helping individuals accrue special skills, but ensuring that this knowledge is available to and dispersed throughout the workforce.
As one Massachusetts Institute of Technology (MIT) paper states, the core goal of knowledge management is to connect “knowledge nodes” - those with knowledge and those seeking knowledge - to ultimately increase the knowledge within an organization. Within that goal, the authors identify four objectives of KM: to capture knowledge, to increase knowledge access, to enhance the knowledge environment, and to manage knowledge as an asset.
Ultimately, knowledge management is an integrated system of accumulating, storing, and sharing knowledge within a team or organization. KM consists of several components, as well as strategies to implement it successfully - we’ll delve deeper into these later in the article.
Who Uses Knowledge Management?
Knowledge management can be implemented enterprise-wide across a number of industries. However, the way you implement KM might change depending on factors such as industry and company size.
KM is often used differently for small vs. large organizations, however. Small (and/or young) companies must carve out a competitive market advantage early on, and therefore benefit from KM by codifying and storing internal knowledge from the get-go. Large organizations - even those with unwavering strength in their market - use KM to act quickly in the digital age, where business changes constantly and often without warning. Without a reliable system to store existing knowledge and accumulate new knowledge, it would be difficult to react to these market changes. However, both large and small companies can benefit from KM because it treats the knowledge that every individual brings as an asset, so employees feel respected for their skills in the workplace.
For those looking to implement knowledge management in a specific department, you can also tailor the practice to sub-fields. Other than enterprise KM, knowledge management is most commonly implemented in IT/information systems and science, organizational management, business administration, human resources management, content management, or for personal use.
History of Knowledge Management
Efforts to formally manage knowledge have been in place for most of the past half century. In the late 20th century, however, with the evolution of computers, organizations began implementing more reliable storage systems. In the 1990s, the Swedish financial service provider Skandia created the first Chief Knowledge Officer (CKO) position, which paved the way for other companies to treat knowledge management as an integral part of their structure.
In fact, several academics have formalized the topic, and knowledge management is now regarded as a scientific discipline. In the mid-1990s, Ikujiro Nonaka and Hirotaka Takeuchi published the first academic research on knowledge management, and coined the SECI model (socialization, externalization, combination, internalization) as a path of knowledge transfer. Since then, Nonaka and Takeuchi have studied several other aspects of knowledge within a company, as have other academics. The knowledge management life cycle, which details how to store and disseminate knowledge throughout an organization, was also set forth by business academics in the l990s and early 2000s; the four most popular models are Wiig (1993), Zack (1996), Bukowitz and Williams (2000), and McElroy (2003).
In general, knowledge management has evolved from a loose tendency to accumulate, store, and teach (and therefore increase) knowledge within a team or organization into a prioritized method of building a knowledgeable workforce - all with the goal of gaining a competitive advantage. As we will discuss later on, there are several theories of how to best store knowledge and stimulate knowledge transfer within an organization. Skeptics say that today’s knowledge management is simply a “fad,” while others argue that its principles are here to stay: its strategies, methods, and implementation systems will simply continue to evolve as technology and business does, too.
What Is the Primary Benefit of Knowledge Management?
While it may not seem directly related to the tactics of knowledge management, the primary goal is to increase company efficiency to improve business decision-making. The idea is that building expertise into your organization - and dispersing it amongst employees - empowers you to make more informed, faster, and ultimately more profitable decisions.
Of course, there are several secondary benefits. Successful KM will enable you to:
- Increase collaboration and idea generation
- Optimize a culture of knowledge sharing
- Protect intellectual capital
- Treat human capital as an asset (which makes employees feel respected for their knowledge)
- Capture and store knowledge for the future workforce
Kevin D. Murray, CPP, CISM is a technical surveillance countermeasures (TSCM) specialist with an extensive career in information security and counterespionage consulting for business and government. He breaks the benefits of KM into four value areas:
Control: Not knowing where your information is is the first step to losing it.
Security: ‘You gotta keep the bait in the bucket, not in the pond.’
Access: When you need your information, you will get it faster.
Responsibility: With all the eggs in one basket, one is forced to carry the basket more carefully, meaning you provide better protection for your information.
RELATED: [Infographic] How Much Time Are You Wasting on Siloed Information?
What Are the Challenges of Knowledge Management?
There are many challenges that businesses face when implementing knowledge management. Here is a list of some of the most common ones:
- Creating a culture of flexibility and collaboration: This is one of the most significant and enduring challenges of KM. Companies already struggle to implement new policies, because people naturally tend to resist change. However, KM can be especially difficult because employees might want to protect their skills and knowledge, or be reluctant to learn from their peers.
- Security: You have to design a knowledge transfer system that makes it easy for the appropriate people to access information, while protecting sensitive or private intelligence from outsiders.
- Measuring knowledge: It can be difficult to define metrics to measure the knowledge within your organization, especially for tacit knowledge that cannot be easily quantified. To overcome this, some experts recommend focusing on the purpose of knowledge, rather than the efforts or results (which are often also unquantifiable).
- Identifying an expert: There won’t always be a single “keeper” of every knowledge type, but you will still have to identify who within the company possesses certain knowledge, and use them as the base level of knowledge from which you want to build. This process is difficult tactically, but can also be delicate among employees who might feel competitive about their skill levels.
- Document storage and management: While not all knowledge makes for straightforward documentation, it will have to be stored and organized in some form. Document management is a challenge for many companies, but organization is a vital aspect of KM - otherwise, it will be impossible to locate and use the knowledge you have stored. Consider using a dedicated document management system to keep everything organized.
- Disseminating knowledge throughout an organization: You’ll need to devise a process where, once you store the knowledge, other team members can access it. This is complicated both theoretically and tactically, so many organizations opt for a software system designed specifically for this purpose. We’ll take a closer look at KM systems later on.
- Continual improvement: Like most process-driven strategies, you should continually improve upon the knowledge management system you implement. Stage periodic reviews or, if possible, dedicate resources to continually optimize your process.
- Determining where KM is housed: If KM serves your entire organization, decide which department will “own” the strategy. Companies most commonly house KM in HR or IT. Remember, this department is not only responsible for effectively managing the knowledge itself, but also for maintaining the community of knowledge sharing and organizational learning.
How Does Knowledge Management Work? Basic Components and Strategies
As we’ve discussed, the theory behind knowledge management is that in order to make the best business decisions, the workforce must be as educated and skillful as possible. One way to ensure an educated - and continually learning - workforce is to stimulate organizational learning, which companies can do by implementing knowledge management. This practice ensures not only that existing knowledge (both explicit and tacit forms) is codified and stored, but that it can be dispersed among other employees so that people can continue to amass skills. Another benefit is that KM evenly distributes knowledge so no one is contributing in silos.
As Nonaka and Takeuchi first stated in their seminal academic papers, there are three main ways that people approach knowledge management:
- People-centric: Centered on people, relationships, and how people form learning communities and other informal ways of knowledge sharing. This idea is also known as ecological KM theory.
- Tech-centric: Focused on the technology that facilitates knowledge storage and transfer, and aims to create technology systems that encourage knowledge sharing.
- Process-centric: Interested in how the organizational structure and processes accommodate and encourage knowledge sharing and organizational learning. This concept includes the production processes, the organizational hierarchy, and the cultural framework.
The approach you take will depend on how your company currently functions. Organizational structure, politics, management style, and existing processes all create parameters around what kind of KM implementation is workable. Regardless of the approach you choose, however, implementing KM will inevitably affect your organization’s people, technology, and processes. Therefore, it’s best to keep all three in mind when enacting a knowledge management strategy.
Theoretical approaches aside, there are some common tactical ways of handling knowledge. The common strategies include:
- Storing knowledge vs. sharing knowledge: Storing knowledge involves accumulating, codifying, and maintaining knowledge in a reliable storage system. This is a good first step, but successful knowledge management also requires a system to disperse that stored knowledge.
- Codification vs. personalization: The difference between these strategies are similar to the previous example. Codification is any activity where you are collecting knowledge (creating and maintaining databases, content architecture, training to support software storage systems), and creating awareness of these collection systems. Personalization, is connecting people to this codified knowledge by forming learning communities, promoting active discussion and knowledge transfer, and facilitating group interaction.
- Push vs. pull: These represent two opposing strategies. In a push strategy, individuals actively encode their knowledge to make it available for others. In a pull strategy, team members seek out experts to request knowledge sharing, so you only transfer knowledge on an as-needed basis.
- SECI model: This is the knowledge transfer and strategy model first proposed by Nonaka and Takeuchi in 1996, and is considered the cornerstone of knowledge management theory. It outlines the four different types of knowledge transfer:
- Socialization: Tacit to tacit, where knowledge is transferred intuitively through observation, guidance, and practice.
- Externalization: Tacit to explicit, which codifies intuitive, intangible knowledge in order to be taught. This type of knowledge transfer is the most difficult because tacit knowledge is extremely difficult to break down into digestible directives.
- Combination: Explicit to explicit, where codified knowledge is transferred or combined with other codified knowledge. This type of knowledge transfer is the simplest.
- Internalization: Explicit to tacit, where an organization follows and practices codified knowledge so that it becomes intuitive.
Data mining is a process of discovering data patterns based on algorithms, and is another common element of sophisticated knowledge management programs. Because codifying all of your internal knowledge will result in a huge knowledge library, data mining can help identify patterns and extract data. It still uses qualitative methods of data analysis, but automated programs will likely rely on algorithmic work.
The Knowledge Management Life Cycle
As we’ve discussed, the process of knowledge management follows general steps. Today, this progression has been formalized by several scholars, along with how knowledge should be stored and disseminated throughout an organization. This includes the processes, tools, and technologies that make up the knowledge management life cycle.
While each life cycle differs in specific steps, this infographic shows the basic steps.
There are multiple, semi-competing theories of the knowledge management life cycle. However, despite the nuanced theoretical and syntactical differences, the top four all follow a similar pattern. Here are the top four KM life cycle models:
- Wiig Model (1993): This model relies on the principle that in order for information to be useful, it must be organized. Therefore, this model is primarily concerned with organizing all data once it is codified, but also outlines how knowledge is built, stored, pooled (with other stored knowledge), and then extended into the organization. The phases of the Wiig model are creation, sourcing, compilation, transformation, and application.
- Zack Model (1996): While the phases here are similar to the Wiig model, the Zack model prioritizes a logical, standardized process when advancing to each new stage. The phases of the Zack model are acquisition, refinement, storage/retrieval, distribution, and presentation.
- Bukowitz and William Model (2000): This model builds upon the previous two by expanding the definition of knowledge storage to include the infrastructure that supports this learning community (such as communication, hierarchy, and working relationships). Bukowitz and William also emphasize the need for not only maintaining your knowledge repository, but also building it over time. The phases here are: get, use, learn, contribute, and assess.
- McElroy Model (2003): Building upon the process focus of the Bukowitz and William model, McElroy is concerned with knowledge production and integration. It creates ways for team members to submit “claims” when they fail to receive or understand knowledge, all in an effort to improve group learning. The phases in the McElroy model are learning, validation, acquisition, integration, and completion.
Here’s how all the different phases of these model relate to each other:
The integrated knowledge management cycle, proposed by Kimiz Dalkir, Ph.D., combines several of the concepts we’ve discussed into one general framework. Dalkir identifies three key stages in her model: knowledge capture and/or creation, knowledge sharing and dissemination, and knowledge acquisition and application. In this model, Dalkir emphasizes the cycle aspect in order to ensure continual improvement - not only in the amount and quality of knowledge stored, but of the underlying processes, as well
How to Improve Knowledge Management
Even with an understanding of the KM life cycle, knowledge management can be difficult to implement. Below are a few tips on how to improve knowledge management:
- Understand the flow of knowledge in your organization: While the various KM life cycle models list phases to follow, they are merely a blueprint. Knowledge management will only be successful if you understand how your organization already naturally gains, stores, and shares knowledge, so use the existing flow of information to structure your formal knowledge management process.
- Clearly define your goals and how you will measure them: Just like any project, you won’t be able to judge your success without clearly defined goals. Choose objectives and how you will measure them before you implement KM.
- Encourage socialization: One way to foster an environment of knowledge transfer is to let it happen naturally, by talking and casually sharing. Allow team members to talk and form relationships in-office, which will make them more likely to turn to each other for information or advice, or to learn new skills.
- Generate new knowledge: Remember that knowledge management is not only concerned with storing existing knowledge; rather, you need to create processes to manage the ongoing stream of new information. Actively generate new knowledge within your organization not only to create a robust knowledge stream, but also to show your employees that you treat new knowledge - and their knowledge - as an asset. This will make workers more apt to adopt KM.
- Employ technology: Technology can play a huge role in standardizing and organizing the KM process. However, remember that technology itself is not knowledge management, but simply a tool to enhance your processes. We’ll discuss knowledge management technology in depth in the next section.
What Is a Knowledge Management System?
A knowledge management system is any technology that is used to store and manage knowledge - essentially, a tool to oversee knowledge management. However, a successful knowledge management system also taps into the underlying goals of KM: codifying knowledge, retrieving knowledge, improving collaboration, and stimulating overall organizational learning.
Knowledge management systems have evolved from a useful tool to optimize KM processes to an integral component of KM itself. Today, organizations rely on KM systems to perform many of the functions of knowledge management - data storage is an obvious example, but technology systems can also help foster collaboration and group learning, among other objectives.
There are several emerging features in contemporary knowledge management systems. Here’s a look at some of the most prominent ones:
- Content and document management: This is a critical component because codifying knowledge almost always results in documentation (or another form of created content). When choosing a KM system, make sure the platform can support the file size, type(s), and volume that you need. This is especially important for enterprise companies who will need to store massive amounts of data, and scale regularly. (For this functionality, look for enterprise portals.)
- Lessons learned databases: These are systems that aim to make intangible (often tacit) knowledge clear and available to other users. The focus with lessons learned databases is as the name implies: on lessons learned by experts that others should follow (think of it as best practices). The key here is to find a database that can house this kind of qualitative information and make it accessible.
- Groupware: As we’ve discussed, collaboration is a key component of KM. Groupware refers to any collaborative work platform, and is usually intended for workers who need to communicate remotely. Groupware is broken down into two categories: synchronous groupware (collaborate and update in real time) and asynchronous groupware.
- Artificial Intelligence (AI): The adoption of AI (as telepresence) into KM systems replaces the human consultants that had been analyzing the data and monitoring the KM processes. Today, cognitive computing, adaptive technology, and intelligent filtering tools, in particular, have huge implications for codifying knowledge, and will likely be adopted by more KM systems. However, the importance of human quality assurance (QA) in much of today’s data work has raised concerns about AI’s ability to fully take over this space.
Although KM systems can help automate and standardize knowledge management, there are several challenges when implementing a system. Security, data accuracy, and changes in technology (cost, implementation, usability) are focused difficulties that you should keep in mind from the get-go. A much more pervasive and ongoing challenge, however, is creating a culture of collaboration and knowledge sharing via technology. People may already be reluctant to share information with their peers, and technology can create even more barriers for doing so. Therefore, you’ll need to build in processes that support and stimulate this type of knowledge transfer.
Kevin Murray offers four key things to look for when choosing a KM system:
- Security: Information is valuable. Like any other valuable, there are people who will steal it.
- Accountability: Information is valuable only when it is accurate. Limit access. Log intensively. Test for accuracy.
- Backup: Everyone knows the value of backing up. Not everyone knows how to do it. Hiding a backup drive in the CEO's desk won't help if the building floods or burns. Creating, maintaining, and correctly storing back-ups should be an automatic functionality.
- Ease of Use: If the system is too difficult or demanding to use, people will take shortcuts. Shortcuts usually reduce security and the integrity of the information you're managing.
When discussing the future of KM systems, many critics claim that knowledge management itself is a fad. However, others think that KM and KM systems will simply evolve to meet the demands of today’s business world - likely incorporating more AI-driven systems. Murray agrees with the latter camp.
“It's no fad. It's an imperative. The amount of information being created grows each year. It has to be managed, if you want it to be useful,” he says. “KM systems will continue to increase in popularity, functionality, and ease-of-use, just like the evolution of typewriters and word processing software. Artificial Intelligence (AI) will blend into the mix even more.”
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