Knowledge Management

Knowledge Management Definition

Knowledge management (KM) is the interdisciplinary process of creating, using, sharing, and maintaining an organization's information and knowledge. It is a multi-faceted strategy for making the best use of organizational knowledge assets in order to achieve business objectives such as enhancing competitive advantage, improving performance, boosting innovation, sharing insights, and continuously improving the organization.

Knowledge management systems are therefore part of the organizational learning process, although they focus more on strategic management of knowledge as a shareable business asset. The core goal of knowledge management is to connect people looking for knowledge within an organization to those who have it, with the ultimate aim of increasing the overall knowledge level of the team and organization.

Four knowledge management objectives assist in reaching that goal. Those goals are: improving the knowledge capture process, streamlining and enhancing the knowledge environment, increasing access to organizational knowledge, and maintaining knowledge as an organizational asset.

Knowledge Management Strategy diagram displays the lifecycle of knowledge items within a business.

FAQ's

What is Knowledge Management?

Knowledge management is the process of more effectively collecting, sharing, maintaining or managing, and deploying organizational knowledge. As a discipline, knowledge management recognizes three basic forms of knowledge: explicit knowledge, tacit, and implicit knowledge.

Explicit knowledge is skills or information that can readily be understood, articulated, and shared with others. Explicit knowledge is also referred to as formal knowledge or codified knowledge. An example of this kind of codified knowledge is a company's handbook or a procedure manual—information that is known well enough to be recorded and preserved easily.

In contrast, tacit knowledge is more difficult to articulate, understand, and share with others. Typically tacit knowledge includes things like innovative thinking, the ability to intuit and understand what industry-specific body language means, or how aesthetics work within a vertical.

There is also implicit knowledge—a third knowledge category. This information is a hybrid kind, in that it is able to be codified in ways that tacit knowledge can't be, but it hasn't been yet. Implicit knowledge can be taught, but hasn't yet been captured in the right ways.

There is another way to categorize types of knowledge to better define knowledge management: knowledge may be conceptual, expectational, factual, or methodological. Conceptual knowledge describes systems and perspectives. Expectational knowledge is linked to hypotheses, expectations, or judgments about things. Factual knowledge is observable, measurable, and verifiable data. And methodological knowledge relates to problem-solving and decision-making.

Benefits of Knowledge Management Systems

Deeper, richer, more transparent communication is one of the benefits of knowledge management systems and tools. To promote successful research and development, for example, this kind of communication is essential.

Knowledge management systems break down data silos and enable more workers in an organization to do more with institutional knowledge.

Better communication within the organization also tends to translate into improved ability to communicate brand, service, and product value to outsiders. Knowledge management also helps organizations protect intellectual capital, making the most of the knowledge base that already exists.

Heightened situational awareness is another benefit of using a KM system. In order to make smart business decisions, organizations must be aware of all of the important details of any given situation. Knowledge management enables better situational awareness by making more information available to more decision makers in a timely way.

It is possible to implement knowledge management systems enterprise-wide in many verticals. How that implementation looks can differ, depending on company size, industry, and other factors. Hypothetical knowledge management system examples can further elucidate this point.

For example, for younger or smaller organizations, knowledge management is often important to solidifying an advantage in a competitive market, enabling better storage and codification of internal knowledge from the start. Larger and even worldwide organizations use knowledge management to make smarter decisions as quickly as possible, gaining their own advantage.

Business Intelligence vs Knowledge Management

Organizations use the business intelligence process and its technologies and strategies to analyze current and historical data. The aim of this analysis is to provide a competitive advantage and enhance strategic decision-making power.

Knowledge management shares those goals, but focuses not only on enterprise data, but on all organizational knowledge.

Knowledge Management Tools and Technologies

Knowledge management tools and technology tend to fall into several categories. Knowledge management software or groupware facilitates organizational information sharing and collaboration. This kind of KM tool includes communication apps, document management systems, and other software for document sharing. Workflow, document management, and content management systems are also knowledge management tools because they enable the information maintenance and sharing processes.

Technologies for virtual meetings and eLearning platforms are also digital knowledge management tools that can help organizations share knowledge—especially knowledge that is tougher to codify.

Finally, knowledge management strategy tools such as ontologies help encode meaning as users attempt to comb through organizational information. To assign information the correct meaning and catalog it so it can be used, it is essential to have the right semantic technologies in place.

Data Management vs Knowledge Management

The information knowledge management spheres are often conflated, but they are not precisely the same. Data or information management relates to one cycle of activity within an organization, rather than the entire body of organizational life.

Specifically, information management concerns how the organization acquires information, from whom the information is acquired, how that information is stored and distributed, and ultimately how the information is either stored or destroyed. In other words, information management is solely focused on digital data information. Knowledge management focuses on the entire body of organizational knowledge, including explicit, tacit, and implicit knowledge concerning company culture and other non-data assets.

Knowledge Management Best Practices

For companies looking to implement smarter big data and knowledge management solutions, it is wise to keep knowledge management best practices in mind. In general, two opposing knowledge management strategies, push vs pull, describe how organizations take on the process. A push strategy emphasizes active knowledge sharing without requests, while a pull strategy centers only on as-needed knowledge transfer.

Beyond that, effective knowledge management relies upon best practices in four areas: content management, location of expertise, lessons learned, and communities of practice, also called CoPs.

Content management or enterprise content management means making all organizational information and data readily available and user-friendly. Content should be easily accessible via portals, dashboards, and content management systems or CMS. Content must be searchable, well-indexed, and linked to external information for a seamless information experience.

Location of expertise means knowing who understands an organization's pain points, who has the expertise to cope with them, and how and where to find those experts. Knowledge is not always able to be codified, so experts may be the only source of some important information for your organization.

Knowing when this is true and how to find it is among knowledge management best practices. Expertise location strategies might include identifying expertise among existing employees in a database, either by allowing employees to self-identify, or via algorithmic analysis of resumes, electronic communications, or other data.

A lessons learned database captures operational knowledge and renders it accessible. Instead of letting this kind of day-to-day knowledge fall by the wayside, the best practice here is to actively capture knowledge in the form of metrics, case studies, and more, and attempt to codify it in a database.

This is a move in part toward eliminating the problem that occurs when experienced workers leave positions and create a knowledge gap. It is also a recognition that there may be more than one “best” practice in any given situation, and so instead offers multiple lessons learned as guidance.

Communities of practice or CoPs refer to smaller groups within an organization that congregate to discuss or share knowledge. These social learning clusters are like virtual water coolers for large organizations and assist in the knowledge creation, knowledge transfer, and knowledge management processes.

What is the Knowledge Management Process

Each knowledge management process is unique, but certain characteristics are common to most scenarios. In the first stage of knowledge management for most organizations, decision makers will decide how to deploy new technology to use organizational knowledge and information more effectively. This is why information technology IT is so critical to knowledge management capture.

In the next stage, it is important to assess how the organizational culture and HR team can and should incorporate the cultural and human aspects of company knowledge into the system. In other words, no matter how progressive or technologically advanced an organization is, without planning for the human and cultural aspects of knowledge creation and management, essential information will be lost.

Specific concerns in this area include how the organizational culture rewards knowledge and information sharing. In some situations, the organization's culture may require enrichment or modification in this area, such as adjusting the compensation scheme to reward information sharing. This might also include the adoption of more user-friendly systems and knowledge management resources.

Finally, content management and information taxonomy are the critical last step of the process of knowledge management. This step is essential to ensuring information is retrievable, well-understood and described, and known throughout the company.

Big Data and Knowledge Management

Since content management and taxonomy are so important to any knowledge management approach, machine learning and data analytics are also playing a greater role in this area. These tools enable more powerful enterprise searches with more effective results, which means organizational knowledge is both more accessible and retrievable without expert archivists or taxonomists.

Organizations today must grapple with voluminous internal records, valuable proprietary information, and a veritable world of data from outside, both structured and unstructured. This means that there is a critical need to organize, taxonomize, and eventually retrieve this data—which is where machine learning and powerful data analytics come into play. The benefits of big data and knowledge management are now important for businesses of any size.

Does OmniSci Offer a Knowledge Management Solution?

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