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What Is Knowledge Management?

Michael O'Dwyer| March 10 2020

| IT insights

In this article, we dissect the term 'knowledge management' and discuss how it relates to information and data.

Rather than rehash the explanations of others or debate the origins of the term, let’s begin by stating that knowledge management (KM) is a means of collecting and analyzing direct and indirect data/information. The collection of this data is followed by the collation and dissemination of results in the form of actionable knowledge to involved parties that, in turn, aid decision-making and drives business objectives.

The above is my definition. It’s not concise, is packed full of jargon, and it covers most of the facts. But not all. Salesforce could conceivably be considered a knowledge management tool for customer service and sales, for example. We’ll look at the entire KM process later, after a brief interval to explain the difference between data, information, and knowledge.

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Knowledge Management vs. Data/Information Management

Data/information management is not the same as knowledge management, and not all information is data. Let’s lean on my prior experience in the electronic subcontract-manufacturing sector to provide a practical example of knowledge management in action.

Imagine a typical production floor with four lines building an identical product, a smartphone, for example. All smartphones involve multiple manufacturing processes; you have the printed circuit assembly (PCA), enclosures (plastics), and display as well as interconnecting ribbons/cables and connectors. Depending on the equipment used, the process is divided into a sequential and a mostly logical series of steps, involving automated and manual tasks. Each step is based on supplied and evolving documented procedures and work instructions (information) with each PCA having a serial and International Mobile Equipment Identity (IMEI) number (being a telecom product) assigned during the initial test process. Each step is recorded, creating data (such as test data, rework information, and responsible parties) directly linked to the PCA and the general operative or engineer involved.

The result is that if a smartphone fails in the field or during a pre-shipment audit, all data is available instantly for every stage in the manufacturing, testing, and logistics process. Did the unit fail at any stage when built? Were components changed or reworked? Are there any failure patterns visible for other units? Is it a known software/firmware issue fixed by a simple over the air (OTA) update?

In most cases, the OTA update will solve user issues, but if not, analysis is necessary until a solution is found. This solution becomes knowledge that is made available to all for future support queries for that particular issue. In addition, if the solution involves a possible improvement in the production process, the corresponding documentation is updated for future builds, forcing continuous improvement.

Of course, not everyone is involved in manufacturing, but the same techniques can be used for companies of all sizes, regardless of industry.

The Knowledge Management Process

Ignoring the tools or resources used, the basics of a knowledge management process remain unchanged. In fact, it’s likely that you already employ KM elements in many areas, given that most organizations have an ISO certification that includes a quality manual and defined document control processes.

1. Data

Data collection methods and tools vary from company to company but can involve daily or weekly reports and logs, attendance reports. Once collected, it is then organized for a more straightforward review using rules or methods favored by your organization.

2. Information

Detailed information can be summarized according to business or operational requirements. Outputs could include charts and other visual representations of the data or anything that results in a ‘big picture’ view of the data. Once this is accomplished, the info is analyzed for patterns, relationships, or areas where improvement is possible. At this point, expert review usually results in several formal reports (from various perspectives or departments).

3. Knowledge

Multiple reports are combined to derive new conclusions, often relating to the entire operational process. New concepts and process flows are defined in the organization’s knowledge base and are easily accessed by all users (on an intranet if internal, for example). Knowledgebases can include FAQs, use cases, webinars, white papers, and other documents.

Finally, the knowledge gained can aid future decisions. If you have a stable process for one activity, much of the acquired knowledge is available when setting up a new activity, i.e., there’s no need to reinvent the wheel.

Knowledge Management in the Cloud?

Every organization can benefit from knowledge management. As you can imagine, implementing a comprehensive solution is not a trivial task, often requiring dedicated servers, expensive software licensing, and resources. It’s for this reason that KM solutions in the cloud (such as Salesforce) are very popular. Costs are reduced. Ease of access is another plus as users can access the knowledge base from everywhere.

However, the usual risks apply to using cloud solutions, not least of which is security and loss of IT control. One projection (via press release) indicates that the global KM market will be more than $1.2 trillion by 2025.

If you wish to review possible knowledge management solutions, make your choice based on desired functions. Most have a trial. Some are aimed at customer support only while others are more robust. Note that knowledge management can focus on people (customers, for example), the technology required to share and analyze data or the process.

You may need elements of all three, requiring multiple solutions and integrations. How you proceed will depend on organizational goals. Whatever you decide, remember compliance and legislative requirements for health, financial, and personally identifiable information (PII).

Before employing AI and other techniques to extract information from unstructured data, consider just how much data you wish to collect. I recommend only collecting data that are needed to satisfy operational activities.

Topics: IT insights

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THIS POST WAS WRITTEN BY Michael O'Dwyer

An Irishman based in Hong Kong, Michael O’Dwyer is a business & technology journalist, independent consultant and writer who specializes in writing for enterprise, small business and IT audiences. With 20+ years of experience in everything from IT and electronic component-level failure analysis to process improvement and supply chains (and an in-depth knowledge of Klingon,) Michael is a sought-after writer whose quality sources, deep research and quirky sense of humor ensures he’s welcome in high-profile publications such as The Street and Fortune 100 IT portals.

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