3 Reasons why Knowledge Management fails

Philosophers have been thinking about knowledge for millennia. Epistemology—the theory of knowledge—goes back at least two centuries

According to Markos Symeonides of Axios Systems, organisations have been thinking about and talking about knowledge management for two decades and most organisations know they should be making better use of knowledge.

Knowledge fuels productivity

People have an inherent understanding that knowledge is valuable to an organisation; knowledge is the foundation of competency—and competency is necessary to get things done. To perform a task and achieve an outcome, an employee must know how to do it.

When organisations are recruiting, they’re looking for people who come pre-loaded with the right competencies—which is why they look for university degrees, industry certifications, and past experience of similar roles. Knowledge means competence. Competence means productivity. Productivity means profitability. However, there is no such thing as a “perfect employee”; one who knows everything they will ever need to know to get the job done.

Less re-discovering, more customer service

In functional teams where there are many people doing similar kinds of work—like the service desk—there is a compelling argument for passing on the lessons learned by one agent on to the other agents. By sharing knowledge they can all spend less time re-discovering the know-how and more time on providing great IT customer service.

Knowledge Management—the process of capturing, curating, and sharing knowledge—is all about scaling up the use of what your team already knows—and has just found out—to reduce waste and improve productivity.

Organisations should implement “outside-in” design principles with the user experience in mind leveraging contextual placement in the user journey. Giving the right knowledge to the right users at the right time drives success.

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Increasing information consistency and ease-of-access by consolidating customer-facing and agent-facing knowledge bases helps reduce knowledge management programs overheads.

Using knowledge management solutions that include search engine optimization capabilities and conversational artificial intelligence (AI) for new types of “conversational” engagement through Virtual Service Agents / ChatBots further enhances the gains as well.

Reasons Knowledge Management programs fail

A knowledge management program is one of many ways in which a service desk can dramatically improve performance, but in many cases these programs fail. Why?

Reason #1: Excessive focus on “Filling the Knowledge Base”

There are four key components to an effective and sustainable knowledge management program:

Collection – Capturing knowledge in a knowledge base so that it can be referenced by the whole team/group/organization.
Connection – Most knowledge resides in people. No matter how much time and effort you spend populating a knowledge database, the scope of your Knowledge Management Database (KMDB) will never get even close to the collective wisdom of your team, which is why it is necessary to enable collaboration so that people can pull knowledge from subject matter experts.
Curation – Ensuring the accuracy and usability of knowledge artefacts. Incorrect information can be useless or even dangerous.
Search – Knowledge artefacts serve no purpose and have no value if nobody can find them when they need them. Knowledge artefacts should be optimized for searchability and users need an effective search engine with which to find what they need—fast.

Many organisations take a quantity-over-quality approach and simply start by stuffing the knowledge base with low-quality content which helps nobody. This most commonly happens when IT staff are measured on—and maybe even financially rewarded for—knowledge capture. When people are evaluated on the number of knowledge artefacts they are creating, people will game the system by creating large numbers of low-quality records.

To counter this, you will need some sort of mechanism to grade the quality of knowledge artefacts. For example, a “This solved my issue” button or five-star rating system which knowledge consumers can use to provide feedback on quality.

Reason #2: Under-resourcing your KM program

Getting the four key components right—collection, connection, curation, and search—is not easy. Organisations often fail to appreciate the effort that is required to build and sustain an effective knowledge management program which achieves the intended transformational value.

Firstly, building and sustaining a KM program requires organisational change management (OCM). Anything which requires OCM needs authority behind it. Without executive buy-in and committed support (e.g. visible involvement, not simply support in name only), KM programs fail. Few organisations have the broad grass-roots enthusiasm it takes to build and sustain a bottom-up KM program without at least some top-down direction and coordination.

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Secondly, “orphaned” functions never last. If nobody takes ownership and responsibility for a thing, it will die on the vine. Somebody must be responsible for the overall KM program. In smaller organisations, one person may look after all aspects of the program. In very large organisations you may need a team to look after the various aspects of collection, curation, collaboration, search optimization, and the tools which support these.

Reason #3: Forgetting about continuous improvement

Ideally, your knowledge management program should start small, focusing on capturing and sharing the knowledge that is most valuable. Look at your top 10 or 20 repeat incidents and requests and capture the knowledge that is needed to resolve them. This may be for end user self service as FAQs or for IT Support Staff. When you streamline the resolution of your most frequent incidents & requests, you’ll quickly notice the difference.

This means being pro-active to begin with: going out to find who has this how-to knowledge, capturing it, curating it, and publishing it somewhere that it can be found by the people who need it. These knowledge candidates may become Problem Management candidates should there be ways to eradicate the root cause.

Start with a simple and lightweight process of capture-curate-publish so that you can keep tight control over what goes into your knowledge base in the initial stages. Later, you will want to open the capture of knowledge and run crowdsourced artefacts through a quality-control checkpoint to ensure they are findable and usable.

Some rudimentary reporting will be required from an early stage: How many artefacts do you have in the KMDB? How many are used per month? What is the average quality rating? How does knowledge use correlate with average call times?

With basic elements of collection, curation, and searchability in place, you will already be seeing some value from your KM program. At this stage, you will want to dig into the performance of your KM program, quantify the effects it is having, and build on what you have already achieved. It may be time to tackle the connection aspect so that people can tap into the knowledge that exists in people’s heads.

However you lay out your knowledge management roadmap, the critical success factor is this: A knowledge management program relies on quality of knowledge. Knowledge quality curation needs to cover four key aspects to ensure high quality knowledge and a high-quality experience for the knowledge consumer:

Non-existent – Users can’t find the knowledge they need because it doesn’t exist in the knowledge base. Knowledge management often has to play catch-up when new applications or services are brought online. To get around this, the Knowledge Management function should be integrated into the release process to ensure appropriate information is created (or updated) in the KMDB—right from the launch of the service.

Hard to find – When time has been spent creating information and it can’t be found then this is another frequent type of failure: it can’t be found because either the search function is poor, or the content hasn’t been optimized for searchability (it doesn’t contain the keywords that people are looking for). In this case, the work has been put in to create the knowledge artefact, but the value doesn’t materialize because if it can’t be found it can’t be used.

Hard to understand – When articles are written in complex technical language, they are only suitable for technical people. If you want to extend knowledge articles out to your employee base, they must be written in plain language and follow a simple step-by-step format.
Out of date – Old knowledge often relates to out of date services or software versions. Where possible, include a publication date and details of which versions the knowledge artefact relates to so employees can quickly assess relevance to their issue.

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