Knowledge management as applied in modern organizations is both a simple concept and a wide-ranging one. Definitionally, it can be boiled down to the effort to manage information and resources to improve the organization’s efficiency. But this effort can of course be applied to any number of specific issues or aspects of an organization.

In this piece, we’re going to explore the use of knowledge management for the navigation of an overabundance of information within a business.

Can There Be Too Much Data?

Any discussion on this topic needs to start with the logical question, which is whether or not there’s any such thing as an overabundance of information. Or, putting it another way, is there such a thing as having too much data for a modern company?

A lot of people might be inclined to impulsively answer in the negative. In our increasingly digital world, we’re perpetually inundated with discussions about the importance of data in business. We understand that robust data operations yield invaluable insights, and we hardly think to question whether there might be an upper limit on how much information is actually useful. The general perception is that the more data a company can produce, the deeper its insight will be — and the deeper the insight, the greater the opportunity to enhance efficiency and improve operations.

There’s a certain logical sense to this, but the truth is that there is such a thing as generating too much information, to the point that it becomes an inefficiency unto itself. As one discussion on data saturation put it, it’s actually a problem that is “everywhere” in modern business. This is because “the rapid rise in our ability to collect data hasn’t been matched by our ability to support, filter, and manage the data.” These comments were made with specific regard to marketing departments, but they do a nice job of summing up the problem more broadly. Companies that focus too much on gathering data in large quantities wind up with more than they know how to make sense of, and thus struggle to find meaningful insights.

How Knowledge Management Can Help

The most fundamental way to think about how knowledge management can be applied to this problem is to slightly tweak the definition provided above. As stated, KM is typically about the effort to manage information to improve efficiency. In this case, however, we might think of it more as the effort to make the management of information more efficient in the first place, so as to improve the quality of business insights. It’s a subtle distinction, but one that establishes a helpful way of thinking: a focus on turning the scattershot collection of information into a more targeted and productive effort.

This is something that’s easier to theorize about than to put into practice. It is actually easier, at this point, for organizations to simply cast a wide net and gather all possible information that might pertain to company performance — from internet activity, social media, internal performance, and so on. Where KM comes into play, however, is actually in narrowing that collection process to focus only on what is relevant, pertinent, and ultimately useful. It is essentially a filtration process that narrows the parameters of data collection in ways aimed at generating only the most helpful information, and avoiding excess clutter.

Naturally this is not an exact or flawless process. Some excess data without particular utility will trickle through. But by making the actual collection process more efficient, an organization can effectively apply KM as a solution to this problem.

How to Implement Knowledge Management

As mentioned, this sort of solution is easier to develop as a theory than to implement as practice. However, there are simple and strategic ways to go about the application of KM.

One is to turn to an employee or team specifically trained to handle data-related needs. For some companies, this might mean hiring data and/or analytics experts from the outside. Others, however, may find it more efficient to train internal administrators for the task at hand. Today, this sort of training is accessible via online business administration degree programs that make it easier for working professionals to study and learn new skills without having to quit their jobs. These programs prepare students for a number of different business tasks, but operations management, data research analysis, and marketing coordination are among them. Any of these specialties can help a company employee to gain expertise in data-related practices, and thus prepare said employee to direct a KM effort.

The other, similar but perhaps simpler option is to establish and train people in what are sometimes referred to as gatekeeper roles. Beyond the general definition that inspires the term, the gatekeeper concept is one more commonly associated with product development. Basically, the idea is that someone in a gatekeeper role controls the flow of information between stakeholders and project development teams, so that there isn’t excessive information or pressure moving one way or the other. And the same concept can be applied to data operations as a form of KM. Essentially, an organization can train gatekeepers to recognize what is pertinent and what is not in data collection, and thus — with relative ease — cut down on the clutter. This in turn makes an entire data operation more efficient.

In the end, the effort can be more complicated than how it is presented here. Particularly where larger organizations are concerned, data operations tend to be vast and multi-faceted. Applying KM across the board takes a thorough, effective strategy. The foundation for this strategy, however, is understanding the problem and the ways in which knowledge management can be implemented to solve it.

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Article specially written for kminstitute.org

By Alicia Thompson