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The evolution of the KM technology stack

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“What does a KM technology stack consist of?”

When Norman Mooradian, assistant professor in the School of Information, San Jose State University, recently asked me this great question, it seemed like it should have a simple answer. Spoiler alert: It doesn’t, and that’s the topic of this column.

Back in the day, a KMWorld conference would have been swamped with software products labeled “KM tools,” and those would have been the answer to Mooradian’s question. Today, there are still software platforms branded with the KM moniker from companies such as Guru, Bloomfire, Lighthouse, and KnowledgeOwl, but effective KM no longer requires one of these vendors. Nothing is wrong with them or their software offerings. Far from it; they are all solid choices. But effective KM is more about blending human skills with an underlying technology stack holding knowledge assets—a stack that is far more complex today than it was 20 years ago. Similarly, there is an order of magnitude more data today than there was back then, and it continues to grow exponentially year on year.

The elements of KM

When you boil it down, KM essentially has two component elements:

1. The need to gather, curate, and create knowledge assets

2. The ability to ensure that knowledge assets are accessible, relevant, retrievable, and available at the right time and place

Looking at the second element, we naturally find a swath of enterprise search and AI technologies on offer. It’s a rich area rife with advances and innovations from startups, established vendors, and giants such as Google and Microsoft. These are, in essence, the technologies that find the knowledge asset and surface it to the end user. We may also be tempted to add generative AI (GPT, et al.) as a separate set of tools. In most cases, these require you to prompt them via a search string, and, in some cases, they read what you are working on and proactively surface knowledge assets to assist you.

But it’s in that first area—gathering, curating, and creating knowledge assets—where things get more fluid and difficult to pin down. Put simply, no two organizations are alike, nor will their sources of knowledge assets be the same. For some, knowledge assets will come predominantly from Salesforce or Workday; for others, it will be a dedicated KM or document management system. This is where it gets tricky, because few organizations have much in the way of clearly defined knowledge assets. Instead, they have mountains of data, documents, and information sources. I might argue that in 2023, few organizations even know what knowledge assets are, and that in itself is a significant concern.

Managing knowledge assets

Everyone has a definition, but most would agree that knowledge assets range from the obvious, such as copyrights, patents, policies, and procedures, to the more obscure but still critical, such as insights, learnings, strategies, and ideas. The first set is relatively easy to define and find within an organization. While the second set is harder to find and leverage, it is often as valuable, if not more so. Put another way, KM is more than simply finding facts and pieces of information; it’s about sharing relevant experiences, learnings, ideas, and perspectives. To gain access to these, we need to leverage not just the data in our IT systems but also the collective experience of the corporate body politic. To do that with any degree of effectiveness, we need human curation of knowledge assets; technology alone won’t cut it.

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