New Paradigms Bring Value to Knowledge Management

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On July 29, Oracle acquired InQuira—the last independent knowledge management vendor in the CRM world. 

In the way we talk about it, knowledge is useless. Seriously, there is no value whatsoever to the knowledge we produce and manage via knowledge management. While this statement is bound to anger most of my vendor clients and confuse end users, please hear me out.

Without going too far in time, we can safely say we started this knowledge management trend 25 to 30 years ago when we discovered we were no longer “industrial era” workers but, rather, knowledge workers. The theory was that if we were valued for our ability to create, store, and reuse knowledge, we could build systems that would allow those around us to leverage that knowledge. Fast-forward 10 to 15 years, and the automation of this knowledge via self-service systems became one of the first things we could envision the commercial Web doing well.

Can you imagine, we said, if any client could find what he needed without the help of an operator (back then, we were still mostly phone-based—same as today, actually)? The cost of a self-service transaction was quoted by vendors to be as low as 1 cent. One cent! There was no way you could beat that. We set out to solve the main problem we thought we had: how and where to store the data, and how to find and retrieve it.

Hardly simple problems. In fact, we had been battling those questions for the better part of two decades in helpdesk applications, call center packages, and even help systems embedded in software applications. We had mastered the art of indexing content and doing Boolean searches by keywords, by phrases, or—the revolution back then—by natural language. The explosion of self-service solutions brought a wave of investments into natural language processing (NLP) tools and solutions.

This influx of cash helped advance NLP, with some of that money devoted to figuring out the problem of how to use speech as an entry point for NLP-based systems. With new models and techniques for NLP and speech recognition, we were able to advance from 40 percent to 45 percent out-of-the-box comprehension without training to between 50 percent and 60 percent. Even better, with appropriate training, we could increase the understanding to almost 80 percent in certain cases.

However, this enhanced recognition did little for self-service. Adoption was great at first, as people rushed to deploy solutions seeking to cash in on those low costs per transaction. That great reception was tempered almost immediately, however, as reports emerged that self-service solutions were unable to find the right information. New models for navigation and searching were used and NLP continued its progress, but the right answer remained elusive. 

We realized that the problem was not the search, but what we were searching. The stored knowledge that the NLP queries were retrieving had two major problems:

It was obsolete; I am fond of saying that data and knowledge have depreciation rates of 50 percent per minute. This means that after two minutes, the value approaches nil. That may be tongue-and-cheek, but in reality stored knowledge does not fare much better. Organizations that deployed self-service solutions quickly found that their knowledge could not answer the questions before it had to be changed.

It was inaccurate. Even when knowledge is created and stored within a time frame that would allow it to be used before it becomes obsolete, the chances that it is accurate and responds to the query are small. The ever-increasing complexity of customers’ problems makes it difficult for the data to answer all the elements in the questions posed, resulting in a small percentage answered by the stored knowledge.

That is an interesting problem (the word that should replace “interesting” is unprintable). Organizations looking to leverage self-service solutions invested in complex knowledge creation and management schemes to improve the accuracy and reduce the dated nature of the knowledge. Conversational engines, “chatbots,” virtual agents, content management, and contextual queries all were deployed in an all-out effort to leverage the investments’ traditional “knowledge management” solutions. All of these failed because they continued to rely on the same purported solution that had actually become the problem: storing knowledge.

The acquisition of InQuira brings a new world of opportunity to answering client questions. With the disappearance of the last independent vendor, we can effectively kill the practice of “store-and-search” knowledge and focus on better ways to answer clients’ questions. Knowledge is only valuable when used, not when stored for potential future use.

These new knowledge paradigms are focused on delivering answers, not knowledge. They will employ the knowledge that people possess to provide answers, but they won’t store that knowledge for future use. The focus has shifted from the knowledge being managed to the knowledge worker being empowered to act on that knowledge and provide a solution.

A great example can be found in the use of online communities to answer questions. While traditional forum-style communities were considered the entry point for knowledge creation and storage and were leveraged to create bigger knowledge bases, the new communities are more like talent directories—go to the community, find the person who has the answer to your question, and quickly find the answer. Of course, in each community of peers, there are bound to be more than a few people who can answer, and this is where knowledge is improved. While the first expert may provide you with an answer, it is the commentary and secondary information provided by the other members that becomes invaluable.

Indeed, by leveraging tribal or community knowledge, we accomplish two things: We aggregate the knowledge of all the experts, thus making the answer more accurate. And we are able to make updates as appropriate, creating a knowledge exchange. As a result, the information is less dated. At first glance, the online community model of knowledge, and the new knowledge paradigm of knowledge in use (and not stored), has more value. Of course, the explosion of social networks and online communities in recent years has dramatically increased the value of this model for knowledge use by bringing more experts closer to the user.

Finally, we may have found a good way to leverage the knowledge we have been building for all these years. What do you think? 

Esteban Kolsky is the founder and president of ThinkJar, a customer strategies research firm helping organizations become more social and better at working with social customers. While some of the vendors mentioned in this story may be active or inactive clients of ThinkJar, none of them sponsored or provided any compensation for this column.


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