Your first day on a new job offers a great opportunity to understand knowledge management (KM). You get to work and observe that everyone just seems to know what the company does and why various details of the work are important. For example, imagine that you start working at a toy company in July. Your coworkers are in a big rush to finish the wholesale catalog.
Why? It’s July. What’s special about July? Well, if you have worked at that toy company for a while, you will simply know that toy stores start to order their Christmas merchandise in August and September… so it can be manufactured and delivered by December. Duh! Doesn’t everyone know that? No, the July deadline is a bit of company knowledge. Establishing a system so that everyone will know things like this, without having to ask, involves KM.
KM is a multidisciplinary discipline that aims to create and share knowledge that is particular to a given organization. The goal of KM is to preserve organizational knowledge so that employees can make good use of it in serving customers and being innovative in product development, operations and so forth.
KM as an established discipline dates back to the early 1990s, a starting point that is not a coincidence. Three trends were converging at that time, which coalesced around the idea of capturing and managing knowledge. One trend was the emphasis on “core competencies” as the basis for competitive strategy. The prior generation had seen the rise and fall of the conglomerate model of business. As conglomerates spun themselves off into focused corporations, managers noted a disturbing pattern that threatened the core competency model: employees, the oldest of the baby boomers, were starting to retire. As they left the business, they took vital knowledge with them.
Knowledge was walking out the door, and with it, competitive advantage. This was not for a lack of data. There was plenty of data, but not enough knowledge. Business leaders found there was a gap between data and useful knowledge.
For example, a maker of musical instruments might have a database of raw materials to order to keep the production line running. However, it’s not enough to know that you have to purchase centimeter-thick Walnut boards to make a guitar. That’s data. Knowledge is about how to turn that board into a guitar—and not just any guitar, but a good one, the kind that makes your company stand out in the market. The subjective processes that turn a board into a guitar might be locked in the heads of workers who are retiring, quitting, or dying. Once they are gone, the recipe for making a good guitar will be permanently lost. KM sought to solve this problem.
The third parallel trend at the time was that of accelerating computing power. Though the computers of 30 years ago may seem slow and quaint by today’s standards, they were revolutionary at the time: cheap, fast, and ubiquitous. Everyone in the company could have one, running knowledge management system software, into which they could enter all their special, hard-learned corporate knowledge.
Companies set up knowledge management departments. They deployed consultants and KM frameworks to capture knowledge. Executives tried building knowledge sharing cultures inside their organizations. In some cases, they even offered incentives for knowledge capture.
Then, reality set in. Unfortunately, many KM programs floundered as companies tried to extract knowledge from employees. Reasons for this varied, but the primary issues related to time, trust, and personal abilities. Incentives notwithstanding, most workers did not have the time to sit down and think about their knowledge and reliably commit it to KM systems. They may have lacked the communication skills to describe what they knew.
Furthermore, even if they wanted to, they might not have felt comfortable doing so. Specialized knowledge is job security. A worker might think, with some justification, if I share my secret of steaming Walnut to make a guitar, you’ll fire me and hire someone cheaper. In tandem, corporate reorganizations and shifts in product design added chaos to an already shaky KM initiative. A knowledge base developed at significant expense might end up being incomplete and obsolete.
As knowledge management strategy evolved to overcome the early KM failures, KM managers began to look at the issue from an entirely different perspective. Instead of asking employees to share their knowledge, companies would capture knowledge implicitly, by indexing data and offering it up in an intelligent search process.
The underlying assumption for this process is that knowledge is often contained in data, if one knows how to parse and structure that data. In the guitar example, you might be able to determine the knowledge about the correct way to steam a Walnut board if you have all the sensor data from the equipment used to make the guitars. By running analytics on the data, you can derive the precise formula for manufacturing. For this to work, you have to have data classification, data search and data access capabilities along with analytics software that can interpret the raw sensor data.
Another example of search data translating into KM comes up in customer support. An enterprise search solution can be equipped with integrated knowledge discovery to help customer support people quickly turn seemingly random data points about a customer into coherent, useful knowledge. To achieve this outcome, the search tool must be able to search critical systems like Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP), along with unstructured data repositories. Search relevance is essential in this process.
Alternatively, search technologies can automatically generate deep knowledge bases. Using Artificial Intelligence (AI), search-powered knowledge discovery software can access, surface, and structure insights from numerous data repositories. Customer support people, or anyone else in the organization who needs to know something, can search the resulting knowledge bases.
It might be fair to say that KM now resides in the concept of the information-driven organization. This is a workplace where people have immediate access to actionable information presented in context. They can leverage enterprise search solutions to surface insights that inform their decisions. It can happen without anyone having to key their knowledge into a dedicated KM system. Rather, the information comes out of intelligent search processes. Success requires advanced search, coupled with Natural Language Processing (NLP), AI, and unstructured data search capabilities.
Why should you bother with making information-driven decisions? This may seem like a stupid question, in that a business usually wants to make decisions based on good information. The reason we need to ask, however, is that so many companies apparently don’t actually value making information-driven decisions. They make decisions based on bad data, a scenario where a lack of knowledge results in sub-optimal outcomes.
Running a business is all about making decisions: what to tell a customer, how to price a product, how much material to order, and on and on. Each decision, large or small, contributes to the overall success of the business. Order too much material, you’re wasting money. Price a product too high, you won’t sell enough. Price it too low, you’re giving way profits. Information steers these decisions in the right direction.
KM is not dead. While some of the techniques used to realize its potential fell short of their goals, the overall idea of KM is more valid today than ever before. As employees come and go more frequently and market cycles speed up, companies urgently need to capture and preserve knowledge to compete and stay profitable. Success will come from taking an implicit, rather than deliberate approach to KM. Instead of asking people to share their knowledge, let advanced search solutions glean the knowledge from data that exists in corporate systems. This way, your company can become an information-driven organization that provides its people with the knowledge they need to do their job successfully.
Related blog posts.
Sinequa prides itself on our state-of-the-art technology and our continuous innovation - and our crack team of machine learning experts, developers, and linguists are constantly breaking new ground to improve the ...
Today more than ever, enterprises are dealing with fast-growing volumes of data. Trying to find meaningful answers in all that data is becoming a greater and greater challenge. It's no secret ...
The information enterprises need to grow and transform their business is within reach, but they don’t leverage most of it.Enterprises possess a wealth of data. Structured data is typically easy to ...