Recently, a prospective Northern Light client explained what she hoped to achieve by deploying a SinglePoint™ knowledge management platform across her organization. Some of the themes she mentioned were quite familiar to us – for instance, her company hoped to break down “knowledge siloes” that were preventing valuable information resident within one department or team from being easily shared enterprise-wide. However, one thing she said was a bit different from the norm: “People at my company do not know what we know and what we don’t know.”
Essentially, she was describing the need for greater awareness of knowledge gaps. It’s a corollary to what we typically think of as the primary purpose of a knowledge management platform: to organize and make accessible an enterprise’s knowledge assets – in the case of SinglePoint, various content sets vital to market and competitive intelligence work. But knowledge gap awareness effectively turns the telescope around. It focuses on what isn’t there, forcing people to consciously acknowledge information that is missing from their realm of understanding. This can come in two “flavors”: things you actually know you don’t know but routinely gloss over (e.g., how does a zipper work?); and things you don’t know you don’t know (a true “blind spot”). The latter is far more problematic, because it results in questions not asked and topics not considered.
Many large enterprises have vast research resources, including valuable subscriptions to third-party market reports and analysis, which frequently go underutilized by rank-and-file employees. (The reasons why are for another blog post!) Answers to myriad unasked questions often reside within those documents. But if a person doesn’t know to ask, what is the value of the information buried there?
Today’s sophisticated knowledge management systems, like SinglePoint, can be helpful in surfacing and closing knowledge gaps.
- SinglePoint finds information a person doesn’t know to look for – Using artificial intelligence (AI)-based machine learning, SinglePoint learns what a user is interested in based on what they download. SinglePoint’s machine learning algorithms then build a semantic model of the user and compare it to the semantic models of each document that are added to the research collection. Based on this comparison, SinglePoint recommends content to the user. With this Recommended Reading List, relevant content will in effect come and find you rather than you having to go look for it.
- SinglePoint offers one-click “More Like This” – By building a semantic model of each document, SinglePoint automatically finds additional content for the user that will address the same topics in the same weighting as a document of interest, and presents it with a quick click of the More Like This button.
- SinglePoint distills and presents strategic insights, not just documents – SinglePoint automatically summarizes search results to make it easier for a person to get to the crux of an issue. The search engine reads all of the documents and summarizes the most significant ideas contained in the documents on the search result into an Insights Report. The user can express an interest in knowing more about a topic—what used to be called a “search query”—and then the system delivers a report rather than just a search result. The machine does the search and then tells the user what it finds that the user should know.
At the end of the day, it is important (and sometimes humbling) for people to admit what they don’t know, as in the mechanical intricacies of a zipper. It is far more difficult to uncover our unknown knowledge gaps, which, in a business context, may seriously impede our ability to render sound judgments and make effective decisions. Fortunately, today’s increasingly intelligent knowledge management systems can provide assistance.
To learn more about how SinglePoint can help your organization to spot knowledge gaps that may impede effective business decision-making, contact Northern Light.