There is information everywhere. It’s in our emails, notes, and office workspaces, primarily virtual ones like Slack and Teams. The sheer volume of information available when looking for content in a knowledge management system(KMS) might be intimidating. It gets even worse, as we often document information in other applications used in our organizations.
For example, we might have long conversations in Slack and need help documenting in Notion or summarizing in a central KMS where others can easily find what was talked about. All these present a big problem in KMS – Searching or finding relevant information needed to perform a task or be productive. This may result in a lack of confidence in the system and have a detrimental effect on output and effectiveness.
This article will review how Search in KMS is broken, the issue it presents to productivity, and possible solutions.
Any knowledge management system must have a search as it enables users to find and access the information they require quickly. Unfortunately, the search functionality of many knowledge management applications has a number of shortcomings that can make it difficult for users to quickly and effectively retrieve the information they require.
Performance issues with knowledge management tool searches are a typical issue. When a workspace has a lot of pages, it can take a while for a search to yield results, which can be frustrating and lower productivity.
The scope of the search is another problem. Many knowledge management applications don’t offer a global search capability that can find information anywhere in the workspace; instead, they only allow users to search within a specific page or set of pages. This can make it challenging to discover specific pieces of information, especially if they are not identified in the page’s title.
Another typical issue with knowledge management tool search is keyword-based search. Finding important information that doesn’t use the exact phrase can be challenging because this type of search only displays pages that have the exact keywords mentioned in the search query. For instance, a document page titled “Support” that contains information on customer service will not appear in the search results if a user is looking for pages about “customer service.”
There are advancements in Natural Language Processing techniques that have shown excellent results in identifying documents, rephrasing essential ideas, and summarizing lengthy texts. Users can now ask complex questions and get curated answers to only useful texts from large documents texts. This technique is known as question-answering. Most KMS tools doesn’t support this.
The NLP technique also presents an alternative – using generator models to get even more curated answers beyond just highlighting a section of text in your documents as responses. Users can get inferred responses from multiple sections of text, and answers are composed based on documents relevant to the search query or retrieved texts.
The limits of keyword-based search become more evident as the number of pages in a KMS workspace rises. When trying to find information within a large amount of digital text, these traditional search methods can be time-consuming and may not always produce relevant or accurate results.
Users may find it advantageous to use more advanced search techniques that can comprehend the intent and context of their search queries to obtain the information they need more successfully.
These tools can make it simpler for KMS users to find the information they’re seeking by accurately and effectively locating specific content throughout a vast body of text and sharing the answers in a place where the users spend most of their time.
At Mantium, we are building one of these tools. A world where it is easy to find knowledge stored in multiple KMS. It is something that we struggled with as a team. From having long Slack conversations & threads to teams finding it difficult to update knowledge in tools like Notion or even locate answers – we’ve faced it all.
To us, it is a motivation to solve our problems. We are building an integration tool that would help us solve finding relevant information, share answers where we live as a team(Slack), and enhance better collaborations across teams. Users can leverage advanced NLP techniques such as question-answering to navigate large amounts of text, and find more nuanced or complex pieces of information. This is the future of finding relevant information in knowledge management systems.
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