Businesses rely on a well-rounded knowledge management system for smooth information sharing and seamless internal operations. In fact, a reliable, contextually-aware knowledge management system is the backbone and the future of a robust digital workplace.
Knowledge management refers to capturing, storing, and disseminating information that lies within files, documents, reports, databases, and guides. However, over 80% of this data is unstructured, incapable of being comprehended by computers. Unstructured databases possess valuable information, but machines cannot recognize the context in which the information is helpful. For example, an employee may want to know the amount of 401(k) contribution the company makes every year. While the question itself may have multiple answers based on the employee’s tenure, the employee may receive a one-size-fits-all solution that does not solve his/her query.
However, a structured, artificial intelligence-ready (AI-ready) knowledge management system is powerful and contextually aware and provides employees with relevant and personalized information when they request for it. For example, suppose an employee wants to change his tax deductions. In that case, the system will provide instructions on how to go about the process and surface the required forms and calculations related to the new paycheck. Additionally, AI can recognize out-of-date information and request the authors to update or establish its freshness.
So, let’s have a look at the five critical features to look out for in a contextual knowledge management system.
While the unending pile of knowledge within an organization holds enormous value, what is most important is to receive the right information at the right time, based on an employee’s location, role, interests, team, etc. In a rapidly evolving business landscape, it is essential for knowledge to flow dynamically across the enterprise and take full advantage of the information to stay competitive.
Modern intranets use powerful AI-driven mechanisms to ensure the most contextually-relevant information reaches the employees.
A contextually-aware knowledge management system holds the ability to dispense consistent and uniform answers across all communication channels, whether it’s the intranet homepage, the mobile intranet app, or a chatbot.
As a contextual knowledge management system allows separation of text from the delivery channel, organizations may reduce maintenance efforts by retaining a single knowledge source for all outlets and formatting content differently across different channels.
In most cases, understanding customer context forms the basis of getting useful information without searching at all. For example, a customer calls the contact center immediately after a significant amount was withdrawn/spent from his account. In such a situation, the contextual knowledge base can assume it was a fraudulent transaction and provide the necessary knowledge to the agent to help him guide the customer regarding the next steps.
Thus, contextual knowledge management improves content decisions by leveraging machine learning to understand what content will best address the situation. Moreover, by proactively sending relevant knowledge articles, organizations can improve resolution rates and enhance the customer experience.
As seen above, more than 80% of the enterprise data is unstructured. While it is easy to analyze structured data, extracting valuable insights from unstructured content, including emails, memos, text documents, videos, etc., can be tedious.
However, a contextual knowledge management system uses AI-driven knowledge mining to explore the data, unearth insights, and discover relationships at scale.
Knowledge mining is a three-step process that includes:
As discussed previously, unstructured data is generated every day in the form of images, audio, PDFs, etc., and is hard to explore, organize, and understand. An average knowledge worker spends over 30% of his work week finding the information he needs, equalling millions of dollars of lost revenue due to decreased productivity. If unstructured data isn’t tagged correctly, it won’t show up in search results.
To overcome these obstacles, organizations can leverage AI and machine learning to automatically classify and tag the data and build a comprehensive, cognitive enterprise search engine.
Considering the vast amounts of incoming data and increasing data silos, traditional keyword search usage won’t suffice anymore and needs to be replaced with a contextual, intelligent, and valuable search functionality that delivers personalized and more relevant search results.
Knowledge is power, and contextual knowledge management is the future of the digital workplace.
As a Microsoft Gold Partner, Mesh 3.0, Acuvate’s Employee Experience Platform provides a modern knowledge system using Microsoft Azure Cognitive Search combined with Microsoft Cognitive Services for AI-powered knowledge mining and delivering advanced, contextual search experiences. Mesh’s digital workplace tools manage knowledge effectively and reduce the time to find information.
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