01 Mar
Why Contextual Knowledge Management Is The Future Of Digital Workplace
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 Additionally, AI can recognize out-of-date information and request the authors to update or establish its freshness.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.
5 Features of a Contextual Knowledge Management System
1. Personalization of Content
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.
- AI facilitates automated tagging and classification of data, breeding ground for efficient search mechanisms, and providing the unique ability to deliver knowledge when it is needed from internal sources, including documents, files, repositories, and apps.
- The right news, corporate messages, and peer announcements can be delivered to employees based on their role, personas, and journey.
- Smart recommendations, including useful documents recommended based on the user profile, targeted notifications, and peer and team updates are provided, saving employees’ time and energy spent in searching and collating information.
2. Omnichannel Knowledge
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.
3. Suggested Knowledge
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.
4. Knowledge mining
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:
- Ingest – Whether structured or unstructured, knowledge exists in various legacy and modern enterprise systems and is brought together using pre-built connectors and first and third-party integrations.
- Enrich – AI technologies, such as natural language processing and machine learning, enrich the data captured in Step 1 by automating tasks, including metadata generation, classification, tagging, and taxonomy usage. Data enrichment improves data discoverability via search.
- Explore – Cognitive enterprise engines connect all your internal and external apps together and act as a unified, organization-wide search engine.
5. Intelligent Search
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.
Getting Started
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.
Poonam Chug is AVP - SaaS Strategy & Business. She has worked in various areas, right from designing and executing sales & account management strategies to reengineering digital workplace solutions. With her determined focus on our mission and progressive approach, she has achieved customer delight in the space of AI, Knowledge Mining, Content & Collaboration, Virtual Assistants, RPA and more. Backed with a deep understanding of customer needs and technology, she leads the SaaS business unit with an upshot of maximizing revenue while ensuring customer satisfaction.
Poonam Chug