In this corporate battlefield, productivity depends not just on the availability of knowledge but also on the speed and accuracy with which it can be accessed. Employees, on average, spend around 20% of their workweek searching for information, proving that 8 hours per week per employee are lost in inefficient searches.
Traditional methods of finding information through emails, ERP systems, documents, and meeting notes create silos and delays. That brings us to the new way of finding information within the company – enterprise search. Enterprise search addresses these roadblocks by unifying information retrieval across systems to help access the right knowledge at the right time.
The question arises – How to leverage the power of enterprise search for optimizing enterprise’s knowledge management systems?
The Evolving Dynamics of Search
Client-server architectures with centrally managed structured data were the source of trust for traditional enterprise search, but with data volume explosions, the larger unstructured legacy systems struggled to scale. Irrelevant crawling, slow indexing, and high costs per indexed page turned out to be the major roadblocks in knowledge findability. Then entered open-source search engines like Apache Solr, which empowered enterprises to manage data ingestion, hardware, and UI development independently. The domination of open-source solutions built new benchmarks for modern search technology. Cognitive search, the prime example of modern technology solutions, further revolutionizes the landscape.
Defining Enterprise Search and Its Growing Need
To deliver powerful search experiences and meet rising consumer expectations, Search engines like DuckDuckGo and Google have set a benchmark for effortless content findability. The present scenario expects enterprises to deliver the same – if not better – search capabilities. And, enterprises must meet this demand by securely aggregating and indexing varied content types from multiple repositories. They look for instant, relevant results with minimal input answers prioritized at the top of the page, and all critical information in one place. That’s how enterprise search solutions shine through intelligent, context-aware search capabilities.
Moreover, the global enterprise search market is all set for remarkable growth with an expected reach of USD 13.3 billion by 2030, which is doubling its 2022 market value. Undeniably, leveraging enterprise search for the enterprise’s knowledge base becomes important to ensure contextual, relevant, and personalized results.
Also Read: Bridging the Gap: How Enterprise Search and LLMs are Revolutionizing Knowledge Management
Defining Enterprise Knowledge Management
Enterprise Knowledge Management (EKM) is the strategic process of creating, organizing, and reusing knowledge assets within an enterprise. 20% of the work time of employees is spent searching for information, EKM reduces this time and boosts productivity. With advanced technologies and methodologies, EKM ensures that knowledge is well-structured and easily accessible for the team and end-users. This approach puts an end to silos, driving tangible results like enhanced productivity, customer experiences, streamlined employee onboarding, and improved enterprise knowledge retention.
Key Pillars of Effective Enterprise Knowledge Management
Building a foundation for robust enterprise knowledge management requires three major steps:
- Content and Documentation Development: The first step includes developing and collecting existing enterprise knowledge, including tacit and explicit knowledge. The team’s responsibility is to identify new and ongoing opportunities to collect, document, and update knowledge from varied repositories and sources.
- Documentation Storage and Organization: Knowledge needs to be stored. Thus, the next step includes preparing for a storage system i.e. an effective knowledge base to make sure it is available and accessible to all.
- Knowledge Sharing and Retrieval: With centralized accessibility of knowledge content, users can retrieve the information they require, as and when they need it, based on the permission access. A centralized knowledge base aids internal and external users in finding and sharing information on time.
Leveraging Technology To Solve The ‘Findability’ Problem
The ability to locate the right information at the right time is critical for an enterprise’s success. When enterprises leverage advanced technologies, they overcome the persistent issue of ‘findability’ and ensure seamless access to knowledge.
- Knowledge Creation Tools: Solutions designed for seamless knowledge creation enable enterprises to capture valuable insights and document expertise in real-time, leading to a streamlined knowledge creation process, and eliminating the risk of knowledge gaps and loss.
- Enterprise Knowledge Platforms: These act as central repositories, consolidating information from multiple sources into a searchable and structured format for enhanced team collaboration.
- Cognitive Search: Transforms traditional search experiences by understanding the user intent and delivering context-driven and highly resonating results, thus improving findability and knowledge discovery.
- Agentic AI Applications and Chatbots: These simplify access to knowledge by providing quick conversational support. These agentic-AI-fueled applications not only streamline workflows but also ensure that customers and employees receive resonating, relevant, and accurate answers, improving search quality and satisfaction.
With these tools, you get to build a robust knowledge-sharing ecosystem essential for the sustainable growth of an enterprise.
The Course Ahead
As enterprises increasingly rely on knowledge as a strategic asset, the need for addressing the persistent ‘findability’ issue becomes critical. With advanced technologies like enterprise knowledge platforms, knowledge management solutions, AI-powered applications, and cognitive search, transformation is no longer away. The way forward is all about integrating robust enterprise search solutions into a knowledge management system to unify and optimize knowledge retrieval.