Knowledge Management

Transform data into organisational intelligence with knowledge management


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What is knowledge management?

Knowledge management (KM) is the systematic discipline of capturing, organising, sharing, and applying knowledge within an organisation. It is both a strategic framework and a practical set of processes and tools designed to ensure that information and expertise are not only preserved but also actively leveraged to create value. 

At its core, KM distinguishes between explicit knowledge (documents, manuals, data) and tacit knowledge (skills, experience, intuition). A strong KM strategy combines technology, processes, and culture to make knowledge accessible to those who need it whenever they need it.

Why is knowledge management important?

Knowledge is one of an organisation’s most valuable assets. Without effective KM, insights remain siloed, duplication increases, and institutional memory fades when employees leave. Key benefits of KM include: 

  • Enhanced decision-making: Leaders base strategies on evidence, not guesswork. 
  • Operational efficiency: Teams avoid “reinventing the wheel” by reusing proven resources. 
  • Innovation and collaboration: Sharing ideas fosters creative solutions. 
  • Risk reduction: Centralised knowledge supports compliance and governance. 
  • Resilience: Institutional knowledge is preserved even amid staff turnover. 

In short, KM transforms scattered data into organisational intelligence that drives performance.

How does knowledge management work?

Knowledge management typically follows a lifecycle model, which can be broken down into four key stages: 

  1. Knowledge Capture 
    1. Identifying and collecting tacit and explicit knowledge 
    2. Examples: interviews, process documentation, data collection. 
  2. Knowledge Storage 
    1. Organising knowledge in structured repositories, databases, and document management systems. 
    2. Examples: internal wikis, digital libraries, secure archives. 
  3. Knowledge Sharing 
    1. Facilitating access across teams and departments. 
    2. Examples: collaboration platforms, intranets, training sessions. 
  4. Knowledge Application 
    1. Embedding knowledge into workflows and decision-making. 
    2. Examples: analytics dashboards, policy updates, AI-powered insights. 

This cycle is continuous. Knowledge is constantly refreshed, recontextualised, and reapplied as organisations evolve.

Types of knowledge management

Knowledge comes in different forms, and KM strategies must address each effectively: 

Type of Knowledge 

Description 

Examples 

Explicit 

Clearly articulated, easy to codify 

Manuals, reports, research databases 

Tacit 

Personal, experience-based, harder to formalise 

Skills, intuition, professional expertise 

Embedded 

Built into processes, routines, and systems 

Standard operating procedures, software workflows 

Successful KM programs recognise that tacit knowledge is often the hardest to capture but the most valuable when shared.

Examples of knowledge management in practice

Knowledge management is relevant across industries: 

  1. Consulting agencies: Internal best practice repositories help global teams align strategies. 
  2. Investment firms: Clinical knowledge bases store treatment guidelines, improving patient outcomes. 

In each case, KM ensures that critical knowledge flows seamlessly across teams, enabling smarter, faster, and more accountable decision-making.
 

Knowledge management summary

Term 

Knowledge Management 

Definition 

Discipline and processes for capturing, organising, sharing, and applying organisational knowledge 

Used By 

Corporations, investment firms

Key Benefit 

Better decision-making, reduced risk, stronger collaboration 

Example Tool 

Nexis+ AI 

 

How LexisNexis can help with knowledge management

Nexis+ AI is uniquely positioned to enhance knowledge management strategies by combining AI-powered discovery with trusted LexisNexis content. With Nexis+ AI, organisations can: 

  • Uncover insights quickly using natural language queries and semantic search. 
  • Contextualise knowledge by linking relevant legal, news, and business intelligence. 
  • Reduce research time by surfacing the most relevant, authoritative results first. 
  • Support compliance and governance with reliable, curated sources. 

By embedding Nexis+ AI into a KM framework, organisations transform raw data into actionable intelligence, driving stronger outcomes across legal, corporate and academic contexts. 

 

Talk to a consultant

Frequently asked questions

Information management deals with collecting and storing data. Knowledge management goes further by contextualising that information, connecting it to expertise, and applying it to decision-making.

Common barriers include cultural resistance to knowledge sharing, outdated technology systems, and difficulty in capturing tacit knowledge.

Modern due diligence increasingly relies on AI, automation, and access to global data sources to accelerate investigations and improve accuracy.

While knowledge management (KM) platforms, content management systems (CMS), and document management systems (DMS) all help organisations organize and use information, their focus and functionality differ:

  • Document Management Systems (DMS): Primarily designed to store, secure, and track documents. They ensure version control, compliance, and easy retrieval of files like contracts, reports, or policies.
  • Content Management Systems (CMS): Focused on creating, publishing, and managing digital content, often for external audiences. A CMS powers websites, blogs, and intranets, making it easier to update and deliver content consistently.
  • Knowledge Management Platforms (KM): Go beyond storing or publishing content. KM platforms are built to capture, organise, and connect both explicit knowledge (documents, articles) and tacit knowledge (employee expertise, best practices, discussions). They emphasise searchability, collaboration, and knowledge-sharing across teams, helping organisations turn raw information into actionable insights.

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