CB Resourcing Glossary of Terms
A reference guide to terminology across the disciplines CB Resourcing recruits into: Knowledge Management, Legal Technology, Research, Information Management, Librarianship, Industry Analysis, Market Research, Market Data, AI Governance, Data Governance, and AI Adoption. Written with candidates and clients in mind — useful for understanding job descriptions, role titles, and the skills behind them.
Knowledge Management (KM)
KM is a core discipline across law, consulting, financial services, engineering, technology, healthcare, and the public sector — fundamentally, any intellectual property or expertise-intensive industry where reusing hard-won knowledge matters as much as generating it.
Knowledge Management (KM) — The discipline of capturing, organising, sharing, and reusing an organisation's collective expertise and content so people aren't reinventing work already done elsewhere in the business.
Knowledge Asset — Any piece of reusable content or expertise an organisation manages — templates, playbooks, research, methodologies, case studies, or expert insight.
Know-how — Institutional expertise and reusable working material — methodologies, templates, checklists, playbooks, precedent, clauses, or design standards — developed through prior projects and distinct from external published research or literature.
Precedent Bank / Precedent Library — A curated collection of template documents, clauses, and forms that practitioners adapt for new matters.
Head of Knowledge Management / Chief Knowledge Officer (CKO) — Senior role responsible for an organisation's KM strategy, systems, and content, often sitting across the business, IT, and central services.
Practice Group Knowledge Lawyer (PSL) — Also called Professional Support Lawyer; a qualified lawyer who maintains legal know-how, monitors legal developments, and supports fee-earners with technical and drafting expertise rather than running client matters directly.
Knowledge Manager / Knowledge Engineer — A KM specialist who builds, curates, or structures knowledge assets for reuse — via document automation and expert systems, wikis and portals, or AI tools.
Taxonomy — A structured, hierarchical classification scheme (e.g. by topic, sector, document type, jurisdiction) used to organise content so it can be found and filtered consistently.
Ontology — A more expressive knowledge model than a taxonomy, defining not just categories but the relationships between concepts (used to power search, tagging, and AI applications).
Community of Practice — A group of practitioners who share expertise and collaborate informally around a common discipline or specialism.
Lessons Learned / Post-Project Review — A structured process of capturing what worked, what didn't, and what should be reused after a project, matter, or engagement concludes.
Tacit vs Explicit Knowledge — Tacit knowledge is know-how held in people's heads (experience, judgement); explicit knowledge is documented and codified (guides, manuals, databases). KM aims to convert more of the former into the latter.
Subject Matter Expert (SME) Network — A directory or structured network identifying who within an organisation holds deep expertise in a given topic, enabling colleagues to find the right person to ask, rather than only the right document to read.
Knowledge Sharing Culture — The organisational norms, incentives, and behaviours that determine whether people actually contribute to and reuse shared knowledge, often considered as important to KM success as the systems themselves.
Legal Technology (Legaltech)
Legaltech — Software and technology tools built specifically to support legal work: contract review, document automation, e-discovery, matter management, legal research platforms, and more.
Document Automation / Document Assembly — Technology that generates documents from templates and rules, reducing manual drafting (e.g. HotDocs, Contract Express, Documate).
Contract Lifecycle Management (CLM) — Software that manages contracts from drafting and negotiation through execution, storage, and renewal (e.g. Ironclad, DocuSign CLM, Icertis).
E-Discovery (Electronic Discovery) — The process of identifying, collecting, and reviewing electronically stored information for litigation or investigations, often using specialised review platforms (e.g. Relativity, Everlaw).
Matter Management — Systems used by legal teams (in-house or firm) to track matters, budgets, timelines, and outside counsel activity.
Legal Project Management (LPM) — Applying project management discipline (scoping, budgeting, timelines) to legal matters to improve efficiency and predictability.
Alternative Legal Service Provider (ALSP) — A non-traditional provider (not a law firm) delivering legal or legal-adjacent services, often technology-enabled, such as document review, contract management, or managed legal services.
Innovation / Legal Innovation — Functions and roles dedicated to identifying and implementing new tools, processes, or delivery models to improve how legal services are delivered.
Low-code/No-code — Platforms that let non-developers build tools or workflows (common in legal ops for building intake forms, workflows, or simple apps) without traditional programming.
API Integration — Connecting different software systems (e.g. a CLM with a signature tool) so data flows between them automatically.
Legal Ops (Legal Operations) — The function within in-house legal departments focused on process, technology, vendor management, budgeting, and metrics to run the legal department efficiently.
Collaboration Platforms — Extranet and client/deal-room technology that lets firms and clients share documents, workflows, and updates securely outside the firm's internal network (e.g. HighQ, iManage Cloud, SharePoint extranets, Litera Foundation). Common uses include deal rooms, client reporting portals, and secure matter collaboration spaces, and the category increasingly overlaps with KM and client-facing innovation functions.
Research (Legal, Business & Competitive Research)
Research Analyst / Research Lawyer — A role dedicated to conducting legal, business, or market research to support fee-earners, clients, or internal decision-making.
Primary Research — Original research conducted directly (surveys, interviews, case analysis) rather than relying on existing published sources.
Secondary Research — Research based on existing published material — reports, databases, news, filings — rather than newly generated data.
Current Awareness — A service alerting practitioners to relevant new law, cases, regulatory changes, or news affecting their practice area or clients.
Business Development (BD) Research / Pitch Research — Research conducted to support new business pitches — e.g. profiling a prospective client, its market, competitors, and legal needs.
Competitive Intelligence (CI) — The practice of gathering and analysing information about competitors' strategies, strengths, weaknesses, and market position.
Due Diligence Research — Investigative research into a company, individual, or transaction to identify risks, background, or relevant facts (often for M&A, compliance, or client onboarding).
Boolean Search — A search technique using operators (AND, OR, NOT) to combine or exclude terms, foundational to legal and database research.
Federated Search — A single search interface that queries multiple databases or sources simultaneously and aggregates the results.
Research Guide / LibGuide — A curated online resource guide (often built on platforms like Springshare LibGuides) pointing users to relevant databases, tools, and materials for a topic or practice area.
Taxi Cab Report (Taxi Rank Briefing) — A short, highly distilled client or company overview designed to be read in the time it takes to travel to a meeting — typically a page or less, covering the essentials a fee-earner or partner needs before walking in the door (who the client is, recent news, key people, relevant matters, and talking points). Prized in BD and client-facing research for prioritising clarity and brevity over comprehensiveness.
Deal Sourcing — The research-driven process of identifying potential investment targets, acquisition opportunities, or add-on companies, most associated with private equity and corporate development. Involves screening companies against investment criteria (sector, size, growth, financials) using databases (e.g. PitchBook, Preqin, Capital IQ), building target lists, and monitoring market signals for opportunities before they come to market — often supported by dedicated deal sourcing or origination research analysts.
Information Management
Information Management (IM) — The broader discipline of governing how information is created, stored, classified, retained, and disposed of across an organisation (encompassing but broader than KM).
Information Governance (IG) — Policies and controls that determine how information is managed for compliance, risk, and value — covering retention schedules, access controls, and lifecycle management.
Records Management — The systematic control of records (physical or digital) from creation through retention and eventual disposal, often governed by regulatory or statutory requirements.
Metadata — Structured data that describes other data (author, date, document type, matter number) enabling search, filtering, and governance.
Document Management System (DMS) — Software used to store, organise, version, and retrieve documents (e.g. iManage, NetDocuments, SharePoint).
Enterprise Search — Search technology that indexes and retrieves content across multiple internal systems (DMS, email, intranet) from a single query.
Retention Schedule — A policy defining how long different categories of records must be kept before they can be securely destroyed or archived.
Information Architecture (IA) — The structural design of information environments (intranets, DMS folder structures, websites) to support findability and usability.
Chief Information Officer (CIO) — Senior executive responsible for an organisation's information technology strategy and systems (distinct from a CKO, who focuses on knowledge content and strategy).
Data Lifecycle Management — Managing data from creation through active use, archival, and deletion, aligned with governance and compliance requirements.
Library & Information Services (including Law Librarianship)
Law librarianship is an important and well-established specialism for CB Resourcing, but it sits within a much wider library and information services discipline — spanning special, corporate, academic, government, health, and embedded librarianship. Many of the skills and systems below apply across all of these settings, with law-specific detail called out where relevant.
Law Librarian — An information professional specialising in legal research resources and legal information services, typically working in law firms, courts, government, or academic law libraries.
Special Librarian — An information professional working within a specific organisation or industry (a corporate library, law firm, hospital, museum, or research institute) rather than a public or general academic library, curating and delivering information tailored to that organisation's needs. Law librarians are one example of a special librarian.
Corporate Librarian / Information Specialist — An information professional embedded within a business (financial services, consulting, pharma, media, and beyond) responsible for research support, database access, and information services for internal staff.
Academic Librarian — A librarian working within a university or educational institution, supporting research, teaching, and learning through collection development, information literacy, and reference services.
Embedded Librarian — An information professional working directly within a team or department (rather than a central library function) to provide research and information support tailored to that group's day-to-day needs.
Collection Development — The process of selecting, acquiring, and maintaining a library's resources (print and digital) in line with user needs and budget, whether that collection is legal, scientific, medical, business, or general in nature.
Legal Database — Subscription research platforms such as Westlaw, LexisNexis, Bloomberg Law, or vLex, providing access to case law, legislation, and secondary sources — one category of specialist database alongside scientific, medical, financial, and business information platforms used in other library settings.
Reference Services — Direct support provided by librarians to help users locate, evaluate, and interpret information or resources, regardless of sector.
Cataloguing / Classification — Organising library materials using standardised schemes (e.g. Library of Congress Classification, Dewey Decimal) so they can be consistently located across any type of collection.
Interlibrary Loan (ILL) — A service allowing libraries to borrow materials from one another on behalf of their users.
Vendor Management (Library context) — Negotiating and managing relationships and contracts with database and information providers, whether legal, scientific, financial, or general research platforms.
Serials Management — Tracking and managing subscriptions to journals, reports, and other periodicals across any subject area.
Information Literacy Training — Teaching users how to effectively search, evaluate, and use information sources — a core library skill applied differently across legal, academic, corporate, and healthcare settings.
Taxonomy & Metadata Work (Library context) — Applying structured classification and description to a collection so materials can be discovered and filtered, a skill shared with broader information and knowledge management roles.
Professional Bodies — Membership organisations supporting library and information professionals across specialisms, including AALL (American Association of Law Libraries) and BIALL (British and Irish Association of Law Librarians) for law librarians specifically, alongside broader bodies such as CILIP (Chartered Institute of Library and Information Professionals) and SLA (Special Libraries Association) covering the wider profession.
Industry Analysis
Industry Analyst — A professional who studies a specific sector (e.g. legal services, fintech, pharma) to produce insights on trends, competitors, and market dynamics.
PESTLE Analysis — A framework analysing Political, Economic, Social, Technological, Legal, and Environmental factors affecting an industry.
Porter's Five Forces — A framework for analysing industry competitiveness: competitive rivalry, supplier power, buyer power, threat of substitution, and threat of new entry.
Market Sizing — Estimating the total revenue or volume opportunity within a defined market or sector.
Benchmarking — Comparing an organisation's performance, pricing, or practices against industry peers or standards.
Thought Leadership — Published content (reports, articles, commentary) intended to establish an organisation or individual as an authority on industry trends and issues.
Trend Report — A publication analysing emerging patterns and shifts within an industry over time.
SWOT Analysis — A framework assessing Strengths, Weaknesses, Opportunities, and Threats for an organisation or market.
Sector Coverage — The specific industries or verticals an analyst, research team, or dataset focuses on.
Market Research
Market Research — The systematic gathering and analysis of data about markets, customers, and competitors to inform business decisions.
Qualitative Research — Research exploring opinions, motivations, and experiences through methods like interviews and focus groups, producing descriptive (non-numeric) insight.
Quantitative Research — Research producing numeric, statistically analysable data, typically through surveys or structured data collection.
Survey Methodology — The design and execution of structured questionnaires to collect data from a defined sample population.
Sample Size / Sampling — The number and method of selecting respondents in a study, affecting the statistical reliability of results.
Focus Group — A moderated discussion with a small group of participants used to explore attitudes and reactions to a topic, product, or service.
Client Feedback / Voice of Client (VoC) Programme — Structured processes for gathering and analysing client sentiment, often used by professional services firms to inform strategy.
Net Promoter Score (NPS) — A metric measuring customer/client loyalty based on likelihood to recommend an organisation, scored on a 0–10 scale.
Segmentation — Dividing a market or client base into distinct groups based on shared characteristics (industry, size, need) to target research or strategy more precisely.
Advisory Research — Research and analyst services (e.g. Gartner, Forrester, IDC) that go beyond describing market size and growth to provide prescriptive guidance — best-practice frameworks, vendor comparisons (such as Magic Quadrants), and recommendations on strategy, technology choices, or operating models. Distinct from pure market sizing/trend research in that its purpose is to advise decision-makers on what to do, not just describe what is happening in the market.
Market Data
Market Data — Structured, often subscription-based datasets covering pricing, deal activity, company financials, or industry metrics used for analysis and decision-making.
Deal Data / League Tables — Datasets and rankings tracking M&A, capital markets, or litigation activity, often used for business development and competitive benchmarking (e.g. Mergermarket, Bloomberg, Refinitiv).
Data Provider / Data Vendor — A commercial supplier of structured datasets (e.g. PitchBook, S&P Capital IQ, Preqin) licensed to organisations for research and analysis.
API Feed — A data delivery method allowing systems to pull market data programmatically and in near real time, rather than manually via a user interface.
Data Licensing — Commercial agreements governing how a purchased dataset may be used, shared, or redistributed within an organisation.
Data Normalisation — The process of standardising data from multiple sources into a consistent format for accurate comparison and analysis.
Time-Series Data — Data recorded at successive points in time (e.g. daily stock prices), used to analyse trends and changes.
Data Feed Integration — Connecting an external market data source directly into internal systems (dashboards, CRMs, research platforms) for automatic updates.
AI Governance
AI Governance — The frameworks, policies, and oversight structures an organisation puts in place to ensure AI systems are used safely, ethically, legally, and in line with organisational values.
Responsible AI — An umbrella term for principles and practices aimed at ensuring AI systems are fair, transparent, safe, and accountable.
AI Risk Assessment — A structured evaluation of the risks (legal, ethical, reputational, operational) posed by a specific AI use case before or during deployment.
Model Risk Management — Practices (often adapted from financial services) for validating, monitoring, and controlling risks associated with AI/ML models.
Explainability / Interpretability — The degree to which an AI system's decisions or outputs can be understood and explained by humans.
Human-in-the-Loop (HITL) — A design principle requiring human review or approval at key points in an AI-driven process, rather than full automation.
EU AI Act — Landmark European Union legislation establishing a risk-based regulatory framework for AI systems, with obligations scaled to the risk level of the application.
AI Ethics Board / AI Governance Committee — An internal body responsible for reviewing and approving AI use cases, policies, and risk decisions within an organisation.
Bias and Fairness Testing — Evaluating AI systems for discriminatory or skewed outcomes across different groups.
Shadow AI — Unsanctioned use of AI tools by employees outside of approved, governed channels, posing risk to data security and compliance.
AI Use Case Register — An internal inventory tracking where and how AI is used across an organisation, often required for governance and audit purposes.
MCP (Model Context Protocol) — An open standard that lets AI models connect to external tools, data sources, and systems (e.g. document management, CRM, research databases) in a consistent, secure way. Increasingly relevant to AI governance because it defines how and where an AI assistant is permitted to read from or act on organisational systems, raising questions of access control, auditability, and data exposure that governance frameworks need to address.
Data Governance
Data Governance — The overall management of data availability, usability, integrity, and security within an organisation, including who is accountable for what data.
Data Steward — An individual accountable for the quality, definition, and appropriate use of specific data sets within a business area.
Data Owner — The person or function formally accountable for a dataset's accuracy, access rights, and lifecycle.
Data Quality — The measure of data's accuracy, completeness, consistency, and reliability for its intended use.
Master Data Management (MDM) — Processes ensuring a single, consistent, authoritative version of key business data (e.g. client records) across systems.
Data Lineage — The documented trail showing where data originated, how it has moved, and how it has been transformed across systems.
Data Classification — Categorising data (e.g. public, internal, confidential, restricted) to apply appropriate handling and security controls.
GDPR (General Data Protection Regulation) — EU legislation governing the collection, processing, and protection of personal data, with significant implications for data governance practice.
Data Privacy Officer / DPO — A designated individual responsible for overseeing an organisation's data protection strategy and compliance with privacy law.
Data Catalogue — A searchable inventory of an organisation's data assets, including metadata, ownership, and lineage, used to improve findability and governance.
AI Adoption
AI Adoption — The process by which an organisation integrates AI tools and capabilities into its working practices, ranging from pilot use to full operational embedding.
Generative AI (GenAI) — AI models (e.g. large language models) capable of producing new content — text, images, code — in response to prompts, as distinct from AI used purely for prediction or classification.
Large Language Model (LLM) — A type of AI model trained on large volumes of text data to understand and generate human-like language (e.g. GPT, Claude, Gemini).
Prompt Engineering — The practice of crafting inputs to AI models to reliably produce accurate, relevant, and useful outputs.
Pilot Programme — A limited, controlled trial of a new AI tool or capability before wider rollout, used to test value and risk.
Change Management — The structured approach to helping people and organisations adopt new tools, processes, or ways of working — critical to successful AI adoption.
AI Champion / AI Super-user — An individual within a team or department who advocates for, tests, and helps colleagues adopt new AI tools.
Augmentation vs Automation — Augmentation uses AI to support and enhance human work (e.g. drafting assistance); automation replaces a task or process entirely with minimal human input.
ROI (Return on Investment) of AI — The measurable value (time saved, cost reduced, quality improved) generated by an AI tool relative to its cost of adoption.
Digital Transformation — The broader organisational shift toward digital tools and ways of working, of which AI adoption is often a significant component.
Upskilling / Reskilling — Training initiatives to build staff capability to use new AI tools and technologies effectively as part of adoption efforts.
This glossary is intended as a general reference for candidates and clients engaging with CB Resourcing across knowledge management, legal technology, research, information management, librarianship, industry analysis, market research and data, and AI/data governance roles. Terminology and definitions may evolve as these fields develop.