Data Room (Virtual)

A secure, privacy-preserving technology environment that enables multiple parties to combine and analyse their datasets without either party gaining access to the other's raw data. Data clean rooms use cryptographic techniques, aggregation rules, and access controls to enable collaborative analytics while maintaining data privacy compliance.

Data collected directly by an organisation from its own customers, users, or audience through owned channels such as websites, apps, CRM systems, transactions, and surveys. First-party data is considered the most valuable data category because it is collected with consent, is unique to the organisation, and provides direct insight into customer behaviour and preferences.

The processes, governance, policies, and technology used to ensure that an organisation's critical shared data entities — such as customers, products, suppliers, and accounts — are accurate, consistent, and controlled across all systems and business units. MDM creates a single trusted source of master data, reducing duplication, resolving conflicts, and enabling reliable reporting and analytics.

Data that a customer intentionally and proactively shares with a business, including preferences, purchase intentions, communication choices, and personal context. Unlike first-party data (which is observed from behaviour), zero-party data is explicitly volunteered through mechanisms such as preference centres, surveys, quizzes, and account settings.

A software system that creates a unified, persistent customer database accessible to other systems by collecting and integrating customer data from multiple sources — including CRM, website analytics, email, social media, transactions, and customer service interactions. CDPs resolve customer identities across channels and devices to build comprehensive individual profiles, enabling personalised marketing, customer journey orchestration, and advanced segmentation.

An automated sequence of data processing steps that extracts, transforms, and loads data from source systems into target systems for analysis, reporting, or machine learning model training. Well-architected data pipelines are critical infrastructure assets that enable data-driven decision-making and AI deployment, and their reliability directly impacts downstream business processes.

A centralised repository that stores large volumes of raw data in its native format — structured, semi-structured, and unstructured — until it is needed for analysis. Unlike data warehouses, which store data in predefined schemas, data lakes use a schema-on-read approach that provides flexibility for diverse analytical workloads including machine learning, real-time analytics, and ad hoc exploration.

Data collected by entities that do not have a direct relationship with the individuals whose data is being gathered, typically aggregated from multiple sources and sold to other organisations for marketing, analytics, or enrichment purposes. The value and availability of third-party data have declined sharply due to privacy regulations (GDPR, CCPA), browser restrictions on third-party cookies, and growing consumer demand for data transparency.