Training Data

Proprietary datasets, analytics capabilities, and data infrastructure that provide competitive advantage. Data assets include customer behavioural data, market intelligence, training datasets for AI models, and proprietary databases that improve decision-making or product quality.

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.

Artificially generated data that mimics the statistical properties of real-world datasets, used to train machine learning models when actual data is scarce, sensitive, or expensive to obtain. Synthetic data enables AI development in privacy-constrained domains such as healthcare and finance, while reducing data acquisition costs and regulatory exposure.

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.

A quantitative measure of data fitness for its intended use, typically assessed across dimensions including accuracy, completeness, consistency, timeliness, uniqueness, and validity. Data quality scores enable organisations to monitor and improve the reliability of their data assets, prioritise remediation efforts, and establish trust in analytical outputs.

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.

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.

The process of generating measurable economic value from data assets, either directly through licensing and sale or indirectly by using data to improve products, optimise operations, and inform strategic decisions. Data monetisation strategies are central to unlocking the full enterprise value of a company's information assets.