Data Mesh

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.

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 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 distributed computing paradigm that processes data near the source of generation rather than in a centralised data centre, reducing latency, bandwidth costs, and data privacy risks. Edge computing is essential for real-time AI applications such as autonomous vehicles, industrial IoT, and point-of-sale analytics.

A centralised platform for storing, managing, and serving the engineered features (input variables) used by machine learning models in both training and real-time inference. Feature stores ensure consistency between training and production environments, enable feature reuse across multiple ML models, reduce duplication of feature engineering effort, and provide a governance layer for tracking feature lineage and ownership.

An approach to systems engineering and product development that embeds data protection principles into the design and architecture of IT systems and business practices from the outset, rather than retrofitting them. Privacy by Design is codified as a legal requirement under GDPR Article 25 and encompasses data minimisation, pseudonymisation, and purpose limitation as default settings.

A cybersecurity framework based on the principle that no user, device, or system should be automatically trusted, whether inside or outside the network perimeter. Zero trust requires continuous verification of identity and access rights for every request, micro-segmentation of network resources, and least-privilege access controls.

Data as a Balance Sheet Asset: Valuation Methods for the AI Era

Data is now formally recognised as productive capital by the System of National Accounts. But how do you value it on a balance sheet? Tony Hillier draws on 30 years of structured finance experience to map three valuation approaches — and where data assets fit into modern capital structures.