Customer Data Platform (CDP)

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.

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 decentralised data architecture paradigm that treats data as a product owned by domain-specific teams rather than centralising all data management in a single platform team. Data mesh is built on four principles: domain ownership, data as a product, self-serve data infrastructure, and federated computational governance.

A secure online repository used in M&A transactions, capital raises, and other due diligence processes to store and share confidential documents with authorised parties. Virtual data rooms provide granular access controls, activity tracking, watermarking, and Q&A workflows.

A centralised repository of structured, processed data optimised for analytical querying and business intelligence reporting. Data warehouses use a schema-on-write approach, meaning data is cleaned, transformed, and organised into predefined structures before loading.

The dataset used to train a machine learning model, comprising examples from which the model learns patterns, relationships, and decision boundaries. High-quality, proprietary training data is a significant competitive advantage and intangible asset, particularly in regulated industries where data scarcity creates barriers to entry.

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.

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.

How do you assess whether a company is ready to implement AI?

AI readiness is assessed across five dimensions: data maturity, technical infrastructure, talent and skills, organisational culture, and strategic clarity on use cases with measurable business outcomes.

What are the key intangible assets in e-commerce businesses?

E-commerce intangible assets include customer relationships and databases, brand and domain names, proprietary technology platforms, supplier relationships, and user-generated content and reviews.

How do data assets compound in value over time?

Data assets gain value through accumulation (more data improves accuracy), combination (linking datasets creates new insights), and application (training AI models on historical data generates competitive advantages that widen over time).