Data Quality Score

A quantitative rating assessing a company's performance and risk exposure across environmental, social, and governance criteria, typically assigned by specialist rating agencies such as MSCI, Sustainalytics, and S&P Global. ESG scores increasingly influence investment decisions, cost of capital, and regulatory compliance, and are becoming a material factor in business valuations and due diligence.

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.

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 detailed financial analysis, typically prepared by an accounting firm on behalf of a buyer or lender, that assesses the sustainability, accuracy, and adjustability of a target company's reported earnings. A QoE report examines revenue recognition policies, non-recurring items, related-party transactions, working capital normalisation, pro forma adjustments, and the bridge from reported EBITDA to adjusted EBITDA.

A customer loyalty metric derived from a single survey question asking respondents how likely they are to recommend a company, product, or service on a scale of zero to ten. NPS is widely used as a proxy for customer relationship quality and brand strength, both of which are critical intangible assets influencing long-term enterprise value.

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.

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.

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.