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.

A detailed financial analysis, typically prepared by an accounting firm on behalf of a buyer or lender, or for the seller before a sale (a sell-side QoE), 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 normalised 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.

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 strategic management framework developed by Robert Kaplan and David Norton that translates an organisation's vision and strategy into a coherent set of performance measures across four perspectives: financial, customer, internal business processes, and learning and growth. The balanced scorecard is particularly relevant to intangible asset management because three of its four perspectives — customer, process, and learning — directly measure intangible value drivers.

A data integration process that extracts data from source systems, transforms it into a consistent format suitable for analysis, and loads it into a target data store such as a data warehouse or analytics platform. ETL pipelines are critical intangible infrastructure for data-driven organisations, enabling the aggregation, cleansing, and structuring of data assets that inform business decisions.

The practice of designing and optimising input instructions (prompts) to elicit desired outputs from large language models and other generative AI systems. Effective prompt engineering can significantly improve AI output quality and consistency, and documented prompt libraries are emerging as a form of organisational knowledge capital.

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.