Data Pipeline

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 framework of policies, standards, and processes that ensures data assets are managed consistently, securely, and in compliance with regulations throughout their lifecycle. Strong data governance increases the reliability and value of data as an intangible asset, directly supporting analytics, AI applications, and data monetisation strategies.

The portfolio of drug candidates at various stages of research, development, and regulatory approval within a pharmaceutical or biotechnology company. The pharma pipeline is a critical intangible asset, with each compound's value dependent on its probability of regulatory approval, expected market size, patent protection remaining, and development costs to completion.

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.

The process of further training a pre-trained machine learning model on a smaller, domain-specific dataset to adapt it for a particular task or industry. Fine-tuning allows organisations to leverage foundational models while creating proprietary, specialised AI capabilities that constitute identifiable intangible assets.

A set of practices combining machine learning, DevOps, and data engineering to standardise and streamline the end-to-end lifecycle of machine learning models, from development through deployment to monitoring. MLOps encompasses version control for models and data, automated testing, continuous integration and deployment, and model performance monitoring in production.

A technology service that authorises and processes electronic payment transactions between merchants and acquiring banks or payment processors. Payment gateways encrypt sensitive payment data, route transactions to the appropriate card networks, and return authorisation responses in real time.

The process of breaking text, code, or other sequential data into discrete units (tokens) that serve as the input and output elements for large language models. Tokenisation determines how a model processes language and directly affects inference costs, since API pricing for large language models is typically based on token count.

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 create and manage a portfolio of companies in Opagio?

Opagio's portfolio feature lets investors and fund managers add multiple companies, track their intangible asset values over time, compare benchmarks, and generate consolidated portfolio-level reports.

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).

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.