Synthetic Data
Definition
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.
Complementary Terms
Concepts that frequently appear alongside Synthetic Data in practice.
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.
Proprietary datasets, analytics capabilities, and data infrastructure that provide competitive advantage. Data assets include customer behavioural data, market intelligence, training datasets for AI models, and proprietary databases that improve decision-making or product quality.
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 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.
A secure, privacy-preserving technology environment that enables multiple parties to combine and analyse their datasets without either party gaining access to the other's raw data. Data clean rooms use cryptographic techniques, aggregation rules, and access controls to enable collaborative analytics while maintaining data privacy compliance.
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 structured process required under GDPR Article 35 to identify, assess, and mitigate privacy risks arising from data processing activities that are likely to result in high risk to individuals. DPIAs are mandatory before deploying new technologies, large-scale profiling, or processing sensitive personal data, and must document the necessity, proportionality, and safeguards of the proposed processing.
The documented lifecycle of data as it moves through an organisation's systems, showing its origin, transformations, dependencies, and destinations. Data lineage provides visibility into how data is created, processed, and consumed, enabling organisations to ensure data quality, comply with regulatory requirements (particularly GDPR's right to explanation), debug data pipeline issues, and assess the impact of system changes.
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