Data and AI, in plain English.
No jargon. Just clear definitions of the terms you'll hear in every data conversation.
Search the glossary.
AI readiness
How prepared your data, systems and people are to use AI safely and effectively.
Dashboard
A visual display of key metrics that updates automatically.
Data curation
Cleaning, organising and modelling raw data so reports and AI can rely on it.
Data governance
The rules, roles and processes that decide who owns data, how it's used and how it's protected.
Data ingestion
Bringing data in from source systems, such as your CRM, ERP or spreadsheets, into one platform.
Data lineage
A record of where data comes from and every step it passes through before reaching a report.
Data maturity
How well an organisation collects, manages and uses data to make decisions.
Data pipeline
An automated process that moves data from source systems into a central platform.
Data quality
How accurate, complete, consistent and up to date your data is.
Data validation
Testing data against rules and its original source to confirm it's correct.
Data warehouse
A central store that brings data from many systems together for reporting and analysis.
EU AI Act
EU regulation setting rules for developing and using AI, based on the level of risk.
Forecasting
Using past data and statistical or AI models to predict future values, such as demand or cash.
GDPR
The EU and UK law governing how personal data is collected, stored and used.
Governed AI
AI that works only on approved data, follows your access rules and shows where every answer came from.
Lakehouse
A data platform combining the flexibility of a data lake with the structure of a warehouse.
Large language model (LLM)
An AI model trained on large amounts of text that can understand and generate language.
Metadata
Data about data, such as its source, owner, definition and last update.
Predictive analytics
Analysis that estimates what is likely to happen next.
Retrieval-augmented generation (RAG)
An AI technique that answers questions using your own documents and data as its source.
Semantic layer
A shared set of business definitions that sits between your data and your reports or AI tools.
Single source of truth
One trusted, agreed version of each key number, used by everyone.
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