Data and AI, in plain English
No jargon. Just clear definitions of the terms you'll hear in every data conversation.
- 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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