The Analytify BI Glossary
Plain-English definitions for the most common business intelligence, embedded analytics, and modern data stack terms. Each entry includes a short definition, a concrete example, related concepts, and a link to where the term shows up in real BI products.
This glossary is curated by the Analytify team and updated regularly. If you spot an error or want to suggest a new term, let us know.
Browse 50 Terms
Showing all 50 terms.
Business Intelligence (BI)
The discipline of turning operational data into structured insights, dashboards, and reports that guide decisions across every team.
Dashboard
A visual data display consolidating key metrics, charts, and KPIs onto a single screen for at-a-glance monitoring and decision-making.
KPI (Key Performance Indicator)
A quantifiable measurement used to evaluate success against a specific business objective — for example MRR, CAC, or churn rate.
KPI Dashboard
A focused dashboard showing 5-9 key metrics with current value, target, trend, and status — the executive decision tool.
BI Report
A structured document or screen that aggregates business data into tables, charts, and KPIs to support decision-making at a defined cadence.
Descriptive Analytics
Summarising historical data through dashboards and reports to answer "what happened?" — the foundation layer of every BI program.
Data Visualization
Representing information through charts, graphs, and maps so people can spot patterns, trends, and outliers faster than reading raw numbers.
Drill-Down
BI navigation that lets a user click a summary metric and progressively zoom into more granular detail along a hierarchy.
Data Warehouse
A centralised database designed to store and analyse large volumes of structured business data, optimised for analytical queries.
Data Lake
A centralised repository storing large volumes of raw data — structured, semi-structured, and unstructured — in its native format on cheap object storage.
Lakehouse
A modern architecture combining data lake economics with data warehouse query performance via open table formats like Iceberg and Delta Lake.
Data Pipeline
An automated sequence of steps that moves data from sources through transformation and into a destination warehouse or analytics tool.
ETL
Extract, Transform, Load — a data integration process where raw data is extracted from sources, transformed, and loaded into a destination.
ELT
Extract, Load, Transform — a modern integration pattern where raw data is loaded into the warehouse first and transformed inside it with SQL.
dbt (data build tool)
Open-source command-line tool that lets analysts and engineers transform data inside a cloud warehouse using version-controlled, tested SQL.
Data Integration
Combining data from multiple sources (ETL, ELT, CDC, streaming, reverse ETL) into a unified view for analytics and operations.
Reverse ETL
Syncing data from a warehouse to operational tools (CRM, marketing, support) — making the warehouse the operational source of truth.
Real-Time Analytics
Ingesting, processing, and querying data within seconds of generation — powering fraud detection, IoT, and live dashboards.
OLTP (Online Transactional Processing)
A database workload pattern of fast, small read/write operations supporting transactional applications like e-commerce, banking, and SaaS.
Data Mart
A subject-specific subset of a data warehouse focused on a single department or function (sales, marketing, finance) for faster team-level access.
Semantic layer
A centralised set of metric and dimension definitions that sits between your warehouse and BI tool, ensuring every dashboard agrees.
Star Schema
A warehouse design pattern with a central fact table containing measurable events surrounded by dimension tables holding descriptive attributes.
Snowflake Schema
A warehouse design that normalises dimension tables into multiple related sub-dimensions, reducing storage at the cost of more joins.
Fact Table
The central table in a star schema containing measurable business events with foreign keys to dimensions and numerical measures for aggregation.
Dimension Table
A table holding descriptive attributes (customer, product, date) that contextualise the events stored in fact tables.
OLAP (Online Analytical Processing)
A database workload pattern characterised by analytical queries — aggregations, filters, and slice-and-dice across large historical datasets.
Cube (OLAP Cube)
A multi-dimensional structure that pre-aggregates measures across dimensions so analytical queries return in milliseconds instead of minutes.
Columnar Database
Stores table data by column instead of by row, dramatically speeding up analytical queries that aggregate or filter on a few columns.
SQL
Structured Query Language — the standard language for querying, modifying, and managing data in relational databases.
DAX
Data Analysis Expressions — the formula language used in Power BI, Excel Power Pivot, and Analysis Services to define measures and calculated columns.
M Language
The data transformation language behind Power Query (Power BI / Excel) for scripting repeatable steps to clean, reshape, and combine data.
Predictive Analytics
Using historical data, statistics, and ML to forecast future outcomes — churn, demand, fraud, lead scoring, predictive maintenance.
Prescriptive Analytics
Goes beyond predicting what will happen to recommend or execute the best next action via optimisation, simulation, and AI agents.
Augmented Analytics
AI/ML/NLP-powered automation of analytics workflows — discovery, prep, narration, recommendation — including modern GenBI.
Time Series
Data indexed by time, plus the techniques used to spot trends, seasonality, anomalies, and forecast future values.
Cohort Analysis
Grouping users by a shared starting event (sign-up week, first purchase) and tracking their behaviour over time as a group.
LLM (Large Language Model)
A neural network with billions of parameters trained on massive text data to understand and generate human language.
RAG (Retrieval-Augmented Generation)
An AI architecture that augments LLMs with external knowledge retrieval, dramatically reducing hallucinations and enabling source citations.
AI Agent
An autonomous software system using an LLM plus tools, memory, and planning to accomplish multi-step tasks like data investigation.
GenBI
Generative Business Intelligence — using LLMs and generative AI for natural-language interactions with business data.
Embedded analytics
Integrating dashboards, charts, and reporting features directly inside a software product so users analyse their data without leaving it.
Multi-tenant SaaS
A software architecture where a single instance serves many customers, with each tenant's data and config logically isolated.
White label BI
A BI tool that can be fully rebranded with your logo, colours, domain, and product name so end users experience it as native.
Headless BI
API-first analytics architecture where a centralised semantic layer exposes governed metrics for any consumer (BI tools, AI, custom apps).
Row-level security
Database/BI feature that filters which rows a user can see based on identity or role, enforced at query time rather than after.
Data Governance
The framework of policies, ownership, processes, and tooling that ensures data is accurate, secure, well-defined, and used responsibly.
Data Quality
The degree to which a dataset is fit for use — accuracy, completeness, consistency, timeliness, uniqueness, validity.
Data Catalog
A centralised, searchable inventory of every dataset, dashboard, and metric — enriched with descriptions, ownership, lineage, and quality.
Master Data Management (MDM)
Creating and maintaining a single, trusted record for shared entities (customers, products, suppliers) across all systems.
Data Observability
Continuous monitoring of data pipeline health, freshness, quality, and lineage to detect issues before they reach dashboards.
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