Glossary

Explore the language of modern business intelligence and data-driven work. With plain-language explanations, practical context, and real-world examples, this glossary makes complex ideas clearer. From decision-making frameworks to the data stack behind AI and analytics, each entry is designed to spark smarter conversations.

Ontology

Ontology, in the context of data and metrics, is the shared vocabulary that defines your business entities, metrics, relationships, and rules. It gives every term a single, trusted meaning across dashboards, queries, and AI powered analytics.

Metadata

Metadata means data about data. It describes a file, table, or metric so you can find it, understand it, and use it correctly. A photo’s metadata can include date, location, and camera model. A book’s metadata lists the title, author, and publisher.

Semantic Layer

A semantic layer is the shared business vocabulary and rules that translate raw tables into consistent, human‑readable metrics and dimensions. It turns questions like “What do we mean by revenue?” into reusable definitions every chart and query uses.

Data Integrity

Data integrity means your data stays accurate, consistent, complete, valid, and timely from the moment it is created to the moment you use it. Think of it like a shared recipe that everyone follows, so the result tastes the same every time.

Data Quality

Data quality measures the reliability of your data. High‑quality data is accurate, complete, timely, consistent across systems, standard-conformant, and free of duplication.

Headless BI

Headless BI is an approach to business intelligence where the metric and semantic layer sit behind an API, separate from built‑in visualizations. You define metrics once, then query those definitions from any front end, so every destination shows the same numbers.

Extract, Transform, Load (ETL)

ETL is a three‑step data process that helps you turn raw inputs into trustworthy information you can use. You extract data from multiple sources, transform it by cleaning and structuring it, then load it into a destination such as a data warehouse or lakehouse where your team can access it. Put simply, ETL is how you turn scattered, messy data into something clear and usable.

Extract, Load & Transform (ELT)

ELT is a data integration method that pulls data from your sources, loads it into a centralized store such as a cloud data warehouse or data lake, then transforms it into analysis‑ready tables. You get raw data available quickly, and you shape it for analysis using the compute power of the destination.

Metric Tree

A metric tree is a visual or conceptual model that maps how key business metrics relate to each other. It links a top‑level outcome, like revenue or retention, to the contributing drivers that explain changes underneath. You get a clear, shared view of cause and effect across teams.

Metric

A metric, in the context of analytics, is a calculated value that tracks performance for a business activity. Think of it as a consistent math formula applied to your data over time, like revenue, conversion rate, or churn rate. A metric includes a clear formula, time frame, and rules for how to slice the data. It turns raw numbers into a repeatable signal you can compare across periods, products, regions, or segments.

Business Intelligence

Business intelligence (BI) is the practice of collecting, organizing and analyzing data to help organizations make informed decisions. BI tools turn raw data into actionable insights through visualizations, reports and dashboards.

API

An API is a contract that defines how to request data or actions from a system, and what will be returned. Think of it like a restaurant menu and order slip. You ask for a dish, the kitchen prepares it, and you get exactly what you asked for.

Data Lake

A data lake is a central repository that stores raw, structured, semi-structured, and unstructured data. Think of it as a data sandbox where you collect everything in its original format until you’re ready to analyze it.

Data Lineage

Data lineage maps the journey of your data from origin to destination. It visually shows where data comes from, how it’s transformed, and where it’s used.

Data Stack

A data stack is a set of tools, services, and procedures that work together to collect, process, store, and analyze an organization’s data.

Dimension

A dimension, in the context of data, is a descriptive attribute that provides context for your metrics. Think of dimensions as the categories or labels—like date, region, or product line—that you use to group, filter, or slice your data.

Data Catalog

A data catalog is an organized inventory of a company’s data assets. This centralized, access-controlled library typically lists datasets, tables, and fields alongside owners, definitions, and lineage so people can search, understand, and use data with confidence.

Cardinality

Cardinality describes how unique the values in a column are. It also plays a role in defining how tables relate to each other. A high-cardinality column contains many unique values, while a low-cardinality column contains few unique values.

Data Visualization

Data visualization is the representation of data as charts, diagrams, pictures, or tables. When information is presented visually, it’s easier to see patterns and quickly spot outliers. Data visualizations are also perfect for performing comparison and forecast analyses.

Data Warehouse

A data warehouse is a centralized repository that stores and organizes structured data from multiple sources. Optimized for reporting and analysis, warehouses give businesses a unified view of their historical and current data.

Key Performance Indicator (KPI)

A key performance indicator (KPI) is a measurable value that shows how effectively your organization is achieving its most important objectives. Think of KPIs like the gauges on your car dashboard—each one gives you real-time feedback to help you maintain your engine and stay on course.

Member

A member, in the context of data, is a specific, unique value within a dimension that represents an individual entity, category, or attribute. Think of a member as an item in a list—like “Q1 2025” in a list of time dimensions or “Blue T-Shirt” in a list of product dimensions.

Measure

A measure, in the context of data, is a quantifiable numeric value used to track and analyze data. It represents a calculation—like sum, average or count—that’s performed on raw data points.

Knowledge Graph

A knowledge graph is a structured network that represents real-world entities (people, places, products, metrics) and the relationships between them. It adds context to data, so systems and people can make smarter decisions.

MCP Server

An MCP server is an open‑standard service that exposes tools and resources to AI clients using the Model Context Protocol. It lets AI models retrieve live data and perform actions—securely and consistently—against systems like file stores, APIs, and development platforms.

Data Governance

Data governance is the system of people, policies, and tools that keeps data accurate, secure, and available. Think of it like hiring a skilled librarian for a massive library. Every book is cataloged, protected, and accessible to those with the right permissions (a library card). In analytics, data governance enables your team to work with consistently-defined data that’s accessed based on user-specific roles and permissions.

Metric Catalog

A metric catalog is a centralized library of standardized metrics and KPIs, each with a clear name, formula, and description. Think of it as a reference guide that ensures everyone in your organisation measures progress the same way.

Objectives and Key Results (OKRs)

Objectives and Key Results (OKRs) are a goal-setting framework that helps teams align on ambitious goals and measure progress with specific, quantifiable results. Objectives define the destination on the roadmap, while 2–5 Key Results act as milestones that track success. Objectives inspire and guide teams, and Key Results keep everyone accountable by focusing on measurable outcomes.

Online Analytical Processing (OLAP)

Online analytical processing (OLAP) is a technology that enables fast, ad-hoc analysis of multidimensional data. By organizing information into “cubes” of measures and dimensions, OLAP lets you slice, dice, and pivot large datasets in near real time.