Learn the language of data
The glossary
Your guide to analytics, metrics, KPIs, and dashboards. Clear definitions. Practical examples. All in one place.
27 terms
A
- What is agentic AI?
- Agentic AI is artificial intelligence that acts as an autonomous agent to achieve a defined goal. It plans steps, uses tools and data, and adapts based on results with limited human input. Agents work within clear goals, boundaries, and allowed tools, and they flag exceptions when they cannot proceed safely. They are only as reliable as the metric definitions and data they work from, so agree on definitions, sources, and targets before handing a workflow to an agent.
- What is agentic BI?
- Agentic Business Intelligence (Agentic BI) is an AI-driven approach where autonomous software agents analyze governed data, surface proactive insights, and take actions to achieve goals. It moves beyond static dashboards by using Large Language Models (LLMs) to understand intent, prep data, and automate workflows, so you can ask questions in natural language and decide faster.
B
- What is business intelligence (BI)?
- Business intelligence (BI) is the practice of collecting, organizing, and analyzing data to help organizations make informed decisions. BI turns raw data into answers through visualizations, reports, and dashboards, so leaders can see what changed and decide what to do next.
C
- What is cardinality?
- Cardinality describes how unique the values in a column are. It also defines how tables relate to each other. A high-cardinality column contains many unique values, while a low-cardinality column contains few.
- What is composability?
- Composability is a design principle for building complex systems from small, self-contained parts with clear interfaces. Like Lego bricks, standard pieces snap together to create new capabilities without starting from scratch. In metrics, base measures such as Revenue and Cost are defined once and reused to build other KPIs, so every number traces back to the same definitions and updates consistently when a base definition changes.
- What is a context layer?
- A context layer is a live, governed system that sits between your raw data and AI. It adds ownership, lineage, business rules, and metadata to every metric, so leaders and AI tools work from the same trusted numbers. It differs from a semantic layer, which standardizes what a metric means. A context layer also records who owns it, when it was updated, and how it connects to other data. The result is fewer conflicting numbers and answers you can act on without double-checking.
D
- What is a dashboard?
- A dashboard is an interactive, visual interface that brings your key metrics into one place so you can monitor and act fast. It turns raw data into charts, tables, and cues that make change and performance easy to spot.
- What is data analytics?
- Data analytics is the practice of examining, transforming, and modelling data to uncover insights, answer questions, and support decision-making. It turns raw records into patterns, trends, and relationships that leaders can act on. The four common types are descriptive, diagnostic, predictive, and prescriptive analytics.
- What is data governance?
- Data governance is a formal framework of people, policies, and technology that keeps an organization's data accurate, secure, and usable. Think of it as the "Librarian" of a massive digital library: every piece of data is cataloged, protected, and accessible only to those with the right permissions. For a business leader, it sets the rules for who owns each number, so decisions rest on information you can trust and stay compliant with privacy regulations.
- What is data integrity?
- Data integrity is the assurance that your data stays accurate, consistent, complete, and trustworthy 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 is the same every time. It depends on clear metric definitions, validation checks, freshness targets, and named owners, so leaders can act on their numbers with confidence.
- What is data lineage?
- Data lineage maps the journey of your data from origin to destination. It shows where data comes from, how it is transformed, and where it is used, so your team can trace any number in a dashboard back to its source and trust the answer before acting on it.
- What is data quality?
- Data quality measures the reliability of your data for making decisions. High-quality data is accurate, complete, timely, consistent across systems, valid, and free of duplication. When leaders trust the numbers, meetings move from debating whose number is right to deciding what to do next.
- What is a 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. Most stacks have three layers: ingestion, storage, and consumption. Together they determine whether leaders get a trusted answer or another spreadsheet to reconcile.
- What is data visualization?
- Data visualization is the representation of data as charts, diagrams, pictures, or tables so you can read patterns and spot trends quickly and accurately. This glossary entry explains how data visualization works, why it matters, how to choose the right chart type, and the tradeoffs to watch for, such as clutter and misleading scales.
- What is a data warehouse?
- A data warehouse is a centralized repository that stores and organizes structured data from across a company, so leaders can answer questions from one consistent source. Unlike operational databases that handle day-to-day transactions, a warehouse is built for OLAP (Online Analytical Processing). It keeps historical data in one place, so everyone works from the same numbers when they review performance and make decisions.
- What is a dimension?
- A dimension is a descriptive attribute that provides context for your metrics. Dimensions are the categories or labels, like date, region, or product line, that you use to group, filter, or slice your data.
K
- What is a 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 a car dashboard: each one gives you timely feedback so you can stay on course.
- What is a 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 leaders and systems can make better-grounded decisions. This entry explains how knowledge graphs work, why they matter, where they are used, and what to consider before building one.
M
- What is an 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 take actions, securely and consistently, in systems like file stores, APIs, and development platforms.
- What is a measure?
- A measure is a numeric value, such as a sum, average, or count, calculated from raw data to track and analyze performance. Measures turn raw data into numbers leaders can act on, and clear, consistent definitions keep everyone working from the same answer.
- What is 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.
- What is a 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.
- What is a metric catalog?
- A metric catalog is a centralized, governed repository of standardized business metrics and KPIs. It documents the name, calculation, and business context for every metric. With definitions in one place, Finance, Sales, and Marketing calculate progress the same way, so leaders get one answer instead of competing numbers.
- What is a 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 drivers underneath that explain why it changed. You get a shared view of cause and effect, so your team can agree on what happened and what to do next.
- What is a metrics layer?
- A metrics layer is a centralized abstraction layer that sits between your data warehouse and your downstream analytics tools. It lets you define business logic and KPI calculations, such as Gross Margin or Monthly Active Users, once in a single, governed location. Leaders then get the same answer no matter who asks or which tool they use.
O
- Objectives and Key Results (OKRs)
- Objectives and Key Results (OKRs) are a goal-setting framework that pairs an aspirational goal with 2 to 5 measurable results that define what success looks like. The Objective sets the destination; the Key Results confirm you're on track. OKRs help leaders keep teams aligned, focus on high-impact work, and make decisions based on measurable outcomes.
S
- What is a 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.
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