Learn the language of data
The glossary
Your guide to analytics, metrics, KPIs, and dashboards. Clear definitions. Practical examples. All in one place.
12 terms
A
- What is Agentic AI?
- Agentic AI acts as a software teammate that completes multi-step work autonomously. It plans, uses tools, adapts based on results, and hands you something actionable, so you spend less time pulling numbers and more time making confident decisions.
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 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 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 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 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 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 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.
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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