---
title: Terms and Concepts
slug: analytics/docs/terms-and-concepts
docTags: 
createdAt: 2026-07-20T15:44:27.406Z
---

The following are key terms and concepts used in the Optimizely Analytics product. See this [Glossary of Terms](docId\:omrbmE3jUtGBERqobzGBJ) for a comprehensive list of all terms.

### Application​

An [application](docId\:Yw_87b_j464Dphu2AnfD_) is a logical grouping of objects for a particular business use case. Managing different use cases across teams or initiatives is an organizational convenience. Applications do not share any state among them.

### Data source​

A [data source](https://support.optimizely.com/hc/en-us/articles/33903147727885-Connect-to-Data-Sources) is the enterprise repository of data – typically a data warehouse such as **Snowflake** or **BigQuery**.

### Connection​

A [connection](https://support.optimizely.com/hc/en-us/articles/33903147727885-Connect-to-Data-Sources) is a pipeline between Analytics and your data warehouse. Analytics issues queries to your data warehouse through a connection. Every application has one connection.

### Dataset​

A [dataset](docId\:ceeHwVegmTgp3ZecBx86k) is a tabular view (rows and columns) of data used for analytics. Datasets are logical views of data in your data source representing a business entity or activity, such as a user, session, ticket, account, or event.

### Event Stream​

An event stream is a dataset representing events, such as user interactions in a mobile app.

### Actor​

An actor is a dataset representing a business entity, such as a user, account, or document.

### Exploration​

An [exploration](docId\:u2tlXpjocdzP6-_-Odi10) provides analytical insight, such as retention, conversion, churn, and engagement. It is built using an exploration template and includes a query definition and a resulting visualization.

### Template​

A template is a predefined set of configurations for defining a particular object, such as an exploration. Templates offer an easy way to quickly author commonly used objects. Analytics offers templates for explorations, cohorts, derived columns, metrics, and datasets.

### Dashboard​

A [dashboard](docId\:giGhHNmkXnZv8Df87ShdT) is a collection of visualizations organized in a grid view. Dashboards are typically used to monitor commonly used operational metrics.

### Cohort​

A [cohort](docId\:qqERbaNW3LL9kPlSp27Lr) is a group of actors sharing common properties or behaviors. For example, a cohort of enterprise users hosted two meetings within the first week of signup.

### Derived Column​

A [derived column](docId:1wi6-v9k1O0weCWNUPCsS) is a calculated column of a dataset that is based on other columns of that dataset or related datasets. For example, a derived column of the **User** dataset – **Engagement Cadence** has daily, weekly, or monthly values for each user depending on their usage activity.

### Metric​

A [metric](docId:9zga6mORJKvRK6j3OqmXG) is a calculation that represents a business measurement. For example, the **Average Session Duration** represents the average time users spend in a session. The dimensionality of metrics is determined at the time of usage. For example, a report may look at the **Average Session Duration** for a particular cohort or users and a certain period.

### Folder​

A folder contains objects that can be saved with a name. Folders logically organize objects such as datasets, explorations, dashboards, and so on. They are hierarchical, such as a folder can contain sub-folders. Users and groups can secure folders.

### Catalog​

The [catalog](docId\:eD4_X8KtD2cLqebOWR7Fl) for an application is the repository of all metadata objects organized into folders. Users see parts of the catalog depending on their permissions.