This feature is currently in open beta.
Information in this article may not reflect the most up-to-date changes, as the product is actively being updated.
Greenhouse Analytics is a reporting experience that lets you explore your hiring data, track key metrics, and share insights across your organization. Whether you're monitoring pipeline health, evaluating source effectiveness, or reporting on time to hire, Analytics gives you the tools to configure and present your data in a way that's meaningful to your team.
Analytics is built around a simple structure: you define a dataset, visualize it, and organize those visualizations into dashboards for easy consumption and sharing.
Key concepts
Data view
A data view is the foundation of any analysis in Analytics. It's a saved configuration that defines which data you're working with, including the fields you want to see and any filters you've applied.
Think of a data view as the logic layer: it determines what data is returned, but it doesn't control how that data is displayed. One data view can power multiple visualizations.
Note: In Greenhouse Analytics, this is called a data view, not a report. Unlike traditional reports, a data view focuses purely on data configuration — formatting and presentation happen at the visualization and dashboard layers.
Visualization
A visualization is a chart or table that displays data from a data view. It maps your configured data into a visual format (such as a bar chart, line graph, or table) and lives inside a dashboard.
Visualizations don't store any data logic themselves. They rely on a data view to define the underlying dataset, which means the same data view can be used across multiple visualizations without duplicating any configuration.
Dashboard
A dashboard is a collection of visualizations organized into a single view. It's the presentation layer of Analytics — where you bring together multiple charts and tables to tell a complete story about your hiring data.
Dashboards can include visualizations powered by different data views, making them flexible for cross-cutting analysis.
How these objects work together
Analytics is structured in three layers, each building on the one before it:
Data view → Visualization → Dashboard
- A data view defines the dataset — which fields, filters, and logic determine what data is returned.
- A visualization takes that dataset and displays it as a chart or table.
- A dashboard brings multiple visualizations together into a single, shareable view.
This separation keeps data logic reusable, visualizations lightweight, and dashboards focused on presentation.
Terminology
Understanding a few core terms will help you get the most out of Analytics.
Dimension
A dimension is a categorical field used to group or segment your data. Dimensions let you break down measures to see how results vary across different slices of your organization.
Examples: Job name, Recruiter name, Job opened at, Is active coordinator?
Measure
A measure is a numeric field that can be aggregated. Measures are the quantitative building blocks of your analysis — they answer "how many" or "how much."
Examples: Applications, Accepted offer versions, Closed jobs
Metric
A metric is a defined calculation that combines one or more measures to express a business result. Metrics go beyond raw numbers to communicate performance.
Examples: Average time to hire, Average time to fill
Tip: When you see a raw count (labeled like "Applications" or "Jobs"), that's a measure. When it's a calculated result that reflects a business outcome (like "average time to fill" for openings), that's a metric.
Subject
The subject is the core entity your analysis is built around. It determines which fields are available, how objects relate to each other, and what each row of data represents in your data view.
Examples: Applications, Jobs, Offer versions, Openings
Choosing the right subject is one of the most important decisions when configuring a data view — it shapes everything else in your analysis.
Frequently asked questions
What's the difference between a data view and a report?
In traditional reporting tools, a "report" typically bundles together data configuration, formatting, and visual output into a single object. Greenhouse Analytics separates these concerns intentionally. A data view handles data configuration, visualizations handle display, and dashboards handle presentation. This structure makes your data logic reusable and your outputs more flexible.
Can the same data view power multiple visualizations?
Yes. Because data views and visualizations are separate objects, a single data view can be used as the basis for multiple visualizations — even across different dashboards. You don't need to recreate your data configuration each time.
Can a dashboard include visualizations from different data views?
Yes. A dashboard can contain visualizations powered by different data views, making it easy to bring together different areas of your hiring data in one place.
What's the difference between a measure and a metric?
A measure is a raw numeric value (like a count or amount). A metric is a calculated result that combines measures to express business performance, like a rate or ratio. In Analytics, you'll encounter both — measures are the ingredients, and metrics are the outputs derived from them.