Linear Insights

Take the guesswork out of product planning with real-time, actionable data analytics.

Instant analytics for any stream of work

Linear Insights delivers deep, real-time visibility at your fingertips. Aggregate, segment and visualize data across your entire workspace. Spot trends, remove blockers, and progress faster.

Three different Linear views with the insights panel opened, showing two scatter plots and a bar chart.

Always in context.
Across every single view.

Insights come attached to every view across the app, illustrating whatever list of issues you are currently seeing. Gain real-time insight into projects, cycles, or any custom view.

The table of an insights view, showing issues segmented by priority. A cell within the "Urgent" column is highlighted with a blue border, indicating that a quick filter is applied to find Urgent issues.

Actionable quick filters

Drill down on any data point to get an instant view of all the underlying issues and immediately take action.

Zoom out to narrow in

Get a high-level view of everything that is happening in your workspace. Identify which areas need your attention and make better decisions.

Go fullscreen

Analyze data in context or zoom into details in a fullscreen view.

Shareable links

Share your insights reports as a link to align your team.

CSV Export

Download any insight data as a CSV to run your own analysis.

Instant

Optimized for speed so you can generate reports in seconds.

How teams use
Linear Insights

Effort Distribution

“Where are we spending our resources?”

As CTO, you want to gain a better understanding of where the efforts of your teams are being allocated. Which projects have taken most attention recently?

As a first step, we create a custom view with all completed issues across all teams in your Linear workspace. Let’s filter that list down to the last three months to focus on the most recent quarter.

To understand which streams of work your company allocated most resources to, we look at the number of issues per project. It seems like a lot of work was spent on your AI-related projects, which aligns with your Q1 goals.

Let’s narrow in and segment your data by team. By applying a quick filter, we get a better picture of the impact of a particular team.

We can also drill into a segment and see the exact team distribution for each project by hovering over each slice.

Insights panel showing projects ranked by issue count.

“Where are we spending our resources?”

As CTO, you want to gain a better understanding of where the efforts of your teams are being allocated. Which projects have taken most attention recently?

We first create a custom view with all completed issues across all teams from the last three months

Number of issues per project tells us where you spent your resources

When we segment the data by team we get a better picture of the impact of a particular team

Looks like a lot of work was spent on AI-related projects

An annotated screenshot of the Linear insights panel. The "Measure" and "Dimension" dropdowns are annotated by the number 2. The "Segment" dropdown is annotated by the number 3. The "NLP chatbot" and "OpenAI Integration" project rows are annotated by the number 4.

Bug clearance

“Are we getting better at fixing bugs?”

Your intuition tells you that the number of reported bugs has increased in recent months. But is that actually true? And if so, is your clearance rate improving or deteriorating?

To get a sense check, let’s create a custom view with all bugs in your workspace. With Insights, we can now visualize how many bugs have been created over time.

The data supports your intuition: The number of bugs has indeed been going up.

The segmentation option allows us drill further into the data. When we segment the data by status, for example, we can see that the bug clearance rate has also gone up.

After we segment the data by team, we find the answer for the increase in reported bugs: the new mobile team that joined recently to build the long awaited iOS app has obviously filed a lot new bugs since they came on board.

Lastly, we want to look at clearance latency: how quickly do bugs get fixed? Cycle times seem to have been pretty constant - except for a few outliers. We can find out what’s going on by clicking on them.

Insights panel showing issues by created date.

“Are we getting better at fixing bugs?”

Your intuition tells you that the number of reported bugs has increased in recent months. But is that actually true? And if so, is your clearance rate improving or deteriorating?

To get a sense check, let’s create a custom view with all bugs in your workspace

How many bugs have been created over time?

The data supports your intuition: The number of bugs has indeed been going up.

Good news! When we segment the data by issue status, we can see that the bug clearance rate has also gone up

An annotated screenshot of the Linear insights panel showing a bar chart of issues over time. Each bar is segmented by issue status. The "Measure: Issue Count" and "Dimension: Created date" dropdowns are annotated by the number 2. The bar chart's x-axis is annotated by the number 3. The blue section of the last bar in the chart (representing the segment of "Done" issues) is annotated by the number 4.

Lastly, we want to look at clearance latency: How quickly do bugs get fixed?

Cycle times seem to have been pretty constant - except for a few outliers.

We can find out more about each outlier by clicking on the respective dot

Data hygiene

“Are we prioritizing issues consistently?”

You are in charge of an important, company-wide project. To ensure that everyone on the project works effectively, a decision has been made that every issue should have a priority.

We can do a data quality check directly in the project by opening the insights panel and creating a report with all issues sorted by priority.

It looks like there are still a lot of issues that haven’t been prioritized. We can add a team segmentation to the report to see if the unprioritized issues are concentrated on a specific team.

Turns out that the US engineering team hasn’t been prioritizing much of anything. Time to talk to the engineering manager!

The EU engineering team has done a more thorough job, but there are still a handful of issues left. Let’s apply a quick filter on all unprioritized issues and process them right away.