Make the most of the data pipeline with the DevOps dashboard

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The DevOps dashboard template gives you a glimpse of what’s possible with the data pipeline. The template offers useful insights into the health of your engineering teams. We hope it will also provide a great jumping off point for creating your own dashboards and reports.

Download the template:

On this page:

With the comprehensive data available for people, projects, and issues in the data pipeline, managers and business leaders can identify trends in productivity, quality, responsiveness, and predictability of work. While the Jira UI is focused on getting work done, dashboards and reports like this one provide insights on a macro level.

In this guide, we’ll provide some explanation of the metrics we’ve included, how they’re calculated, and what they might indicate about your team. 

If you want to dive straight into connecting the template to your own data source, see: 

DevOps dashboard at a glance

Here’s the DevOps dashboard in Tableau, populated with the sample data included with the template.

DevOps dashboard in full, with numbered annotations pointing to the areas to be described in the numbered list below.

  1. Project summary lists the projects (or project categories if you choose to aggregate by category) featured in the dashboard, and shows the percentage of resources directed towards a project (measured by issues closed by team members). This list also acts as a filter for the report, if you want to drill down and see data for a specific project or project category.
  2. Allocation metrics shows the percentage of resources directed towards each project (measured by issues closed by team members) to projects or project categories each week during the reporting period.
  3. Quality metrics charts the quality, responsiveness, productivity and predictability metrics for all projects or project categories.
  4. Dashboard settings configure the data to be included in the dashboard, including the date range, project or project category aggregation, and issue types to include.
  5. Quality summary shows aggregate values for each of the four quality metrics by project, to help you identify outliers or systemic problems (or successes).

DevOps metrics in detail

For the explanations below we’ll assume you aggregate data in the dashboard by project. However, you can also choose to aggregate by project category. The concepts are the same for either option.

Allocation

In the DevOps dashboard, the project allocation section shows the percentage of your team directed towards a project. This data can help you ensure you have coverage of your most important projects.

How is it calculated?

Because Data Center applications don’t have the concept of a "team", we’ve used issues closed by a person as a proxy. We treat the people who closed issues in a project as ‘team members’ for the purposes of that project. You can filter the report to only include particular people in a team or department.

It’s worth noting that allocation is not indicating effort or time spent. The best way to explain how allocation is measured, is with a few examples.

Show me an example...

First let’s look at how allocation works for just one person. Jie closed a total of 10 issues across two projects during the time period.

User

Total issues closed

Project key

Issues closed in project

Allocation

Jie

10

InterNets

1

0.1

Project Trojan

9

0.9

The allocation of this ‘team of one’ therefore is 10% to the InterNets project, and 90% to Project Trojan.

Now let’s add some more people and projects. In this example, we have a team of four people, who closed issues in four projects during the time period.

User

Total issues closed

Project key

Issues closed in project

Allocation

Alana

5

Oyster Project

5

1

Fran

7

Oyster Project

7

1

Jie

10

InterNets

1

0.1

Project Trojan

9

0.9

Omar

2

InterNets

1

0.5

Matadors

1

0.5

We get the project allocation by dividing the team members allocation by the total allocation, which in this example is 4 (1+1+0.1+0.9+0.5+0.5).

Project

Allocation

Oyster Project

50% (2/4)

InterNets

15% (0.6/4)

Project Trojan

23% (0.9/4)

Matador

13% (0.5/4)

This shows that 50% of your resources were directed towards the Oyster Project, and 15% to the InterNets project.

If you were to filter out the Oyster Project, the calculations change as follows.

User

Total issues closed

Project key

Issues closed in project

Allocation

Alana

-

-

-

-

Fran

-

-

-

-

Jie

10

InterNets

1

0.1

Oyster Project

9

0.9

Omar

2

InterNets

1

0.5

IOS

1

0.5

Now, 30% of resources (in the report scope) are directed towards the InterNets project.

Project

Allocation

InterNets

30% (0.6/2)

Project Trojan

45% (0.9/2)

Matadors

25% (0.5/2)

These calculations are all based on the person who completed an issue. Usually this is the assignee, but if a completed issue doesn’t have have an assignee, we’ll use the issue history to determine who completed the issue. If there are multiple users in the issue history, we use the user who first completed the issue

What does it indicate?

This metric helps you identify trends, and see where you may be able to re-deploy people onto projects with higher importance to the business.

This data can also be used in conjunction with the rest of the dashboard data, to see the impact of higher or lower allocation levels on other metrics.

Allocation bar graph showing two projects, with bars to represent the allocation each week

Screenshot showing allocation metrics for two projects

Quality (defects)

In the DevOps dashboard, we approach quality from an engineering health point of view. When left unchecked, technical debt will impact your team’s ability to deliver quality software. By examining how you prioritize fixing defects over new feature work, you can spot trends that may indicate an engineering health problem.

How is it calculated?

Quality = Defects resolved / total issues resolved x 100

Put simply, of all the issues resolved in a given week, what proportion were bugs and other ‘defect’ issue types. Remember, we’re focused on engineering health indicators, not product or end-user experience quality.

You can define which issue types to treat at ‘defects’ in the dashboard settings. For example, you may have different issue types for bugs, architectural tech debt, and failing tests that can all be classed as ‘defects’ in the dashboard.

What does it indicate?

A steady line indicates the team is balancing technical debt with new feature work. That’s an indicator of a stable system with good engineering health.

If the line starts trending up (as in the example below), it might indicate that developers delaying working on defects until the end of a project, or you the team’s focus has shifted to new feature work.

If the line starts trending down, developers may not be paying enough attention bugs, and you may have a quality issue in future.

If there are many sharp peaks and troughs, it would be worth delving deeper, and finding out what was happening during those weeks. There may be some team specific context you’re missing, such as an incident or release deadline. You may also want to filter the dashboard by a specific project to see if the trend is noticeably different to the aggregate for all projects.

Line graph showing quality metric over time, the line has sharp peaks and troughs

Screenshot showing Quality (defect) chart for one project, compared to all projects

Responsiveness

In the DevOps dashboard, the responsiveness chart indicates how fast a team is completing work, by measuring the time to completion.

Responsive teams are extremely valuable to the business. They quickly deliver user facing value on a known cadence. With smart investments, you can build a strong and responsive team which enjoys the work, keeping customers and the business happy, without sacrificing the other dimensions long-term.

How is it calculated?

Responsiveness = resolution date - created date

The value is expressed in days. Only issues that were completed within the given time frame are included.

What does it indicate?

The solid lines indicate actual time to completion, and the dotted line indicates the direction it’s trending.

A steady line indicates the team is able to deliver customer facing value quickly. It may indicate good estimation practices, as work is broken down into similar sized, manageable chunks.

If the line is trending up, the team is taking longer to complete issues and deliver value. This may indicate an issue sizing and estimation problem in the team.

If the line is trending down, the team is taking less time to complete issues. Generally this is a good trend, but again, it can indicate an issue sizing or estimation problem.

Sharp peaks indicate the average time to complete an issue was higher than usual. It might be worth investigating what issues were completed in the time period, to see if the problem was caused by a single issue, or resourcing issues.

Line graph showing responsiveness metric over time, the line has a sharp peak in the middle of the graph

Screenshot showing the responsiveness chart for one project, compared to all projects.

Productivity

In the DevOps dashboard, the productivity chart indicates the quantity of work being delivered.

How is it calculated?

Productivity = number of issues completed

This is a simple metric that shows the volume work that meets the team’s definition of done in the given time period.

What does it indicate?

A steady line indicates the team is working through a similar number of issues in a given time period. This can be an indicator of a stable workload, as a result of good estimation and sprint planning.

Sharp peaks and troughs may indicate a problem with workload, or with estimation. It could be worth delving deeper, and finding out what was happening during those weeks, and perhaps comparing this data to the allocation data for the same period.

Comparing a single project to all projects can be daunting, but focus on the shape of the line, rather than the difference in the number of issues.

Line graph showing productivity metric over time, the line is steady, only increasing slightly

Screenshot showing productivity chart for one project, compared to all projects

Predictability

In the DevOps dashboard, the predictability chart indicates how consistent the pace of work is, by comparing the number of issues started (transitioned to an ‘in progress’ status) with issues completed over time.

How is it calculated?

Predictability = issues completed - issues started

While responsiveness is measured in time (days), predictability focuses on the flow of work, specifically the volume of work that is started versus work completed within the same time frame.

What does it indicate?

Ideally, the number of issues started and completed in a given period should be about the same, around 100% on the chart.

A value greater than 100% positive value indicates that more issues were completed than started. This is a good trend, but may indicate that people aren’t picking up new tasks after completing a task.

A value lower than 100% indicates more issues were started than completed. This may indicate that work items are too large to be completed within the time frame, or that the team is starting too many things, and could benefit from some work in progress limits.

Line graph showing predictability metric over time, the line has many sharp peaks and troughs

Screenshot showing predictability for a project, compared to all projects.

Quality summary

In addition to the individual charts for quality, responsiveness, productivity, and predictability that allow you to compare one project to all projects, you can also see a summary view which shows the aggregate value for each project individually.

What does it display?

Aggregate for each project

For example, the responsiveness chart will show the aggregate time to completion for an individual project in the reporting timeframe.

When reading this chart, remember that only the x-axis is important. The y-axis is spacing the projects out randomly, so the dots don’t overlap. Hover over each dot to see the project name and value.

What does it indicate?

This helps you identify projects that are outliers, where it might be beneficial to investigate further.

You may also observe organization-wide trends that may indicate a bigger engineering health or agile craft problem.

As with the other charts, it can be useful to contrast the data with the allocation data. Perhaps the reason for low productivity and responsiveness is due to too few team members contributing to the project.

Four separate scatter graphs for each of the quality metrics, dots are clustered mostly on the left side of the graph

Screenshot showing the quality summary for all projects in the dashboard scope

Configure the dashboard settings

Once connected, you can configure the dashboard to show particular projects, team members, and time periods.

To configure the dashboard:

  1. Select the Settings icon on the dashboard.
  2. Set any date, project, team, and issue settings. Refer to the table below for information on each setting.
  3. Select the Close icon to close the settings pane.

Here's a summary of the available settings. 

Settings

Description

Date range

Drag the slider to set the start and end dates to report on. The dashboard can only show data for the date range included in the data pipeline export.

Time granularity

Select whether you want to display charts by week, month, quarter or year.

Dashboard aggregation

Select whether to report by project, or by project category.

Projects / project categories

If aggregating by project, select specific projects to report on. If aggregating by project category, select specific project categories to report on.

The list will include all projects or project categories included in the data pipeline export.

Team members

Select a sub-set of users to report on. By default this field returns user ID. You may be able to change the query to select by user name or full name, if that data is available.

Issue types

Select the issue types to include in the dashboard. For example you could choose to exclude Epics or Service requests.

Defect issue types

Select the issue types you want the dashboard to treat as ‘defects’. This is used in the quality metric to indicate the effort spent fixing bugs and other defects.

Make your data work for you

We hope the DevOps template has given you a valuable insight into your team’s current engineering health, and sparked a few ideas for how you can use the data pipeline data to make better business decisions.

You should treat this template as a jumping off point. The way we have chosen to calculate the metrics may not suit the way your teams work. Take the responsiveness and productivity charts for example. We’ve used an issue as the basic unit of measurement. This works great for Kanban teams, where we can expect issues to be of a similar size. However for a scrum team, you may find it better to use story points as the unit of measurement, so you can measure the average completion time per story point, rather than per issue.

Ready to get started? 

Last modified on Dec 9, 2021

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