Velocity Reporting Has Gone Wrong + 5 Strategies to Do it Right

Alex Omeyer
7 min readNov 8, 2023

We often think of velocity as a metric or set of metrics.

But that isn’t how it should be. When we talk about our velocity, we need to treat it as a narrative with metrics contained within.

That narrative needs to be context-rich and nuanced, while still being concise.

But the value of narrative is often lost in translation due to the shortcomings of conventional reporting practices.

There are a number of ways velocity reporting can be lacking. Here are a few you might recognise:

  • Lacking context — Simple charts show progress but miss the “why” behind the data.
  • Lacking commentary — Numbers without insights or explanations leave stakeholders guessing.
  • Manual hassle — Time-consuming updates invite errors and sap productivity.
  • Integration woes — Mismatched tools can complicate and confuse reporting.
  • Complexity overkill — Oversized reports don’t get read, and overly technical reports don’t get understood.

So in this article, I’m going to look at five decent ways to report on velocity via velocity chart tools and velocity reporting software. and explain which problems they solve. Some solve different problems better, but they all have the potential to streamline and improve reporting processes.

1. Your PM tool’s native velocity chart

Your Agile project management tool might have native support for velocity reporting.

For example, Jira allows you to create velocity charts to show the work being completed during sprints, if you use Scrum.

To create a velocity chart in Jira, you’d need to configure a board to define estimation statistics and time tracking, then create it via the board’s Reports section after completing sprints.

In general, velocity charts help teams estimate how much work they can complete in future sprints. Here’s a basic outline of how they are calculated:

  1. Story points estimation. Tasks the team can complete are estimated using units like story points, ideal days or hours in sprint planning.
  2. Sprint completion. The team works through the backlog items for the sprint length.
  3. Velocity calculation. Review story points completed (or a parallel metric). If the tea finished tasks estimated at 5, 3 and 2, the velocity for the sprint is 10.
  4. Velocity over time. The agile metrics of velocity each sprint are tracked over multiple development cycles.

Here’s an example of a Jira velocity chart:

Of course, Jira isn’t the only tool to have some native support for velocity charts. ClickUp and Wrike natively offer them, while Monday and Asana have some ways of tracking velocity without charts. Velocity is a third party tool for Asana reporting.

Naturally these native charts are only suited to teams already invested in their respective ecosystems.

The main problem with native velocity charts is that they don’t tell the full story. They:

  • Lack context — this has to be added manually
  • Lack commentary — again, we have to add this manually
  • Form only part of the reporting usually required — you might need to add burndown charts, allocation and completion metrics (for example in a sprint report)

And as my colleague said, these out-of-the-box solutions have serious limitations when it comes to reporting.

2. Stepsize AI

Stepsize AI is an automated product development reporting tool.

At the moment, it integrates with Jira and Linear. Integrations for Notion, Asana and ClickUp are coming soon.

Stepsize AI automatically analyses everything that happens in your project management software to deliver weekly updates on team and project progress. It uses AI to understand the context of your projects, linking tasks to goals and activities, summarising sprint themes and goals, and monitoring key product development metrics.

The result is super-accurate weekly reports with the perfect amount of context and detail, with your velocity chart created for you.

The main benefits of using a tool like Stepsize AI include

  • Totally effortless velocity charts (and other metrics)
  • Context-rich reports created by AI that grasp the nuanced context of projects and goals
  • Concise, readable updates with the option to expand for more information, and links to sources
  • Goal-centric reporting that innately links your project and sprint progress to objectives

Setup is simple. Once you’ve integrated your board, which takes less than a minute, the AI takes care of the rest, forever.

And yes — it’s a security-first tool, so you’ll be protected by 256-bit encryption and your data never trains AI models.

We think Stepsize AI is a big win for teams that want to minimise manual reporting efforts and get accurate, reliable product development reports.

You can generate your first AI-powered report for free — it takes a few minutes, and you don’t need any technical skills.

3. Haystack

Haystack is an Agile analytics tool.

Its main party piece is how good it is at offering real-time insights into the software development lifecycle

Haystack automatically reports on velocity through a dashboard on a sprint-by-sprint basis. Although it doesn’t offer AI-driven context and commentary as with Stepsize AI, there’s release notes and milestone tracking to help paint the picture.

After this, we’ll look at some generalist tools, including Tableau and Metabase. Unlike these tools, Haystack can highlight trends and potential bottlenecks without manual input.

Another strength it shares with Stepsize AI is that, because it uses data direct from tools like Jira, it provides a view of velocity that is undistorted by opinion.

Where Haystack is perhaps weakest is that it still relies on human commentary, and relies on human reporting to add key context and align the reports with goals. That said, for teams invested in data-driven project management, Haystack offers a comprehensive suite of analytics tools.

Also look at: Keypup or Swarmia, other software delivery metrics tools.

4. Metabase

Metabase is an open-source business intelligence tool.

Its USP is that it enables companies to explore and visualise their data without necessarily requiring SQL knowledge.

This, along with what we think is a user-friendly interface and wide range of integrations, make it a win particularly for smaller teams without a huge data operation.

To use Metabase for measuring velocity, you would do something like this:

  1. Connect it to your data source, such as Jira.
  2. Use Metabase’s visual query builder to select relevant fields like ‘Created Date’, ‘Issue Type’, and ‘Status’.
  3. Apply filters to include only completed issues within your desired timeframe.
  4. Create a calculated field to count the number of closed issues as your velocity metric.
  5. Plot it on a line chart with the velocity on the y-axis and time on the x-axis

This will give you a clear visualisation of your team’s sprint velocity over time. Here’s some general information about visualising data in Metabase.

Its setup process does not require extensive technical skills, making it reasonably accessible to non-developers.

Of course, Metabase doesn’t solve the problem of needing to manually add context and commentary, and you still have to lift your velocity chart out of Metabase to include it in reports, such as sprint reports.

That said, our team are fans of Metabase and we use it for some internal reporting tasks. You won’t need a data specialist or large data operation, nor extensive technical skills, to get value out of it.

Also look at: Trevor.io, which allow non-technical teams to work with data without SQL, or RestApp, which abstracts Python and SQL into a visual interface.

5. Tableau

Tableau is a powerful data visualization tool.

It’s widely used for business intelligence and is an extremely powerful tool for creating interactive, shareable dashboards for all kinds of purposes. With that power comes technical challenge. Unlike other tools on this list, to get the most out of Tableau, you’ll need people with specialist skills. It’s a tool suited to larger businesses and enterprises.

Nevertheless, it’s entirely possible to build powerful velocity measurement tools in Tableau.

Roughly, you’d follow these steps:

  1. Connect to their Jira data source using a compatible connector.
  2. Select the relevant fields like ‘Created Date’, ‘Issue Type’, and ‘Status’.
  3. Filter the dataset to include only closed issues within the desired timeframe.
  4. A calculated field named ‘Velocity’ would be created with a formula to count closed issues, and this field would be placed on the Rows shelf, while ‘Created Date’ would go onto the Columns shelf.
  5. Adjust the chart to display the data as a line graph.

There’s certainly a learning curve associated with Tableau, but once you have the right skills in your team, it can be suitable for teams of any size. Plus, it’s incredibly scalable and is as close to “unlimited” as you’ll get in this space.

As with Metabase, relying on Tableau for your velocity charts means you have manual work to do in order to create reports and add context and commentary.

Also look at: Qlik Sense or Domo for enterprise-ready generalist BI.

If you use Jira or Linear, consider Stepsize AI to automatically create comprehensive, accurate and context-rich reports which include your velocity chart by default.

I’m building Stepsize AI with my team, and I’d love your feedback.

Your first weekly report and velocity chart are free, and you can set it up in minutes here.

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Alex Omeyer

Building Stepsize, a SaaS company to measure and manage technical debt.