Screenful x GitHub: Advanced analytics for GitHub Projects

Aug 8, 2023

We're thrilled to announce the release of Screenful, the user-friendly analytics tool for the new GitHub Projects. With Screenful, you can easily create advanced charts from issues and pull requests, and share them with all stakeholders in the form of dashboards and scheduled reports.

Why GitHub + Screenful?

By integrating with GitHub, Screenful offers valuable insights into your software development process. With Screenful, you can:

  • Get out-of-the-box insights into your draft issues, issues, and pull requests

  • Slice and dice your data by any of your custom fields

  • Create custom charts, including advanced ones like Scatter plot, Burndown chart, Table chart, Workload (planned), and Forecasting chart

  • Forecast your delivery dates using historical data

How to set up the integration

Setting up the Screenful GitHub app is easy. Just follow these steps:

  • Go to the Analytics & Reports App page and click Install

  • Create and register the GitHub App

  • Import repositories and projects (classic or new) as data sources

Your setup is complete, and you can navigate to the Insights tab to see the automatically generated charts and metrics. If you have added custom fields to your boards, they are automatically imported and made available for charts.

Creating custom charts

To create a custom chart, go to the Charts tab and click Add new chart in the top right corner.

A modal is opened with a set of chart templates. You can create a new chart from scratch or pick one of the predefined charts.

When you click Create chart, the chart is created, and you can find it under the Charts tab under the main navigation.

From there, you can assign it to a dashboard or a report, or share with others using the share links.In addition to the common ones like line, bar, and pie charts, we also have more advanced charts such as the Scatter plot, Burndown chart, Table chart, Workload (planned), and the Forecasting chart.

Forecasting your delivery dates

As a project manager, the question you often hear from stakeholders is when will it be done? It’s an easy question to ask but much harder to answer with any level of certainty.

The traditional approach is to detail out all requirements and estimate each task in story points or hours/days, and then use that data to come up with the project timeline. The problem is that people, in general, are not very good at estimating how long some piece of work takes.

Fortunately, there is an alternative approach, which is based on looking at your historical data and extrapolating that to your future work.

Here’s a chart that shows how much work a team has completed per week during the last 3 months:

If team composition is the same for the remainder of the project, wouldn’t it be helpful to use this information to estimate how long it takes to complete the remaining work?

A data-driven approach looks at your historical throughput and makes a prediction based on that. Here’s an example of such a forecast using the Forecasting chart

This chart looks at the weekly history of the team’s throughput and creates three scenarios based on the distribution of the work completed per week:

  • Optimistic: 80th percentile

  • Most likely: 50th percentile

  • Pessimistic: 20th percentile

The optimistic scenario expects that your team will complete more work than in 80% of the past weeks. In the most likely scenario, your team completes the equal amount as what is the median week. In the pessimistic scenario, the team completes only as much as 20% of the past weeks.

You can learn more about the Forecasting chart from this how-to guide.

Sharing with reports

As a project manager, the question you often hear from stakeholders is when will it be done? It’s an easy question to ask but much harder to answer with any level of certainty.

The traditional approach is to detail out all requirements and estimate each task in story points or hours/days, and then use that data to come up with the project timeline. The problem is that people, in general, are not very good at estimating how long some piece of work takes.

Fortunately, there is an alternative approach, which is based on looking at your historical data and extrapolating that to your future work.

Here’s a chart that shows how much work a team has completed per week during the last 3 months:

If team composition is the same for the remainder of the project, wouldn’t it be helpful to use this information to estimate how long it takes to complete the remaining work?

A data-driven approach looks at your historical throughput and makes a prediction based on that. Here’s an example of such a forecast using the Forecasting chart

This chart looks at the weekly history of the team’s throughput and creates three scenarios based on the distribution of the work completed per week:

  • Optimistic: 80th percentile

  • Most likely: 50th percentile

  • Pessimistic: 20th percentile

The optimistic scenario expects that your team will complete more work than in 80% of the past weeks. In the most likely scenario, your team completes the equal amount as what is the median week. In the pessimistic scenario, the team completes only as much as 20% of the past weeks.

You can learn more about the Forecasting chart from this how-to guide.

Avísanos si tienes preguntas o comentarios contactando hello@screenful.com. Para estar al tanto, lee nuestro blog, o síguenos en LinkedIn

This article was written by Sami Linnanvuo

Sami is the founder & CEO of Screenful, the company that turns data into visual stories. You can find him on Twitter.