Plandek Academy logo

Sprint Delivery Guide – 3

Download the guide

Table of Contents

3. Advanced intelligent analytics to improve Sprint and Epic delivery

Applying intelligent analytics to the Agile delivery process is constrained by the quality of the underlying data available (e.g. the quality of data residing in workflow management tools like Jira).

It is also constrained by the nature of Agile, whereby much of a ‘project’ or product will remain undefined and un-estimated, limiting the available data set from which to derive intelligent analytics (e.g. predicting a software delivery date).

Despite these constraints, intelligent analytics can significantly benefit software delivery teams, particularly at the team/Sprint level.

The example below shows a view from Plandek’s LiveView intelligent analytics tool at the Sprint level.

The LiveView platform analyses the entire ticket history of the team in question to compare it to the current ticket set to identify anomalies.

Having identified meaningful anomalies, LiveView’s machine-learning algorithms quantify risk and identify ‘Action items’ (mitigations) that can be taken to correct course and improve Sprint accuracy.

Plandek LiveView intelligent analytics to improve Sprint performance
Plandek LiveView intelligent analytics to improve Sprint performance

‘Action items’ point teams to tickets outside the expected parameters. The parameters considered include:

  1. Ticket stalling: when tickets appear to stall at any point throughout the delivery process or exceed standard cycle times and require immediate action by the team.
  2. Distractions: where there is evidence that team members are working on other tickets outside the Sprint simultaneously.
  3. Carry-overs or Work added: tickets being worked on that are carried over from previous Sprints or have been added after the Sprint has started.
  4. Burndown anomalies: work left to do relative to time left in the Sprint exceeds the team’s norms.
  5. Development complexity: commit size/count, files changed, and other parameters exceed the norm for tickets of similar size and complexity.
  6. Pull request anomalies: when pull requests stall or indicate a higher-than-normal level of activity, suggesting a team is struggling to merge.
  7. Build event anomalies: when the builds containing the related tickets fail or succeed. Deployment anomalies: when tickets are deployed or if the deployment fails and requires action.

LiveView is an example of intelligent analytics supplementing the expertise of the Team Lead/ Scrum Master by reviewing a tremendous amount of data and identifying anomalies or blockers that are not necessarily visible ‘to the naked eye’.

It also provides:

  • A quantitative risk assessment for Sprint completion that can be aggregated to consider risk at the broader Epic or release train level.
  • Suggested ‘action items’ or mitigations to improve Sprint outcomes (prescriptive data analytics). As the algorithms improve, so do the quality and breadth of these system-generated action items.

A similar principle can be applied to Epics.

Example: Plandek LiveView intelligent analytics to improve Epic performance
Example: Plandek LiveView intelligent analytics to improve Epic performance

Again, the algorithms look for issues that create risk and require mitigation. These data can be used to predict Epic timing and to reduce potential delays.

The quant risk data can also be aggregated across products or release trains (for example) to understand delivery risk across roadmap items better.