Recently, we ran into a customer who has 16 Scrum Masters deployed throughout their development organization, tasked with performing analysis on how their teams are performing, and then putting the necessary adjustments and actions into practice. Not surprisingly, one of the tasks that they spend a lot of their time on is manually collecting data from their project management tool (in this case, it was Jira), putting that into a spreadsheet, and then spending hours trying to draw conclusions from their analysis.
This theme is echoed in many of the companies we talk to, where on average 20% of a Scrum Master’s time is spent on aggregating data, putting it into a spreadsheet, and then struggling to sift through large amounts of burndown charts and sprint reports. Also, she’s more than likely only analyzing one part of the development process, which may not paint a full picture of how a team is actually performing, and where the inefficiencies are.
On top of that, it’s imperative that she’s keeping the development team on track and consistently providing the necessary coaching and direction that’s relevant to what they’re doing in that moment in the development cycle.
If we look at Sales and Marketing organizations that have introduced a more advanced set of tools to their daily workflow, sales reps and marketing managers alike now have the ability to enhance their efficiency and effectiveness with closing deals, building and launching new lead-gen campaigns, etc.
For software engineering teams - and in this case, for a Scrum Master - AI-powered analytics is poised to help her command a much more productive and valuable presence in the overall development process.
Not only will it help her eliminate all of the manual data collection and analysis, but it will allow her to gain a much more complete and accurate picture of the entire development process, so she can spend more time on helping the team course correct, or address a challenge or opportunity in real-time. For example, if there’s a schedule deviation that occurs during the sprint because a certain story is taking longer than expected, the team can tackle that. Or, let’s say a new third party library is added to the codebase that will introduce a lot of bugs in the future, catching it before it’s committed and deployed will reduce issues later in the process.
Embracing AI-powered analytics will strengthen a Scrum Master’s toolkit, and help the team transform the way they plan and execute their projects. The Scrum Master 2.0 will be a lot smarter the next time she walks into a planning meeting, or when she addresses the pressure of a project stakeholder. Less busywork, higher value contributions.