Mike Cannon-Brookes
Management
Sure. Thanks for the question, Brent. Look, there's no doubt that data is incredibly important to our customers. And we have an amazing depth and richness of data that is incredibly important to them. The more customers consume Atlassian products across the family, the more data about their history, project and task speeds, how that connects into documentation, who's collaborating with who, we have a phenomenal data set when we put it all together across our products. We are exposing that to customers in various different ways. Firstly, this quarter, you've seen -- well, first, I would I would start with, in the platform, infrastructural, we continue to build a large data lake where we are cross-referencing and connecting all of the different data points that we have across multiple products and across third-party products, I would say, in the various different markets and workflows that we're involved in. So in software, in IT operations and through to broader knowledge management and work management that data lake all of the principles that Scott just talked about. So this is not a trivial engineering exercise across billions and billions of data points, but to do it with data residency in mind and do it with all of the different enterprise compliance in mind is a nontrivial exercise. That's a part of what the Atlassian platform does in terms of the data and insights features keeping compliance and privacy in mind at all times. We then take that data lake. We can expose it to customers in a direct way. We have an early access program going on to expose that directly to customers to put into their broad-scale BI tool and mix and match with whatever data that they have. That's one potential way that, that is emerging for customers' insights in a particular customer users workflow to help them manage what they are doing at that particular time. So we shipped the series of features inside Jira Software this quarter where that emerged inside the software Board. So for example, we can look at your organization's general task completion rate in terms of how you do agile software. You can look at a particular team's task completion rate, how much work they tend to get through in a given sprints, two weeks. And then as they're doing sprint planning, we provide real-time insights to them rather than having to go look at some sort of reporting tool to work out the insights in real time, we say, Hey, you might be trying to do too much this next two weeks based on our analysis of your last -- however long they've been using it in the last two years' worth of work, this particular team, this piece of software, this code base. We know a lot about what's going on. So that is starting to emerge inside the product itself. And then lastly, you get to, as you mentioned, reporting in other areas. Chartio's strength is in its reporting and dashboards capabilities. You'll see the start to emerge, I imagine in the Atlassian platform as a broad-scale capability, much alongside automation and machine learning and other things where we look platform-wide across multiple products and also potentially seeing those reports and dashboards appearing inside singular products where that makes sense. It's a fantastic team with a huge amount of experience in this area of dealing with large-scale data sets and presenting them. It also has a lot of capabilities to continue to look at third-party data. Often it's not your Jira software data alone that helps with sprint planning when we need to look at source data and data from other providers as well. So really excited about the space and the team that's joined us there in Chartio and lots of potential, a huge amount of work that we're already doing there, but future looks great.