Generative AI use cases for analytics and insights
The analytics and insights capability helps convert vast amounts of data into actionable insights that drive decision making and continuous improvement. By using generative AI, this capability processes data from various sources, including code repositories, project management tools, and team collaboration platforms, to provide a holistic view of the development process and team productivity. Generative AI goes beyond traditional metrics in order to offer predictive and prescriptive analytics. It can forecast potential issues and suggest targeted improvements. For instance, it can analyze patterns in code commits, bug resolution rates, and feature delivery velocity in order to identify high-performing teams, pinpoint bottlenecks, and suggest process optimizations. Moreover, it can provide insights into team dynamics and individual performance. These insights help leaders make data-driven decisions about workload distribution, training needs, and team composition. By presenting these insights through interactive dashboards, the capability empowers stakeholders at all levels to make informed decisions, optimize processes, and continuously enhance team productivity, which leads to faster delivery of high-quality software.
The following table shows analytics use cases that you can enhance with generative AI and the persona responsible for those use cases.
| Use case | Persona |
|---|---|
| Monitor individual and team productivity | Development manager |
| Analyze productivity trends to detect potential burnout so that you can take proactive measures to maintain team well-being and productivity | Development manager |
| Track how often code changes are deployed to production to gauge the speed and agility of the development process | Product manager |
| Analyze deployment frequency data to identify periods of low deployment activity that might indicate process inefficiencies or resource constraints | Product manager |
| Measure the time between code commit to deployment in order to identify opportunities to streamline the development and deployment processes | Development manager |
| Track the percentage of deployments that result in failures that require immediate remediation in order to assess the reliability of the release process | Site reliability engineer |
| Use change failure rate metrics to identify areas of code that frequently cause issues in order to guide targeted refactoring and testing efforts | Software developer |
| Monitor how long it takes to restore service after an outage or incident so that you can reduce downtime and improve the overall system resilience | Site reliability engineer |
| Analyze trends in restoration times to enhance incident response processes and drive faster recovery from system failures | DevOps engineer |
| Create a customized dashboard that aggregates key metrics, such as deployment frequency, lead time, and change failure rate, in order to provide a comprehensive view of development and operational health | Product manager |
| Create dashboards that are tailored to the needs of different teams in order to provide focused insights into their specific areas of responsibility, such as development, operations, or business | Product manager |
| Track business key performance indicators (KPIs), such as revenue impact, customer satisfaction, and market share, in order to align development efforts with broader business objectives | Product manager |
| Analyze the impact of new features on business KPIs to assess their success and guide future product development | Business analyst |
| Monitor code quality metrics, such as code complexity, test coverage, and bug density, in order to make sure that the codebase remains maintainable and secure | Software developer |
| Identify areas of the codebase that require refactoring in order to drive long-term sustainability and reduce technical debt | Solutions architect |
