Stop guessing, Start measuring
Got Agile Teams? Great!Fresh challenges appear!
To do
In progress
Done
Do you know if you are more productive with Agile Teams?
Does your software meet all your service levels? (i.e.: availability, latency, security)
Is talent retained in the organisation? (Churn)
Are changes less expensive and are teams working on ‘the right things‘?
What’s next?
Enhance Developers collaboration with (Cloud) Operations
±$50 per hour of time spent on PMO scripts
Using DevOps make Developers collaborate better with (Cloud) Operations team, shorten the cycle time, and create a culture of trust and high performance.
Reduce operational overhead cost and toil
Agile Analytics can help reduce operational overhead and toil up to 50%! By managing Service Level Objectives and SRE Practices, you’ll quickly see how easy that is.
Measure if you are faster to market
With $20 - $40 per line of code and a typical branch size of 100 changes: abandoning a branch costs between $2000 and $4000 per branch
Using DORA Metrics, measure if you are faster to market. Find out how productive your teams are and use the right Engineering Metrics to measure your impact.
Bye, Bye manual Work Classification
A manual work classification run costs 500-1500 euro a piece (or 5k+ for automation)
No more spreadsheets or manual work classification processes to see what teams are working on. Agile Analytics completely automates this process using Sprint Insights.
Who Agile Analytics is for
Streamline Project Delivery with Targeted Insights
Agile Analytics empowers Project Managers to optimize project tracking and execution by using key metrics like lead time and average cycle time to compare planned vs. delivered work. Steer your projects toward timely and successful completion!
Lead Time. Understand the time it takes from project inception to delivery. Agile Analytics helps you track and reduce lead times, enabling faster time-to-market.
Average Cycle Time. Measure and manage the average time your team spends on various stages of project workflows, which is crucial for identifying bottlenecks.
Planned vs. Delivered. Compare what was planned versus what was actually delivered in a given time frame. Assess the accuracy of your project forecasts and the reliability of your delivery pipeline.
What Agile Analytics does
Product Management Teams
Development Teams
Operational Teams
Sounds awesome, but isn’t <tool X> doing this?
For Project Managers
Agile Analytics provides several unique propositions that are unavailable if your organisation only uses Jira or Azure DevOps to manage projects and ensure their success.
Sprint Insights for fully automatic work classification. Tools like Jira and Azure DevOps do not provide work classification distinction in their analytics, which can lead project managers to be uninformed about resource usage and risks in planning.
Hours A.I. for Detailed Labor Analytics. Advanced analytics on who spent what hours on specific topics. Other tools may track time, but the insights offered by Agile Analytics are based on actual data-driven evidence.
Try it out for free
Experience full access to Agile Analytics with our 60 days free trial. Including assistance in onboarding. No creditcard required.
Frequently Asked Questions about our platform
What is DevOps?
DevOps is a set of practices that combines software development (Dev) and IT operations (Ops). It aims to shorten the systems development life cycle and provide continuous delivery with high software quality. DevOps emphasizes collaboration, automation, and monitoring throughout the software development and operation processes.
How can your Agile Analytics platform do what Jira or Azure DevOps cannot?
That’s easy: all major vendors have incentives to keep you inside their tool. We don’t believe in walled gardens or silos. That’s why we created Agile Analytics to connect all critical data sources. This allows you to implement DevOps and Site Reliability Engineering easily.
What tools can I connect to Agile Analytics?
We support all major vendors for Cloud and Development tools like AWS, GCP, Azure, Prometheus, Datadog, Elasticsearch, Atlassian Jira, Microsoft Azure DevOps, Gitlab, Bitbucket, Github, Slack and Microsoft Teams.
What are SRE and Toil?
Site Reliability Engineering (SRE) is a discipline that incorporates aspects of software engineering and applies them to infrastructure and operations problems. The goal is to create scalable and highly reliable software systems. SRE focuses on automating and optimizing processes, managing incidents, and establishing clear metrics and Service Level Objectives (SLOs) to ensure performance aligns with business requirements. Toil, in the context of SRE, refers to the repetitive, mundane, manual tasks that are necessary for the day-to-day maintenance of a system but do not add value in the long term. SRE seeks to minimize toil by automating these tasks wherever possible, thus allowing engineers to focus more on creative problem-solving and innovations that enhance system reliability and efficiency.
What is a Service Level Objective (SLO)?
A Service Level Objective (SLO) is a crucial component of a service level agreement (SLA) that defines the level of service a customer expects from a service provider. It outlines specific performance, availability, and reliability metrics the service provider commits to meeting.
Error Budgets are closely tied to SLOs in reliability engineering and service management. An Error Budget represents the acceptable downtime or errors in a service within a given period, as defined by the SLO. It allows teams to balance innovation and reliability by setting thresholds for acceptable service disruptions. When errors or downtime exceed the budget, it may trigger a review or adjustment of processes to improve reliability.
What are Engineering Metrics?
Engineering metrics are quantitative measures that provide insights into the processes and effectiveness of engineering teams. These metrics help managers and leaders gauge productivity, quality, efficiency, and the overall health of software development processes. Agile Analytics leverages these metrics to optimize team performance, improve project outcomes, and foster a better understanding of team dynamics.
Can Agile Analytics really reduce Toil (Operations overhead) by up to 50%?!
Yes, Agile Analytics can indeed reduce toil (operations overhead) by up to 50% by implementing Service Level Objectives (SLOs) and Error Budgets and managing these effectively. By leveraging Agile Analytics, teams can set precise, data-driven SLOs that align closely with business objectives and user expectations. This structured approach ensures that all team members are focused on maintaining these objectives, which enhances operational efficiency. Additionally, managing Error Budgets through Agile Analytics allows teams to balance the need for innovation against the imperative for system stability, minimizing unnecessary or reactive work. This proactive management cuts down on toil and encourages a more collaborative and accountable framework within Agile teams, leading to significant reductions in operational overhead
Do you have more questions?
Tools Ecosystem
Agile Analytics integrates all monitoring and management systems to provide clear and 360-degree insights. These are the integrations used by our customers:
Agile Analytics reduced operational toil by 40%. Implementing Service Level Objectives and DevOps showed us where to place focus: Feature development or non-functional aspects.
Implementing DevOps?
Implementing DevOps requires linking the support systems to bring the ‘Dev’ to the ‘Ops’ and vice versa.
Find out how to set this up in 30 minutes yourselves.