Search Results

This article is written in collaboration with 55 Degrees Partner, Spectrume Group UK . .   Recognised by Atlassian as a top-tier Platinum Solution Partner, with local teams in London and Cardiff. Spectrume Group UK make Atlassian tools work harder for your business, from licences and migrations to training and ongoing support. Setting the Scene Every team has felt it: ambitious deadlines, shifting priorities, and the pressure to deliver more with less. So what happens? Leaders and teams - keen to please, eager to say “yes” - commit to more than they realistically can. It’s not a flaw, it’s human nature.  Nobody wants to be the blocker, the one who says “no.” Optimism feels good in the moment, but without accurate forecasting it quietly becomes a trap. Work piles up, deadlines slip, stress levels rise, and trust begins to erode. That’s the Cycle of Doom : not caused by laziness or poor intent, but by teams trying their best without the data to back their promises. Breaking the Cycle Project managers, Scrum Masters, and Agile delivery leads know that accurate forecasting is critical to breaking this vicious loop of missed deadlines and lost trust. This is where 55 Degrees’ ActionableAgile® Analytics  changes the game. ActionableAgile® Analytics: Forecasting That’s Based on Reality Unlike Jira’s native reports , which are limited and often misleading, ActionableAgile uses your team’s historical delivery data to build probabilistic forecasts . Instead of pulling numbers out of thin air, the app runs thousands of simulations to show the most likely outcomes. You don’t just get one date, you get a range with confidence levels (e.g. 85% chance we’ll be done in 10 days ). Suddenly, planning stops being a guessing game and starts becoming credible in front of stakeholders. The app’s other insights include: Cycle Time Scatterplots : revealing how long work actually takes so you can set realistic Service Level Expectations. Aging Work-in-Progress charts : instantly flagging items that have been in progress too long and are likely causing delays. Flow Metrics dashboards : turning Jira data into clear, actionable insight for smarter planning. In short, ActionableAgile acts like a “virtual agile coach”, constantly showing where your process can improve and how likely you are to hit your targets. Why This Matters Most teams know the Cycle of Doom all too well. You promise a date, miss it, and suddenly planning meetings feel more like courtroom trials than collaborative sessions. By grounding commitments in probability-based forecasts, conversations shift: “We’re 95% confident this will be done in 10 days - and here’s the data to show it.” That kind of confidence is contagious. Stakeholders trust the numbers, teams stop firefighting, and delivery becomes predictable. Instead of a Cycle of Doom, you create a Cycle of Confidence . Where Spectrum UK Comes In ActionableAgile is a powerful tool - but tools only create value when they’re implemented and adopted well. That’s where Spectrum UK  adds impact. Configuration & Onboarding:  We integrate ActionableAgile into your Jira environment, tailoring dashboards and workflows to your reality. Agile Coaching:  We help teams shift from juggling work to finishing what matters, adopting Kanban-style practices that make the data meaningful. Project Build & Support:  From setup to ongoing optimisation, we make sure forecasting becomes actionable; a tool for clarity, not guesswork. Together, ActionableAgile + Spectrum UK  take forecasting from theory to daily practice. The Proof: When Tech Meets Expert Guidance When data-driven forecasting and expert implementation come together, the results are transformative: Predictability Becomes Real ActionableAgile : Wireless Logic  doubled sprint planning accuracy, improving stakeholder trust through predictability. Spectrum : SmartStream trusted us with a high-stakes Cloud migration of 400,000+ issues and 2,500 users, delivered on time, within 3 months, no downtime. Together: leaders can commit with confidence, and teams deliver without fear of overpromising. Complexity Simplified ActionableAgile : KFC  eliminated messy spreadsheets, absorbed a 36% surge in incoming work and boosted output by 16%. Spectrum : Studi merged multiple Jira instances into one streamlined Cloud platform, reducing costs and improving governance. Together: no more waste or duplication. Just a single, trusted system that everyone can rally behind. Faster Time-to-Value ActionableAgile : Sanofi  slashed time to production from 3 months to just 12 days - All within 3 months. Spectrum : Decathlon migrated 3,000+ Jira licenses to Cloud in 13 sprints - unlocking instant access to new features. Together: agility without disruption, smarter, faster delivery at scale. Confidence at Scale ActionableAgile : BNP Paribas  scaled probabilistic forecasting to 150+ teams, creating a common language for performance. Spectrum : Our fixed-price migrations for global enterprises proved that even the most complex transformations can be predictable and secure. Together: scalable change, without the chaos. Human-Centred Outcomes ActionableAgile : Teams build healthier habits - focusing on finishing work, not just starting more. Spectrum : Clients like Studi saw simplified workflows, improved adoption, and happier users after governance was tightened. Together: it’s not just about better systems, it’s about calmer, more confident teams. The Outcome With this combined approach, you don’t just hope  projects succeed, you plan and deliver with confidence. Leaders stop guessing. Teams stop overcommitting. Organisations finally break free from the Cycle of Doom. Want to see what this could look like for your team? Spectrum UK are offering a FREE 15-30 minute consultation  to assess your current setup, explore your goals, and identify the fastest path to improvement. No commitment. No pressure. Just a clear picture of where you are now, where you want to be, and how we can help you get there. Stop the Cycle of Doom with Spectrum UK + 55 Degrees. Ready to break free from the Cycle of Doom? This guide walks you through the types of forecasting traps your team might be guilty of and guidance on how to overcome them. Wouldn't it be nice to know you can deliver - not just hope?

As you migrate to Jira Cloud and bring ActionableAgile® Analytics along, our goal is to help you minimize downtime, preserve functionality, and make the most of everything Cloud has to offer. With Atlassian officially announcing the end-of-life for Jira Data Center, many teams are beginning to prepare their move to Cloud. While change can feel daunting, moving to Jira Cloud with ActionableAgile® Analytics gives you access to faster innovation, better integrations, and expanded automation capabilities, helping your teams work smarter and deliver more predictably. Atlassian’s Shift to Cloud: What You Need to Know Atlassian has announced that it will be  shifting its full focus to Cloud , gradually phasing out Data Center (DC) products until they reach  end of life on March 28, 2029 . We know many of you still rely heavily on Data Center today, and we’ve been following this transition closely. Here’s a breakdown of Atlassian’s current timeline and what it means for you: March 30, 2026  – Sales of new Data Center apps and subscriptions will end for new customers. March 30, 2028  – Last date for existing customers to purchase new Data Center licenses, Marketplace apps, or license expansions. March 28, 2029  – End of life for Data Center. All DC licenses and Marketplace app licenses will expire and become read-only. Until then, Atlassian will continue to provide  technical support, critical security bug fixes, and connectors  between Data Center and Cloud. After March 28, 2029, however, Data Center products and apps will no longer receive support or fixes. Renewals will be prorated to end on that date, and while Atlassian has mentioned that extended maintenance may be available, it would come at an additional cost and only in exceptional cases. What this means for you If your organization is still running on Data Center, you have time, but the countdown has officially begun. The earlier you begin preparing for Cloud, the smoother your transition will be. That’s why we’re focused on making sure we support you every step of the way—from planning and testing to migration and adoption. Why Moving to Jira Cloud Benefits Your Team When you migrate to Jira Cloud with ActionableAgile® Analytics, you’re not just preserving the analytics you rely on—you’re unlocking more opportunities for growth and efficiency. Some of the biggest benefits include: Faster access to new features  – Cloud customers always get the latest updates first, so you’ll benefit from enhancements without delay. Future-ready improvements  – Native integrations with Jira Cloud platform features give you more seamless workflows. Expanded automation and data handling  – Automate more of your processes and leverage smarter ways to manage your data. In short: moving to Cloud doesn’t just help you keep up—it helps you stay ahead Jira Cloud vs. Jira Data Center: What It Means for ActionableAgile® Analytics Area Moving to Jira Cloud Staying on Jira Data Center Feature Updates Get new features first, faster release cycles Limited updates, some new features won't be delivered to DC. Integrations Native integrations with Jira Cloud platform features for smoother workflows No new Atlassian-native integrations planned, limited ecosystem growth Automation & Data Handling Expanded automation options and improved data handling capabilities Current automation capabilities only, no major enhancements expected Support & Roadmap Atlassian is investing heavily in Cloud, with long-term improvements guaranteed Support continues until Atlassian's DC end-of-life, but innovation slows How We’ll Support You Migration can feel daunting, but you don’t have to do it alone. You still have time, and starting early will help you avoid a last-minute crunch. The good news is ActionableAgile® Analytics on Cloud uses the same analysis model as Data Center, so your core data carries over seamlessly. Here’s how we’ll support you along the way: Ongoing updates – We’ll keep you informed as new features and support resources become available. Practical resources – A step-by-step migration guide is on the way to make planning easier and more predictable. Dedicated support – As a valued Data Center customer, you now have access to our Customer Success Program, designed to give you personalized guidance throughout your journey. You can connect with our Head of Customer Experience, Margaux Fiche, at margaux@55degrees.se  to start planning your path and explore how we can best support your transition. FAQs about migrating to Cloud with ActionableAgile® Analytics Here are answers to some of the most common questions we hear from teams preparing for migration. Will ActionableAgile® Analytics continue to support Jira Data Center? Yes. We will continue supporting Jira Data Center customers throughout Atlassian’s end-of-life timeline. Core updates such as charts, insights, and calculations will still be delivered. Why won’t all new features come to Data Center? Some features require extensive development and testing that are specific to the Data Center platform. With Atlassian planning to sunset Jira DC, it’s important for us to focus resources on updates that bring the greatest long-term value to customers across platforms. Which types of features are affected? Platform-dependent updates — such as native Jira dashboard gadgets or certain integrations with Jira internals — may not be delivered to Data Center. Instead, we’re prioritizing improvements to the ActionableAgile® Analytics experience itself, which will benefit both Data Center and Cloud users. What new improvements can I expect on Data Center? You will still see enhancements including: A new app landing page Data set views for easier navigation Consistent chart templates Expanded chart insights What if my team plans to move to Jira Cloud later? We will be ready to support you with a smooth transition when that time comes. ActionableAgile® Analytics for Jira Cloud includes the same core capabilities and ongoing innovation. Migration Planning Checklist To help you plan your transition, here’s a simple checklist to keep in mind: Step What to do Why it Matters Assess Review your current use of ActionableAgile® Analytics on Jira Data Center. Identify key datasets, saved configurations, and most-used charts. Ensures nothing critical is missed during migration. Plan Map out which features you rely on most. Check the FAQ for features that may differ between DC and Cloud. Helps set priorities and expectations. Trial Run Install ActionableAgile® Analytics for Jira Cloud in a test site. Import sample data and validate key reports. Gives your team a safe way to preview the Cloud experience. Prepare Data Work with your Jira admins to plan the DC → Cloud migration of Jira itself. Document how ActionableAgile® Analytics datasets will be recreated in Cloud. Ensures data continuity and minimizes disruption. Migrate Execute the Jira migration. Reconfigure datasets and saved views in ActionableAgile® Analytics for Cloud. Gets your production environment ready. Review & Validate Compare charts and insights between old DC reports and new Cloud reports. Confirm accuracy. Builds confidence and user trust in the new setup. Train & Adopt Introduce users to any new features available in Cloud. Provide quick guides or training sessions. Boosts adoption and helps teams take advantage of improvements. Migrating to Jira Cloud may feel like a big step, but with early planning and the right support, it's an opportunity to get the best out of ActionableAgile® Analytics. On cloud, you'll always have faster access to new features, deeper integrations with Atlassian platform, and expanded automation to make your workflows more efficient. We'll be with you every step of the way, helping you maintain continuity, unlock new value, and move forward with confidence. Whether you're ready to begin your move to Jira Cloud or just have questions along the way, we're here to support you. And if you're planning to move off Atlassian, let us know—we can help if you're moving to Azure or learn more about your next steps to better support you.  Reach out to us at support@55degrees.se

It's a feature you've been waiting for: ActionableAgile® Analytics charts in Jira Dashboards. 🎉 We’re excited to announce the release of  native Jira Cloud Dashboard Gadgets for ActionableAgile® Analytics — bringing your most valuable insights directly to the place where your teams already work:  Jira Cloud Dashboards. This launch is a game-changer for teams, leaders, and stakeholders who want instant visibility into flow metrics without switching tools or exporting data. Why This Matters Until now, sharing ActionableAgile® Analytics insights with a wider audience has not always been seamless. Teams often had to export charts or guide non-users into the app, which worked but added friction. With Jira Cloud dashboard gadgets, that barrier disappears. You can now: Share charts easily  with team members and stakeholders who don’t actively use ActionableAgile® Analytics. See live updates  whenever the dashboard is loaded — no need to export or refresh manually. Bring agile insights into context  by displaying them alongside other Jira data your team already tracks. In short, this feature brings  flow metrics visibility  to  everyone who needs it,  not just direct users. What’s Included in the First Release We’ve started with the  four most essential flow metric charts  — the ones you told us you rely on the most. Cycle Time Scatterplot Visualize how long work items take to complete. Spot trends, outliers, and opportunities for improvement. Aging Work In Progress Highlight work items that have been in progress the longest. Take action before aging tasks become blockers. WIP Run Chart Track how much work is in progress over time. Identify bottlenecks and manage workload more effectively. Throughput Run Chart See how many items your team completes per time period. Monitor delivery pace and forecast future performance. Together, these gadgets deliver a clear, accessible view of your team’s flow and predictability, right from the Jira dashboard. How It Works Adding these gadgets is quick and intuitive: Go to your Jira Dashboard. Search for  “ActionableAgile”  in the gadget search. Add the chart you’d like to display. In the configuration, choose the dataset and view settings you want. That’s it! 🚀 Your chart will now appear on your dashboard, updating automatically whenever you load the page. 💡  Note:  You can’t adjust full chart controls from the dashboard view — these are set by the chosen view in your dataset. To make sure your dashboard gadgets always look the way you want, just create and save a dashboard view in your dataset. You'll still be able to interact with some of the data displayed. Permissions and Access Editors  (those with permission to edit a dashboard) can add and configure ActionableAgile® Analytics gadgets. Viewers  can see the charts but cannot modify them. This ensures dashboards stay consistent while still giving everyone access to the insights they need. A Few Things to Know This release is available for  Jira Cloud only. Jira Data Center is not supported. We’re starting with four core charts, with more to come in future updates. Beyond new charts, we also plan to add insights from the chart data - giving you even deeper visibility and guidance right inside Jira. Why We’re Excited (And You Should Be Too) The release of Jira Cloud Dashboard Gadgets for ActionableAgile® Analytics is more than just a feature update — it’s a major step in making flow metrics a natural part of your team’s daily rhythm. By embedding these charts where your teams already collaborate, we’re eliminating barriers, strengthening transparency, and making it easier than ever to build predictability into your workflow. This has been one of our  most anticipated customer requests,  and we’re thrilled to finally deliver it. We can’t wait to see how your teams put it to use. If you’re on  Jira Cloud , the gadgets are available now. Head to your Jira Dashboard, search  ActionableAgile , and start adding your charts. Still Have Questions? We Can Help. We want to make sure you get the most out of this exciting new feature. Here are a few ways to get support and dive deeper: 📘 Additional Resources: Check out our  documentation  for step-by-step guidance on how to set up and configure your Jira Dashboard Gadgets. 💬 Chat Support: Have a question or run into a challenge? Our support team is ready to help with anything you need. support@55degree.se 🌍 Community: Join the conversation! Connect with other ActionableAgile® Analytics users, share your ideas, and learn from the experiences of teams around the world in our   community space.

We’re excited to share that ActionableAgile® Analytics for Jira Cloud has now been migrated to Forge Remote , Atlassian’s next-generation platform for Marketplace apps. While the change happens behind the scenes, it brings meaningful improvements for you: Improved security & compliance: Your data is now processed using Atlassian’s zero-trust Forge infrastructure, with automatic authentication and context handling. This ensures stronger safeguards without requiring extra setup on your part. Better reliability & performance: Forge’s serverless runtime scales automatically with usage, meaning fewer bottlenecks, faster responses, and more consistent uptime. Future-proof foundation: Atlassian has announced the deprecation of Connect. By moving now, we’re ahead of the curve, ensuring your app continues to run without disruption and remains compatible with Atlassian’s evolving cloud ecosystem. Faster innovation for you: Forge Remote gives us the best of both worlds: the security and simplicity of Forge, plus the flexibility of our own backend services. This means we can deliver new features and improvements more quickly. Most users are already upgraded automatically. If you're still using the old Connect version, simply press the upgrade button to move over.

When I first became a manager (more years ago than I care to admit), I inherited a team full of smart, dedicated people. Everyone was working hard. Meetings were full of good ideas. But we still weren’t delivering. Deadlines slipped. Priorities shifted. Work dragged on far longer than expected. We weren’t lazy. We weren’t disorganized. But we also couldn’t explain why things weren’t getting finished. And that made it really hard to improve. Then someone introduced me to flow metrics. We started measuring how work moved through our process — how much we got done, how long things were taking, and how much work we had in flight. That visibility changed everything. We could see bottlenecks. We could spot aging work before it became a problem. And slowly but surely, our delivery became more predictable and less stressful. What Are Flow Metrics? Flow metrics help you understand how work moves through your system. They’re simple, practical measurements that reveal where work is flowing smoothly and where it’s getting stuck. No crystal balls required. The four core flow metrics are: Throughput : How many work items you complete in a given time period WIP (Work in Progress) : How many items you’re actively working on Work Item Age : How long your current work items have been in progress Cycle Time : How long it takes to complete work from start to finish Each metric tells you something different, and together they give you a clearer, more actionable view of how your team works. Who Can Use Flow Metrics (and Why)? Flow metrics aren’t just for specialists or process geeks. If your work depends on delivering value - and most work does - these metrics can help. Here are just a few roles that can benefit: Team Leads & Scrum Masters: Spot bottlenecks, balance workloads, and support a healthier, more sustainable pace of delivery. Product Managers: Improve forecasting, prioritize realistically, and align delivery expectations with stakeholders. Engineers & Designers: Get insight into how often you’re being interrupted or asked to multitask, and how that affects delivery. Executives & Stakeholders: Understand delivery trends and risks without needing daily status updates. Agile Coaches & Change Agents: Use data to drive conversations, measure improvements, and support teams more effectively. This list isn’t exhaustive. If your role touches delivery in any way, from strategy to implementation and onwards to support, flow metrics can give you valuable insight into how to work smarter, not just harder. Ready to Learn More? If you want to dig deeper into each of the four core metrics, we’ve got you covered: What is Throughput? What is WIP? What is Work Item Age? What is Cycle Time? Understanding your team’s flow is one of the most effective ways to reduce chaos, increase predictability, and create a better experience for everyone involved in delivering value. Want to Go From Insight to Action? Understanding your flow is the first step. Taking action on it is where the real magic happens. ActionableAgile® Analytics  helps you visualize, analyze, and improve your flow using the very same metrics you’ve just read about. Whether you're looking to increase predictability, reduce lead times, or simply make your process more transparent, our tool can help. Try it for yourself or get in touch  to learn how we can support your team.

Adopting flow metrics can feel like trying to convince your team to give up coffee......difficult and a little dangerous. But it doesn't have to be. In our recent webinar with ProKanban.org , we tackled the big questions: How do we overcome resistance? How do we make flow metrics meaningful to teams? And how do we introduce them without causing mass panic? Here are the key takeaways to help you build buy-in without breaking trust. Missed the webinar? Watch it on Youtube. Bringing Flow Metrics to Your Team What’s the best way to introduce flow metrics to a team that’s never used them? You might worry that if you suddenly start talking about cycle times and throughput, you’ll get blank stares – and you’re probably right. Rita suggests a gradual, data-informed approach to get buy-in: “If you come straight into a stand-up and say, ‘ from now on we’re going to use flow metrics ,’ you’ll get probably blank stares… People will wonder what’s happening.”   Instead, begin collecting some data in parallel to your current process. Start measuring how long your work is actually taking and how much you’re delivering. “I started to look at the metrics… I thought, oh, what’s happening here? We have a lot of variation,”  Rita says, describing her own journey. She brought these findings into a retrospective, using real data from the last few weeks to reflect on how work was flowing. Poll results from our live webinar The key is to introduce flow metrics gradually as helpful information, not as a sudden mandate. You might start by examining recent cycle times and throughput in retrospectives or sprint reviews (looking at historical data is a lagging  measure). As the team becomes more comfortable, you can begin using leading  indicators like work item age during daily stand-ups. For example, some teams add an aging marker on cards in their Jira or Kanban board to highlight items that have been in progress longer than a few days. How to deal with the Story Points discussion Many Scrum teams are used to estimating in story points (with tools like planning poker or Fibonacci sequences) and using velocity for planning. A common question is how flow metrics relate to or replace these practices. The experience shared by our experts is that you don’t need to estimate in points to have useful predictions, in fact, your real historical data is usually more accurate than a guess. That said, do not  abandon the valuable discussions that happen during refinement and planning. The goal is to shift those conversations toward understanding the work and risks, rather than debating estimates. Rita went into some detail about this - “ Absolutely do not drop conversations about risks and alignment and what needs to be done. But to sit there and spend time on ‘how long do you think this will take?’ – that’s where we dropped it .”  In her team, they stopped arbitrary story point estimates, but they still collaboratively discuss each upcoming story to make sure they understand it and that it’s small enough. Once the team is clear on the what and why of a story, they start the work. How long it actually takes (the cycle time) is then fed back into their metrics. Over time, teams often find that story point estimates don’t add much value beyond what their throughput  and cycle time  data already tell them. Colleen recommends doing refinement as close as possible to when the work will start, to incorporate the latest information. Planning far in advance with estimates that might change can be wasteful. Instead, teams can forecast using flow metrics: for example, using throughput to project how many stories they can likely complete in the next iteration, or using historical cycle time percentiles to answer “ when might this single item be done ?” (often via Service Level Expectations, like “ 85% of our stories get done in ≤8 days ”). These forecasts are based on reality which is the actual variability of past performance, rather than the optimistic assumptions that often creep into estimates. Smaller Batches, Faster Flow Another aspect of improving flow is tackling the batch size of work. Intuitively, one small item will move through the system faster than a large batch of work. Colleen emphasizes this when addressing how to explain the benefit of moving one item faster versus a group of items: “One item is always going to be faster, obviously, than a group of items… The bigger [the batch], the slower it tends to move.”  Large projects or epics that comprise many pieces will inevitably take longer and face more complexity than a single, well-defined story. The takeaway: whenever possible, break work into smaller independent chunks. Each chunk (perhaps a user story) should ideally deliver some value on its own. By keeping batch size small, you can deliver pieces incrementally and get feedback sooner. If you have an 8–12 week epic, try to slice it into smaller features or stories that can be completed and released continuously, rather than waiting 3 months for a big bang. Smaller batches moving through the system quickly will improve overall throughput and help maintain that consistency we discussed above. Poll results from our live webinar Common Challenges and Misconceptions “Our data is messy or not good enough.” Many teams worry that their tracking data (e.g. in Jira) is incomplete or inconsistent, and thus hesitate to use it. In reality, your data is what it is  – and that’s okay. “The data you have is reflective of the system you have,”  Colleen notes, meaning it captures all the variability of your current process. That variability is precisely why forecasts often feel hard! Don’t wait for perfect data; instead, start using what you have to set a baseline. Include all those outliers and long cycle times in your analysis – they’re telling you something important about your process. Over time, as your team improves (maybe by slicing work smaller or reducing dependencies), your data will naturally get “cleaner” (more consistent). But even if you have a wide range of outcomes, techniques like Monte Carlo simulations can ingest that and give a probability-based forecast. In short, “If you have data, it’s good data.”  Use it to start the conversation and drive change. “Won’t management use these metrics against us?” Perhaps the biggest fear is that making all this data transparent will turn into a surveillance mechanism or a blame game. This concern is real – it’s crucial to establish psychological safety and an improvement-oriented culture when introducing flow metrics. Colleen emphasizes that metrics should be a tool for the team first and foremost, not a stick for leaders to poke at individuals. The goal of visualizing flow metrics is to help the team make better decisions in real time (like swarming aging work or adjusting WIP), and to experiment with changes for improvement. It’s not to compare Team A vs Team B, or Developer X vs Developer Y. Make sure leadership understands this too. Ideally, managers should look at metrics like cycle time and throughput as indicators of process health, not employee effort. If a number is outside the expected range, the question to ask is “ What’s affecting our workflow here? ” rather than “ Who is to blame? ” Want to Learn More? If you're ready to keep going, check out: E-Book - Getting started with flow metrics ProKanban.org Training & Certifications The Kanban Pocket Guide (a free resource worth bookmarking)

Work Item Age is the elapsed time since the work item started. Work Item Age is one of four key flow metrics along with Cycle Time , Throughput , and WIP (aka work in progress ). Work Item Age is, arguably, the most important flow metric because controlling age can result in significantly higher levels of predictability. In short, if you can only measure and manage one thing, make it Work Item Age. In simpler terms, Work Item Age is the elapsed time an open ticket has been waiting or working since it began. Every active item has an age, which grows day by day until the item is finished. Once the work is done, that item’s age stops accumulating (and essentially becomes its Cycle Time). How do you calculate Work Item Age? To begin calculating Work Item Age, you need to have a defined process with two specific points identified: Your Start Line – or the point in your process in which items are considered started Your Finish Line – or the point in your process in which items are considered complete When you have those points defined, you can identify your WIP as they will be the work items between those two points. Identifying WIP in your process You measure the age of each work item in your WIP using the following calculation: (Now – Start TU) + 1 TU stands for Time Unit. You can use any granularity you'd like: seconds, minutes, hours, days or more. Why do we add 1? The “+1” ensures that if something started this morning and it’s still in progress now, we count today as one day of age. Every new day an item remains unfinished, its age increases by 1. Importantly, Work Item Age includes all the waiting time, just like Cycle Time does. It doesn’t matter if a task has been sitting idle over the weekend or blocked by a dependency – that time still counts in its age. Work Item Age is the total elapsed time since the work item started If a story was started 5 days ago, its age is 5 days (assuming we measure in calendar days and add the inclusive day count). We calculate it similarly to Cycle Time, using today as the “end” since the work isn’t finished yet. For example, if a task moved to “In Progress” on October 1 and today is October 10, its Work Item Age would be (Oct 10 – Oct 1) + 1 = 10 days. Why Work Item Age Matters Work Item Age is arguably the most important flow metric for teams focused on improving their delivery . Why? Because it deals with the here and now  – it highlights work that is currently in progress and potentially at risk of taking too long. Cycle Time and Throughput are lagging indicators (they look at completed work), but Work Item Age is a leading indicator . It gives you an early warning when something is languishing. Age is by far the most crucial metric to track, since controlling age can dramatically improve your predictability. If you manage only one thing, manage the age of your work items. Aging Work In Progress Chart from ActionableAgile Analytics. Used for visualizing  Work Item Age. Each dot represents a work item in progress. Consequences of Aging Work Think of Work Item Age as a “staleness” gauge. The longer a task lingers unfinished, generally the harder it becomes to finish. Team members lose context, assumptions go stale, and inertia sets in. By tracking age, you can prevent tasks from quietly rotting on your board. It brings visibility to items that might otherwise be forgotten until it’s too late. Studies have shown that keeping Work Item Age low tends to result in lower overall Cycle Times and more reliable delivery forecasts. In other words, managing aging work directly improves your team’s speed and predictability. You’ll finish work faster and with fewer surprises at the end. There’s also a psychological aspect: a ticket that has been “In Progress” for 15 days when most finish in 5 is a big red flag. It prompts the team to ask, “Why is this taking so long? What’s blocking it?”  Those conversations are exactly what you need to unblock issues or decide if you should pull the plug or get help. Watch this quick expert take on Work Item Age. Getting Started Work Item Age is important to track for any team who wants to become more predictable. At its core, Work Item Age is a process improvement metric. When you see items aging more than expected, you can experiment with tactics to see if they help. There is no single fix but common tactics include limiting WIP, controlling work item size, reducing dependencies, and more. Tracking Work Item Age helps you spot stalled or slow-moving work before it becomes a problem. Use it during these key events to keep your flow healthy and your delivery on track: During daily scrums / stand-ups to highlight items that are aging or stalled - transition to ask “what’s not moving?” instead of just “what are you working on?” In retrospectives, review items that aged unusually long and why During SAFe team syncs or ART syncs to flag aging work at risk of breaching SLAs or SLEs When preparing for release readiness to identify aging features or stories still incomplete In incident response workflows, to monitor if critical bug fixes or support tickets are aging During risk reviews or dependency checks in cross-team planning Whenever an item sits in one status longer than your team’s typical Cycle Time Once you manage Work Item Age, your Cycle Time data should stabilize and make forecasting easier! Ready to get started with flow metrics? This guide walks you through what to measure, how to get your team aligned, and how to build a case for change that your stakeholders will actually care about. It’s time to shift from intuition-driven to insight-driven delivery. If you’re looking for a tool to help you track your flow metrics and improve your process, try out ActionableAgile for free. Don’t forget to reach out if you’re interested in joining our customer success program!

Throughput is the count of work items your team completes per unit of time. In other words, it measures how much work gets done over a period. You might have a Throughput of 2 per day, 10 per week, or even 17 per sprint. Whatever your preferred time unit, this flow metric helps you understand how quickly you finish work . It’s a simple metric, but very powerful for understanding your team’s delivery capacity and for planning. Throughput is one of the four essential Kanban flow metrics (with Cycle Time , WIP, and Work Item Age) and gives insight into how quickly value is flowing out of your process How do you calculate Throughput? Calculating Throughput is straightforward. There are two things you need to decide on: The time unit  – e.g. per week, per month, per iteration. Choose a timeframe that makes sense for your context. Teams often use weekly or biweekly Throughput, especially if they’re doing Scrum (per sprint) or just want a steady pulse measure. The finish line  – define what counts as “completed” (usually items that reach the “Done” column or have a status like Resolved/Closed). In the image below, work is considered started when it enters the In Progress  column on the board. It is considered finished when it enters the Done  column on the board. This team has defined the Done column as their finish line. So, any items that move into the Done column are counted as Throughput. Why should I care about Throughput? Looking at your Throughput allows us to analyze how consistently you deliver value. Consistency of throughput, and how it compares to the rate at which you start work, is one indicator of how stable your process is. Perhaps the most common use for the Throughput metric is providing forecasts for completing multiple work items. You can use Cycle Time to forecast for single items, but you need a rate metric like Throughput to provide forecasts for groups of work items. Throughput Run Chart chart from ActionableAgile Analytics within Jira How do you use Throughput to forecast? Traditionally, people use their Throughput to determine the average rate at which work is finished and then divide the total work by that average. However, forecasting based on averages will produce average results. Obviously, we don't suggest you do that. Fortunately, you can use a Monte Carlo simulation that can use your Throughput data to simulate probable outcomes based on the variation found there. It’s a much more accurate, not to mention risk-aware, way to deliver forecasts. Read more about Monte Carlo simulations and forecasting. Getting started Teams often start looking at Throughput around the same time they begin looking at Cycle Time , WIP , and WIP Age . Focusing on building stability in these key flow metrics is a good start. The more stable your basic metrics are, the fewer outliers your forecasts have to account for and the more your forecasts are perceived as acceptable and, most importantly, accurate. Watch this quick expert take on Throughput Ready to get started with flow metrics? This guide walks you through what to measure, how to get your team aligned, and how to build a case for change that your stakeholders will actually care about. It’s time to shift from intuition-driven to insight-driven delivery. Interested in tracking flow metrics like this one? Try out ActionableAgile for free and reach out if you’re interested in joining our customer success program!

Does your team have a lot of tasks going on at the same time?   WIP is an acronym for work in progress.  So, the WIP flow metric measures the total number of work items that have been started – but not yet finished – at any given point in time. WIP is one of four key flow metrics, along with Cycle Time , Throughput , and Work Item Age . In practical terms, it’s how many cards are “in progress” on your board right now. If you’ve pulled five user stories into development and none are done yet, your current WIP is 5. These flow metrics are interconnected – meaning, when one of these flow metrics changes significantly, you’ll see an impact on one or more of the others. Most of us have experienced this reality: the more you have in progress, the longer it takes to get each thing done. It makes sense. If we put all of our effort into one thing, that one thing will be finished faster than it would if we spread our time across multiple items. For more on this relationship, read about Little’s Law . The amazing news is that this makes WIP a leading indicator of future Cycle Time, and it means that controlling your WIP can be a tool in your journey to help you deliver quickly and predictably. Watch this quick expert take on Work in Progress (WIP) How do you calculate WIP? To begin calculating WIP, you need to have a defined process with two specific points identified: Your Start Line – or the point in your process in which items are considered started Your Finish Line – or the point in your process in which items are considered complete In the image below, work is considered started when it enters the In Progress column on the board. It is considered finished when it enters the Done column on the board. A defined process with clear start and finish points When you have those points defined, WIP is calculated as a simple count of work items between those two points. In the example above, we show a current WIP level of five (5). A count of cards between the two points tells us that current WIP is 5 Why should I care about WIP? People can only do one ACTIVE thing at a time. You might be able to listen to music while working or something like that but you can't actually type or say two different things at one time. We are like single processors that way. So, when we have more things "in progress" at one time, we are switching back and forth between them. This makes each one take a bit longer to do than if you focused on just one thing at a time, not starting another until the first is finished. While you may not be able to have only one thing in progress, you should understand the impact of higher WIP - in other words more task switching. Take a look at one of our past blog posts to see about a study we did with my team at the time regarding the unspoken cost of high WIP levels: Can you afford not to limit work in progress? Higher WIP = Higher Delivery Costs When too much work is in progress, each item takes longer to finish. Teams spend more time (and salary) per feature delivered. A recent BCG study   found over a third of software projects were delayed, and every delay postpones ROI. High WIP means you’re paying for people and resources longer before you get any value back. Slow Delivery = Lost Opportunity Piling up WIP slows your time to market. Customers get value late, which defers revenue and can miss market windows. This AWS blog  shares a great example on how a multitasking team delivered a mobile app 10 weeks later than necessary – missing a peak shopping season and delaying partner onboarding. Pushing too many things at once also increases the cost of delay. This is the economic cost of delivering value later rather than sooner. In short, the more items in progress, the longer customers wait (and the longer your company waits to earn back its investment). Getting started Teams often start looking at WIP around the same time they begin looking at Cycle Time , Throughput , and WIP Age . Looking at the impact of your WIP on the other key flow metrics will provide insight into the WIP limits you might want to experiment with for your process. As your experiment goes on, see what impact the WIP limit change had on your Cycle Time and Throughput. Repeat this process until you get a result that you’re pleased with! When using WIP in everyday work some examples can be seen below. Before starting a new sprint or pulling in more work to avoid overloading the team. In daily stand-ups if the team has many tasks in progress at once (flag high WIP). When throughput drops or cycle time climbs – signs that WIP might be too high. During retrospectives to see if too much work-in-progress caused delays. When an urgent item arrives, to decide if something else should pause before adding it. WIP Run Chart from ActionableAgile Analytics within Jira Ready to get started with flow metrics? This guide walks you through what to measure, how to get your team aligned, and how to build a case for change that your stakeholders will actually care about. It’s time to shift from intuition-driven to insight-driven delivery. Interested in tracking flow metrics like this one? Try out ActionableAgile for free and reach out if you’re interested in joining our customer success program!

Do you know how long it actually takes for your team to finish a task from start to finish?  Cycle Time measures the total elapsed time from when a work item begins to when it completes. This means that, depending on how you define start and finish for your context, you can measure the Cycle Time for a whole process or just a portion of it. How to Calculate Cycle Time Calculating Cycle Time is straightforward. First, define what “Start” and “Finish” mean for your process. For example, work might start when a ticket moves into an “In Progress” column and finish when it reaches “Done.” Once those points are clear, you can compute Cycle Time with a simple formula: Cycle Time = (Finsh Date - Start Date) +1 You might be asking, " But why do you add + 1? " - Fair enough. The +1 accounts for work that starts and finishes within the Cycle Time so no time is left out. For instance, if a task started on January 1 and finished on January 5, its Cycle Time would be 5 days (inclusive). We add that extra day so that an item started and finished the same day isn’t recorded as 0 days . You never have a zero-length Cycle Time; even a task completed within hours counts as a 1-day Cycle Time Remember, Cycle Time doesn’t stop when work pauses. If a task gets blocked or sits idle over a weekend, that idle time still counts in the Cycle Time. This makes conversations with stakeholders simpler, as calendar days are relatable to both internal and external audiences. Why Cycle Time Matters Cycle Time is one of the four basic flow metrics, along with Throughput , WIP , and Work Item Age . These four flow metrics are baselines metrics that give you some insight into the underlying flow health of a process. Cycle Time is a critical flow metric for understanding and improving your delivery speed. It directly answers the question: “How long does it take us to complete a work item?”  This is hugely important when stakeholders ask, “When will it be done?”   Tracking Cycle Time allows you to answer with data rather than guesse s. Lower (and consistent) Cycle Times mean you’re delivering value faster and more predictably to your customers. Measuring Your Predictability Looking at Cycle Times helps us understand how predictably we deliver individual work items. If your process generates a wider range of Cycle Time data now than it did in the past then, objectively, you could say that your process has become less predictable than it used to be. By looking at how long it took you to finish a given percentage of items historically, you can get an idea of how long it may take you to deliver an item in the future - assuming your process hasn't significantly changed. This is best seen on a Cycle Time Scatterplot. Each dot represents the Cycle Time of a given work item. Cycle Time Scatterplot chart from ActionableAgile Analytics within Jira Using Cycle Time for Forecasting One powerful use of Cycle Time data is forecasting single work items. Instead of relying on gut feel or arbitrary story point estimates, you can look at your past Cycle Times to predict how long similar work might take. A common technique is to determine a percentile from your historical data. For example, by looking at the above Cycle Time Scatterplot we can easy see that 95% of the completed stories had a Cycle Time of 23 days or less. You can then communicate something like, “ Based on our data, there’s a 95% chance we’ll finish an item like this within 23 days. ” This probabilistic forecast sets realistic expectations using evidence from your actual performance. Be careful not to just take a simple average of past Cycle Times, averages can be misleading if you ha ve outliers. One huge delay can skew an average upward, and it doesn’t reflect the variability or risk. It’s better to use percentile ranges (as mentioned above) or to employ simulation methods. Some teams use Monte Carlo simulations on their Cycle Time data to forecast completion dates. Monte Carlo simulation leverages the distribution of your Cycle Times to run many “what if” scenarios, providing a more reliable and risk-aware forecast than a single average. A great metric to start with Cycle Time is often the first flow metric that teams attempt and it is very easy to track — even by hand! All you need is to write down the start date and the end date of a work item and you can calculate cycle time. You can even make your own charts! Ready to get started with flow metrics? This guide walks you through what to measure, how to get your team aligned, and how to build a case for change that your stakeholders will actually care about. It’s time to shift from intuition-driven to insight-driven delivery.

Agile and DevOps teams rely on data to make decisions. Yet if you’re a Jira user, you’ve probably felt some pain points with Jira’s built-in reports. The charts and gadgets Jira provides are okay  for basic tracking, but are they enough to truly improve your team’s flow and predictability? Maybe not. That’s where ActionableAgile® Analytics comes in – it’s designed to give you deeper, actionable insights right inside Jira. In this post, we’ll compare Jira’s native reporting  features with ActionableAgile Analytics  side by side. We’ll see how each handles common needs like Cycle Time analysis, work-in-progress tracking, forecasting, and more.  The goal is to understand which tool actually helps you solve the problems slowing your team down, instead of just reporting them. Let’s dive in. The Basics: Jira Reports vs. ActionableAgile® Analytics Jira’s Built-In Reports:   Jira comes with a variety of out-of-the-box reports and dashboard gadgets (e.g. Control Chart, Cumulative Flow Diagram, Burndown Chart, etc.). These are convenient because they’re right there in Jira, showing live data with minimal setup.  If you need a quick view of sprint progress or how many issues were resolved vs. created, Jira’s default reports can do that. They’re also straightforward, you don’t need to be a data expert to use them. However, teams often hit limitations with Jira’s built-in reporting. The insights can be superficial . You might get an average Cycle Time, but you won’t know the spread or if you have outliers causing delays. There’s little customization  – you can’t add new types of charts or easily change what’s shown.  Cross-project reporting is limited too; most Jira gadgets focus on one project or board at a time. The bottom line is Jira’s native reports give you basic awareness, but often leave you with more questions than answers about your process. ActionableAgile® Analytics:   An Atlassian Marketplace app that lives inside Jira and was built specifically to analyze flow metrics  and improve predictability. It’s like giving Jira a supercharged analytics brain. With ActionableAgile Analytics, you get a suite of advanced charts Cycle Time Scatterplots, Aging Work-in-Progress charts, Histograms, Throughput trends, and more. These aren’t just for show; they let you see the reality of your workflow, including variation and outliers, which Jira’s reports often mask. ActionableAgile Analytics emphasizes statistical context .  For example, it can overlay percentile lines on your Cycle Time chart, so instead of just saying “average Cycle Time is 5 days,” you can see “85% of our items get done in 8 days or less.” That kind of insight helps you set realistic expectations (like SLA targets) and manage risks. Another big plus: ActionableAgile Analytics has built-in forecasting (Monte Carlo simulations) . You can literally forecast project completion dates or how many tasks you’ll get done by a certain date with a click – all based on your historical data.  Finally, ActionableAgile Analytics integrates smoothly, meaning you don’t have to export Jira data to use it. It pulls from Jira in real-time and updates the charts as your Jira issues update. The chart and the configuration you're looking at can also be saved and shared with colleagues to help everyone being on the same page (while respecting viewing permissions). It basically turns Jira from a tracking tool into a learning tool for your organization. Instead of just seeing what happened, you start to understand why  things happen and how to improve. Feature-by-Feature Comparison To make this concrete, let’s compare Jira vs. ActionableAgile Analytics on a few key reporting features that agile teams often care about: 1. Cycle Time Analysis (How long does work take?) Jira’s Control Chart:  Jira offers a Control Chart to visualize Cycle Time (the time it takes to complete an issue). It shows each completed issue as a dot and an average line. It’s useful for seeing overall trends. But it is quite limited for those seeking more.  The chart uses the average, which can be skewed by one or two really slow items. If one issue took 60 days but most take 5 days, that lone outlier will drag the average way up and paint an overly grim picture. Jira’s chart doesn’t easily let you filter those out or show percentile lines.  In short, Jira’s Control Chart gives you a hint at your Cycle Times but not the full story. You might still wonder, are we generally consistent or do we have a few huge outliers?  Jira won’t answer that well. Jira's out of the box Control Chart ActionableAgile Analytics’s Cycle Time Scatterplot:  The ActionableAgile Analytics scatterplot looks similar at first (dots over time), but it packs a lot more insight. It plots each item’s Cycle Time and can display percentile lines (50th, 85th, 95th percentiles, etc.). So instead of just one average, you can see, for example, “half our tasks finish in under 4 days, 85% finish in under 8 days, but a few went way up to 20+ days.” This immediately tells you about variability and risk.  If you have a big gap between your 85th and 95th percentile lines, that means occasional items take much longer – maybe those are special cases worth investigating.  ActionableAgile Analytics also lets you filter by issue type or tag. Want to see Cycle Time just for user stories vs. bugs? Click a filter and you can. You can even toggle which phases of your workflow to measure (maybe you only care about coding+review time, excluding backlog wait).  All of that is point-and-click in ActionableAgile Analytics. The result is you truly understand your delivery times. Teams can then have conversations like, “ Hey, 85% of our work gets done in under 8 days – that’s pretty good, but what’s up with these few that took 20+ days? Are they exceptions or a pattern we need to address? ”. Jira’s control chart alone rarely sparks that level of discussion because it doesn’t make the patterns obvious. 2. Managing Work in Progress (WIP) and Aging Work Jira (no native aging chart):  Jira doesn’t have a report to show how long each in-progress item has been open. You might use a dashboard filter to list issues “in progress > 10 days” or similar, but there’s no visual aging work-in-progress chart built into Jira Software.  This is a gap, especially for Kanban or flow-based teams. It means teams often miss early warnings that something is stagnating. Let’s say a ticket has been in the “Doing” column for 15 days, if your Cycle Time is usually 5 days, that’s a red flag. Jira itself won’t flash a warning; someone has to notice it manually. ActionableAgile Analytics’s Aging WIP Chart:  ActionableAgile provides an Aging Work in Progress  chart that looks like a Kanban board view. Each column of the chart is one of your workflow states, and the tickets currently in that state appear as dots or bars. The catch?  The higher up the dot, the older that item is (how many days it’s been active). This is super intuitive – at a glance, you see which work items are “aging” and might need attention. If a dot is creeping near the top of a column (say approaching your 85th percentile line for Cycle Time), it’s a visual signal that this item might blow past your usual timeline if you don’t do something. It basically answers the question “ What’s about to become a problem? ” every single day. Teams using this chart often make it part of daily stand-ups: “ Let’s check the aging WIP – oh, task XYZ has been in review for 9 days, which is unusually long for us. Should we escalate that? ”. Without ActionableAgile Analytics, you might not see that until day 20 when someone finally asks “ Whatever happened to XYZ? ” In fast-moving environments and even more in regulated ones where delays can mean missed compliance dates, catching aging work quickly is crucial. ActionableAgile Analytics gives you that visibility; Jira doesn’t out-of-the-box. 3. Forecasting and answering “When will it be done?” Jira’s approach:  Jira Software doesn’t provide a built-in way to forecast how long a set of issues will take to complete. Velocity and Throughput averages make a big assumption: that your team’s pace and variability will stay the same in the future as it was in the past. But that’s rarely true. Scrum teams often use Velocity charts or Sprint Burn-downs to guess at completion (e.g., “ we do ~50 story points a sprint, so maybe 3 sprints to finish ”). This does not account for: Changing team size Scope creep or scope reduction Story point inflation Blockers or unplanned work Kanban teams might try to use Control Charts or Throughput averages to project forward. These averages offer a single-point prediction with no understanding of variance or confidence. In the real world, variance matters more than the average. Throughput-based averages (e.g., “ we complete ~5 items/week ”) ignore: Fluctuations in ticket size/complexity Seasonality (e.g., holidays, big releases) External dependencies Many teams are forced to export data to Excel or use add-ons from the marketplace if they want probabilistic forecasts. Jira's Build in Velocity Report - Designed to help you estimate the work you will do in future sprints ActionableAgile Analytics’s Monte Carlo Forecasting:  ActionableAgile has forecasting baked in. You can choose between two modes: “ When will all these issues be done? ” or “ How many issues can we complete by date X? ”. It then runs a Monte Carlo simulation (literally thousands of randomized trials based on your historical Throughput or Cycle Time data). The output is a range of outcomes with probabilities. For example, ActionableAgile Analytics might tell you “50% chance to finish by May 15, 85% chance by May 22, 95% by June 1” for a given backlog. This is golden information for planning – it lets you communicate to stakeholders in terms of risk: “We’re pretty confident we’ll be done by the end of May, but there’s a small chance it could slip into early June.”   With Jira alone, teams often would either give a single date (which is usually wrong) or pad a lot “to be safe.” ActionableAgile Analytics’s forecast is data-driven and updates as your pace changes. It’s also great for answering scope questions: “ If we cut scope by 5 stories, what’s the impact? ” or “ If we pull in these 2 extra tasks, how might it affect the timeline? ” You can simulate those scenarios quickly. Real Screenshot of how BNP Paribas Bank uses Monte Carlo for forecasting to leadership during strategic events. This image shows how the team asses release plans based on Monte Carlo Simulations. Read their full story here In a sense, ActionableAgile Analytics brings an analytics-driven negotiating tool  to product and project management. You can say, “ Sure, we can add that extra feature, but our 85% confident date then moves out a week – is that trade-off worth it? ” This level of clarity just isn’t available in Jira’s standard toolkit. And because ActionableAgile Analytics keeps this inside Jira, you don’t have to manually update spreadsheets for every change, It’s always using the latest data from your actual workflow. Summing things up When your data’s off, so is your direction. And eventually, your customers and stakeholders will be the ones to suffer. Here are the big reasons why teams choose ActionableAgile Analytics over sticking with Jira’s native dashboards: Find Bottlenecks and Outliers Easily:  ActionableAgile Analytics highlights where your process is hurting. Jira might tell you the average, but ActionableAgile Analytics will show you “these 5 tasks took 3x longer than the rest – and they all happened in the Design phase, maybe that’s a bottleneck.” It brings the exceptions to the forefront so you can do something about them. With Jira alone, you often only catch these in hindsight (or not at all). Data-Driven Forecasting:  If you need predictability (who doesn’t?), ActionableAgile Analytics’s Monte Carlo simulations are a game changer. You stop making wild guesses about timelines. Instead, you get probabilities that help set realistic expectations. Teams that use it often find that stakeholders trust them more because they’re transparent about uncertainty (“ We’re 90% confident in this range ”) rather than overconfident in a single date that slips. It’s a more professional way to forecast work, and it’s automated – so you can run a new forecast anytime as things change. This is especially important for project managers or product owners who have to report upwards; you can defend your projections with data. Improve Team Conversations:  This might sound soft, but one of the coolest things about having richer metrics is how it changes the team’s behavior. Instead of opinions like “ I feel like we’re always stuck waiting on QA ” you can look at a chart and say “ Actually, our QA stage has 3 days of wait on average, which is 30% of our Cycle Time – that is significant, let’s fix it. ” It focuses discussions on facts. "A big takeaway is being able to support meaningful conversation with our business partners [Stakeholders], that align to the value stream(s). Having an elevated level of trust in the data of how we operate to support outcomes is a must have." Comprehensive Solution in One Tool:  With ActionableAgile Analytics, you don’t need five different add-ons or exports. It covers Cycle Times, Throughput, WIP limits, Work Item Age, flow efficiency, and forecasting all together. Some teams try to patch together multiple tools: Jira for basic charts, plus Excel for deeper analysis, plus maybe a custom script or two. That’s a lot of maintenance and the context-switching can be painful. ActionableAgile Analytics consolidates a lot of that. And because it’s designed to work with Jira data structure, it’s less fiddly than a generic BI tool. You don’t have to be a data scientist to use it – it was made for practitioners (product managers, Scrum Masters, team leads) who just want answers from Jira without hours of wrangling. In terms of cost-benefit, many consider it worth it because reclaiming the time spent on manual reporting and avoiding project delays pays for itself. Thinking of leveling up your agile metrics in Jira?  Consider giving ActionableAgile® Analytics a try. Many teams, from startups to large enterprises, have made that jump and never looked back. You can start by exploring some resources on the 55 Degrees blog or checking out customer stories . When you’re ready, start a free trial and begin your journey to better data.

We’ve got big news! Personal Views for Data Sets  is now available in beta for ActionableAgile® Analytics for Jira Cloud! 🎉 This new capability is designed to make your data exploration faster, smarter, and more collaborative than ever before. Why Personal Views Matter If you’ve ever found yourself reapplying the same filters and chart settings every time you analyze a dataset, this update is for you. With Personal views, you can now: ✅ Save your favorite chart configurations and filter settings as named views ✅ Quickly switch between different views to explore data from multiple angles ✅ Share views across teams This means less time setting things up and more time understanding your flow, uncovering bottlenecks, and improving delivery, all with greater consistency and collaboration. A More Streamlined Analysis Experience Whether you're collaborating across teams or diving into team metrics, personal views help you skip the repetitive setup and focus on what matters: the insights. By giving you the ability to define reusable, named perspectives, this feature empowers teams to standardize reporting and make discussions around data more aligned and actionable. Rolling Out in Phases We’re releasing Shared Views in stages to make sure the experience is smooth and scalable: 🔹 Phase 1 (Available Now):   Data Set Admins  can create and share views across teams 🔒 (Coming Soon):   Private Views  will allow every user to save and manage their own personal configurations for deeper customization. 🔒 (Coming Soon):  Display your saved views and Data Sets directly on your Jira Dashboards, giving teams instant visibility into the latest, most relevant insights. (Coming Soon) This phased rollout ensures that teams can start aligning right away while giving individuals the power to tailor their own workflows soon after. Try It Out and Tell Us What You Think Personal views is available now, and we can’t wait for you to try it! Jump into ActionableAgile® Analytics for Jira Cloud, save your favorite chart setups, and make your Jira Dashboards more insightful than ever. Have questions? Visit our Support Page Ready to dive deeper? Check out the documentation here Want to chat about the feature or hear how others are using it? Join us in our Community As always, we’d love to hear how you’re using personal views and what you think!