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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!

Still guessing when work will be done?   You’re not alone—and you’re not without help. Figuring out where to start with flow metrics can feel overwhelming, especially when you’re buried in Jira dashboards or worse...... spreadsheets. That’s exactly why alongside our partner ProKanban.Org we created An Introduction to Flow Metrics —a no-fluff, practical guide designed to help you stop estimating and start predicting. What You'll Learn The 4 core flow metrics that matter most How to build team buy-in (without buzzwords) Ways to turn metrics into business value your execs will love How to overcome common challenges from your team "We knew that if we wanted to build predictability, we had to take care of flow metrics." BNP Paribas Bank Polska 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.

Source: Camms License: CC By 4.0 At 55 Degrees, we work with financial institutions across Europe and beyond, providing non-critical ICT services that support collaboration, forecasting, and agile decision-making. While we are not a financial entity ourselves, and therefore not directly regulated under the EU's Digital Operational Resilience Act (DORA), we recognize the increasing responsibilities our customers face when managing digital risks — especially when relying on third-party providers like us. What is DORA? The Digital Operational Resilience Act (DORA)  is a European Union regulation designed to strengthen the financial sector’s ability to withstand and recover from ICT-related disruptions. It applies to a wide range of regulated entities — including banks, insurers, asset managers, and fintechs — and requires them to assess, manage, and monitor the ICT risks posed by their vendors. This includes ensuring that third-party service providers meet high standards for cybersecurity, incident handling, business continuity, and operational resilience. Even if a vendor isn’t classified as “critical,” regulated firms must demonstrate appropriate oversight and assurance — and that’s where we come in. The 5 Pillars of DORA Strategic Commitment to DORA Compliance To further differentiate ourselves in the data security space and demonstrate our long-term commitment to our financial-sector customers, we are actively working toward full alignment with DORA as a third-party ICT service provider. While we are not currently classified as a “critical ICT provider,” our goal is to meet or exceed the standards expected of one. We believe this commitment reflects both our values and our customers' expectations for transparency, resilience, and regulatory awareness. Optional DORA Addendum for Customers To make things easier for customers navigating DORA compliance, we offer an optional DORA-focused addendum to our standard customer agreement. This document outlines additional commitments that reflect the regulation’s expectations for third-party providers, including: Incident reporting protocols Testing and continuity support Subcontractor transparency Risk oversight collaboration The addendum is available on request and can be executed during procurement, onboarding, or renewal. DORA Alignment in Practice Here are just some of the practices we’ve adopted to help customers meet their DORA-related obligations: We maintain compliance with SOC 2 Type II, ISO/IEC 27001, and GDPR frameworks. Our leadership team receives regular cybersecurity training and is engaged in risk oversight. We use Vanta  and InfosecIQ  to deliver continuous security education to our team. We maintain documented and tested incident response and business continuity plans. We support customer-led audits, risk assessments, and data security reviews. We can align to DORA-like incident reporting expectations under customer-specific terms. We are actively assessing our internal practices against the DORA framework and plan to implement enhancements that strengthen governance, risk management, testing, and monitoring. Commitment to Operational Resilience Ultimately, our DORA alignment is part of a larger vision: to offer trustworthy and resilient product services that evolve alongside our customers’ regulatory landscapes. By tracking new developments like DORA — and proactively aligning with its principles — we help ensure that our products, policies, and people support your operational resilience. Confidence in tools starts with confidence in vendors. If you're evaluating tools for DORA-aligned teams, we’re ready to support your journey. 👉 Talk to us  about how we help regulated organizations work smarter.

At 55 Degrees, we're always looking for ways to improve the security, performance, and experience of our products. That's why we're excited to announce that ActionableAgile Analytics for Jira Cloud  is moving from the Atlassian Connect framework to Atlassian Forge . This shift is more than just a technical update — it unlocks new opportunities for our customers and future-proofs the product for years to come. Stronger Security with Scoped, Temporary Access Security has always been a top priority for us. We maintain rigorous security standards, including ISO 27001  and SOC 2  certifications, and we are committed to transparency through our Trust Center . With Forge, ActionableAgile now uses short-lived, scoped OAuth 2.0 tokens  to access Jira data. Unlike Connect apps, which could maintain persistent access to your Jira environment, Forge limits access to only what’s needed  and only when it's needed . This tighter control significantly reduces risk and improves transparency around what the app can do with your data. Even though we continue to host some parts of the app externally (using Forge Remote), all Jira data access remains strictly secured through Atlassian's infrastructure. We take your trust seriously and are continually challenging ourselves to meet and exceed security expectations. We would not be moving ActionableAgile to Forge unless we were confident that the platform is fully ready to meet our high standards. Future-Proofing with Atlassian's Newest Capabilities Atlassian has made it clear that Forge is the future  of app development in their ecosystem. By moving to Forge, ActionableAgile gains access to new APIs, events, and extensibility options  that aren't available to Connect apps. This allows us to deliver deeper, more seamless integrations and ensures that as Atlassian evolves, ActionableAgile will stay right at the cutting edge. It also means faster adoption of platform innovations, so you can take advantage of new features as soon as they become available. Unlocking Exciting New Features Moving to Forge opens the door to a range of new functionality. We're already working on features like native Jira dashboard gadgets  and Confluence macros , making it easier than ever to bring your analytics into the spaces where your teams collaborate every day. With Forge's event-driven architecture, we can also build smarter, more responsive features that proactively support your workflows. Upgrading to Forge isn't just a behind-the-scenes change — it's an investment in providing you with stronger security, richer functionality, and a better overall experience . We're committed to ensuring that ActionableAgile continues to be the best analytics tool for Jira users, today and into the future. We’re excited about what’s ahead — and we can’t wait for you to experience it with us! Don't Miss Out—Upgrade Today! If you're an existing customer, we really encourage you to upgrade to the latest Forge-based version of ActionableAgile Analytics for Jira Cloud today  to start benefiting from improved security, deeper integrations, and upcoming new features. Check your add-on manager to upgrade - it's a simple as button click.

May 2025  – 55 Degrees , a leader in flow analytics and agile solutions, is proud to announce a new strategic partnership with ProKanban.org , the global community dedicated to practical Kanban education and accreditation.  This collaboration is designed to make agile education, expert guidance, and practical tools more accessible for teams, partners, and organizations worldwide. Bringing Greater Education and Community Impact Through this partnership, we’ll be rolling out: A series of live and on-demand webinars. We're excited to get things started on Tuesday, June 10 with our “ Getting Started with Flow Metrics ” webinar. Registration information will be released soon. Downloadable playbooks and case studies, including real-life stories from teams who have implemented Kanban and flow analytics successfully. Guidance on finding the right certification or training path, helping teams and individuals navigate their learning journey with confidence. “We see firsthand just how much demand there is for education and organizational understanding around flow metrics from partners, customers, and teams who are starting to transition to this new way of working and trying to bring their organizations along with them. This partnership with ProKanban.org gives us a way to change that. Together, we’ll be making it easier for people to learn, get support from experts, and connect with others who have faced the same challenges. It’s about making the learning curve less steep and helping teams take the next step.” Brodie Chivers, Partnerships Manager, 55 Degrees “Too often tools overwhelm users instead of enabling them to take action. Our partnership with 55 Degrees is about changing that by giving teams the education and insights they need to make better decisions with their data. It’s a natural extension of our mission at ProKanban.org , to make practical, evidence-based improvement accessible for anyone who wants to deliver value with more confidence.”   Colleen Johnson, CEO, ProKanban.org Supporting ProKanban Trainers and Practitioners Both 55 Degrees and ProKanban.org share a commitment to practical, evidence-based improvement, and believe that learning should be open, inclusive, and supportive.  Through this partnership, ProKanban trainers will have access to additional support from 55 Degrees around licensing and course material—making it easier to demonstrate flow metrics and provide hands-on, real-world learning experiences. Looking ahead, we are committed to exploring new ways to support ProKanban trainers and practitioners as the community continues to grow. About 55 Degrees 55 Degrees helps organizations improve delivery performance with analytics and forecasting tools backed by flow metrics and probabilistic forecasting. Whether teams work in Atlassian, Microsoft Azure, or use our standalone solution, we help organizations improve how work flows, reduce delays, and deliver outcomes with greater confidence and predictability. About ProKanban.org ProKanban.org is a global community focused on helping teams deliver the right work at the right time. Through practical training, tools, and certification, we teach data-driven practices that improve flow, reduce waste, and increase delivery confidence. Our mission is to make work more efficient, predictable, and sustainable without adding complexity. Whether you're a developer, team lead, or executive, our resources are designed to support sustainable ways of working that adapt to change and deliver measurable results.

About Sanofi Sanofi is a global healthcare company known for its innovative approach. The company is committed to advancing scientific breakthroughs to enhance the well-being of individuals. With a presence in approximately 100 countries, Sanofi's team is focused on pushing the boundaries of medicine, striving to accomplish what was once deemed unachievable. Their efforts extend to offering potentially life-altering treatment choices and vital vaccine coverage to millions of individuals worldwide. Additionally, Sanofi strongly emphasizes sustainability and social responsibility as key pillars of its corporate mission. Introducing Bilal Alawiye and his role within Sanofi Bilal began his career as a software engineer, transitioning to Agile practices in 2014 after attending a transformative seminar on Agile principles. His journey led him to an initial focus on Scrum and team dynamics, which expanded into a passion for coaching and Lean principles. Over time, Bilal developed an interest in organizational systems and flow, drawing inspiration from influential thinkers like Daniel Vacanti and others who advanced agile methodologies. Currently, Bilal serves as a Systems Coach at Sanofi, leading a team of approximately eight coaches across different departments within Sanofi’s “Accelerator” ecosystem—a collaborative initiative launched in 2022 to drive strategic projects and foster cultural transformation. His work emphasizes continuous improvement and Lean principles as a foundation for sustainable change. Although “agile” remains part of his title, Bilal prefers terms like “improvement” and “continuous growth,” reflecting his broader approach to systemic coaching and change management. Background Sanofi has been using ActionableAgile® Analytics in Jira Cloud since January 2023.  55 Degrees is excited to build a long-term relationship and support Sanofi with its Accelerator as it looks to drive continuous improvement internally. The Situation Sanofi’s accelerator team embarked on a transformative journey toward agile product delivery, aiming to implement a culture of continuous improvement across their commercial sector while implementing a product-focused approach. Historically, the organization’s large, traditional structure presented a challenge, and moving to a product-focused, iterative model required both cultural and technical shifts. Thanks to supportive leadership, among other things, Sanofi was able to initiate these changes. In 2022, Sanofi’s commercial accelerator team was launched and faced the task of modernizing its approach to product development and delivery within a large, traditionally structured organization. Challenges with Traditional Approaches and Cultural Shifts Sanofi’s work was characterized by highly structured, routine processes that lacked the flexibility to respond to rapid changes in the pharmaceutical landscape. " Teams were not used to working in [an agile] way ," Bilal explained. The company’s size and traditional frameworks made it difficult to scale agility and foster cross-functional collaboration. The shift from rigid, task-oriented workflows to a product-driven approach required not only new processes but also a cultural transformation, which was championed by the CTO. “ Our CTO really had an amazing vision…he came from that world and was able to influence this shift in our ways of working ,” Bilal noted. “ From a business point of view, it was much more of a product approach. We had some conversion rates that we wanted to tackle. We were targeting mainly doctors and patients, but we were also focused on giving treatment faster and earlier to patients. " The Need for Predictable and Efficient Delivery Beyond the cultural changes, Sanofi aimed to improve delivery efficiency and predictability. Traditional practices relied heavily on manual tracking and delayed feedback loops, which slowed down progress and limited visibility into team and product health. By 2022, Sanofi recognized the need to reduce cycle times, standardize their metrics, and adopt a flow-based approach that would allow teams to deliver high-quality outcomes quickly and consistently. "On average, it took about three months for a normal product outside the accelerator to go to production. We needed a way to reduce that dramatically." The Solution: ActionableAgile® Analytics When Bilal and Sanofi’s accelerator team began their improvement and disruptor journey, they recognized that incorporating lean principles and flow into their agile practices—and leveraging their existing data in Jira—could be a powerful way to boost delivery efficiency and foster a culture of continuous improvement. ActionableAgile Analytics for Jira Cloud was the ideal choice, allowing Sanofi’s teams to work directly within Jira without the need to transfer data to external platforms. Once implemented, ActionableAgile Analytics allowed Sanofi to explore real-time flow metrics, sparking immediate interest and engagement. This was boosted thanks to some additional hires who were interested in lean concepts - Something Bilal attributed substantially to the success of rolling out the tool to the organization. Shifting from ‘Mechanical Scrum’ to Flow-Based Agility Sanofi’s accelerator team initially worked with teams accustomed to a structured, routine-based approach, which Bilal referred to as “Mechanical Scrum.” This setup often focused on tasks like daily stand-ups, retrospectives, and story points, which didn’t fully align with the team’s goals for agility.   To change this, the accelerator team began by having one pilot team focus solely on work item age, incorporating this metric into their daily stand-ups. After the stand-up, they would do a problem-solving session they called “Résolution de Problème” (lean practitioners may find this similar to Kaizen discussions).  This shift to incorporating work item age into the daily conversations allowed the team to optimize their work in a very noticeable and visual way. This approach quickly proved valuable, giving the team new transparency into workflow delays and bottlenecks. As they collaborated closely in Jira and experimented with the Monte Carlo Simulations in ActionableAgile Analytics’ for forecasting larger efforts, they realized the power of the platform and probabilistic thinking—sparking broader interest and adoption across teams. Expanding ActionableAgile Analytics Across Teams Encouraged by the shifted mindset and early visible successes, the accelerator team gradually extended ActionableAgile Analytics to additional teams, and soon after, other teams outside the accelerator began adopting the tool in a “snowball” like effect.  Today 7 teams out of 12 use ActionableAgile Analytics as an asset to track flow.  The conversation has started to shift from velocity to flow metrics such as Cycle Time, Work Item Age, WIP, and Throughput.  Most importantly, probabilistic forecasting is now widely recognized across teams throughout the organization, providing a shared language and the ability for more and more of our organization to ground decision-making in real-time data. “People no longer see ActionableAgile Analytics as a tool; they really see it as a ‘continuous improvement of flow’ asset.” (ActionableAgile Analytics Dashboard from March 2025 - Showing how Sanofi keeps WIP limited to just 3 items, enabling them to complete 95% of work items within 10 days. The charts also reveal a dramatic improvement over the year, with the number of work items dropping from over 80 to fewer than 10, reflecting greater focus and flow discipline.) The Result From Bottleneck to Breakthrough: Seeing and Solving the Problem Another Technique that helped Sanofi was seeing their CFD, and heat map showed that sprint after sprint, there was significant aging in the validation process of the work.  Bilal: “We observed that validation was linked to the PO only, We managed to optimize the process of validation by creating a shift right policy where at the beginning of every daily, We would first inspect what needs to be validated and allow everyone to validate that instead of leaving the PO as the main actor of validation as this proved to be a bottleneck for the team.” The North Star for Coaches - Cycle Time The shift towards flow has also reshaped the role of coaching within the accelerator, transforming it from an advisory role into an accountable “sports coaching” model, as the team terms it. Coaches now track metrics like cycle time as a “North Star” to gauge learning and adaptation speeds.  This focus on measurable outcomes has fostered a culture of continuous improvement, with flow metrics like cycle time serving as indicators of efficiency and alignment. In embracing ActionableAgile Analytics, Sanofi’s teams have found a sustainable method for promoting fast, efficient learning and elevating their workflow processes, establishing flow metrics as a core part of their agile transformation. Accelerating Time to Production - 3 months to 12 days! With the implementation of ActionableAgile Analytics, Sanofi’s accelerator team achieved a remarkable improvement in time to production. Previously, moving a product from development to production outside of the accelerator took about three months. However, thanks to the combined efforts of ActionableAgile Analytics, the engineering team, and the discovery team, the accelerator reduced this timeline to just 12 days. By leveraging dedicated engineering platform, teams, and investing in tools like ActionableAgile Analytics,  Datadog, and Backstage for DORA metrics, the ac celerator developed a streamlined, efficient production pipeline, setting a new standard for delivery speed across the organization. “ At Sanofi, we’re a scientific company, so we prioritize a scientific approach to problem-solving and taking action. We can’t rely on wishful thinking alone. For example, if we see that delivery takes 30 days, we can now challenge our coaches with a clear, data-driven question: What coaching strategy could help reduce this to under 15 days? ” Summary of Outcomes Early Adoption and Scalability of Flow Metrics - The Accelerator has laid the groundwork for scaling ActionableAgile Analytics to broader organizational levels. A shift from Mechanical Scrum to Flow-Centered Agility. Significant Reduction in Time to Production. Increased Transparency and Non-Judgmental Data Culture  - Sanofi created a culture of transparency, where data serves as a tool for inquiry rather than evaluation. Support for Agile Coaches with a Standardized “North Star” Cycle Time Metric. What Does the Future Look Like for Sanofi? Establishing Transparency and Embracing Flow at All Levels Looking ahead, Sanofi’s accelerator team envisions a future where transparency and flow metrics become central to all levels of the organization, from individual teams to portfolio-level strategy. By leveraging ActionableAgile Analytics, they aim to create a culture where data sparks productive inquiry rather than judgment. Bilal noted that traditional metrics often push teams toward binary thinking—labeling performance as simply “good” or “bad.” In contrast, flow metrics shift the conversation toward continuous improvement. “ It’s not about good or bad,” Bilal explained. “It’s about understanding where we are now, where we want to go, and what obstacles are preventing us from reaching that target condition—both in terms of process and outcomes. Then we ask: what’s the next step we can test to address that obstacle? ” Currently, ActionableAgile Analytics is primarily applied at the team level. However, Sanofi’s vision involves scaling flow metrics to the organizational tiers where bottlenecks and dependencies often impact larger initiatives. Building Buy-In Although manager buy-in came more easily than anticipated, the team recognizes that more work is to be done among some team members. The challenge is addressing dependencies and resource limitations exposed by flow metrics. The accelerator’s long-term goal is to consolidate data from multiple teams to provide a holistic view of flow challenges, identifying areas where improvement is needed across the organization. With these changes, Sanofi aims to embed flow at every level, evolving from team-specific improvements to a fully integrated, data-driven approach to organizational agility.

We’ve been waiting for this moment, and it’s finally here! Portfolio Forecaster 2.0 is moving from beta to general availability (GA), and we couldn’t be more excited. This isn’t just an update, it’s a complete transformation of how teams forecast their work. Over the past months, we’ve fine-tuned Portfolio Forecaster 2.0 based on your feedback during the beta phase. Your insights helped us iron out issues, enhance key features, and introduce new capabilities that make forecasting more flexible, precise, and powerful than ever -- and this is just the start! Now, Portfolio Forecaster 2.0 is powered in part by Atlassian Forge, packed with new features, and available to even more teams. If you're still using the previous version, now is the time to upgrade because the future of forecasting has arrived! What is Portfolio Forecaster? In an unpredictable work environment, Portfolio Forecaster helps teams remove uncertainty by providing accurate forecasts based on actual work patterns. Instead of relying on gut feelings and static roadmaps, Portfolio Forecaster delivers probabilistic forecasting, allowing you to see when your work is likely to be completed. No magic, just data-driven insights. Whether managing a single project, multiple teams, or an entire portfolio, Portfolio Forecaster gives you the visibility you need to plan confidently. From Beta to General Availability: What’s New? When we launched Portfolio Forecaster 2.0 in beta, we knew we were onto something special. But we didn’t stop there! Thanks to your feedback, we went back to work, refining the experience to make it smoother, smarter, and more secure. Here’s what we improved between beta and GA: ✅ Fixed Bugs & Improved Performance – Faster, more reliable, and smoother than ever. ✅ Scenario Planning – One of the most requested features is here! Now, you can compare multiple "what-if" scenarios to make informed decisions before committing to a plan. ✅ Forecast Portfolios – Get a bird’s-eye view by combining multiple forecasts into a single, streamlined view, perfect for tracking progress across projects, programs, or departments. ✅ Portfolio Forecaster Now Runs on Atlassian Forge Remote – a major upgrade in security and performance, ensuring your data is managed with the highest level of protection. With these enhancements, Portfolio Forecaster 2.0 is more robust, flexible, and secure than ever, built to help teams of all sizes drive better predictability and smarter decision-making. What Makes Portfolio Forecaster 2.0 a Game-Changer? Goodbye, Limitations: Forecast Any Issue Type Forget the old restrictions. Portfolio Forecaster 2.0 lets you forecast using any issue type! Whether you're tracking epics, stories, tasks, or custom issue types, you now have complete flexibility in how you structure your forecasts. Scenario Planning: Explore Different Outcomes Before Deciding One of the biggest enhancements in Portfolio Forecaster 2.0 is Scenario Planning, giving you the power to explore multiple possibilities before making a decision. With Scenario Planning, you can: ✔ Compare different “what-if” scenarios to evaluate best-case, worst-case, and realistic timelines. ✔ Adjust team capacity, work scope, or priorities and instantly see the impact on your forecasts. ✔ Make data-backed decisions instead of relying on guesswork. This feature is a game-changer for project managers, product owners, and leadership teams. It helps you plan ahead, mitigate risks, and adjust strategies proactively. Forecast Portfolios: A High-Level View of Everything One of the most powerful additions in Portfolio Forecaster 2.0 is Forecast Portfolios. A way to combine multiple forecasts into a single, consolidated view. This means you can: ✔ Track multiple projects, teams, or initiatives from one place. ✔ Keep leadership and stakeholders informed with a clear, high-level overview. ✔ Maintain visibility into cross-team efforts without losing the details. If you manage multiple teams or large-scale projects, this is the feature you’ve been waiting for! Stronger Security with Atlassian Forge Remote Portfolio Forecaster now runs on Atlassian Forge Remote, a major security and performance upgrade. This means: ✔ Tighter data security aligned with Atlassian’s latest best practices. ✔ Improved scalability, ensuring Portfolio Forecaster can grow with your organization. ✔ Better performance, so forecasts run faster and smoother. Security and reliability matter, and with Forge Remote, Portfolio Forecaster 2.0 is built for the future. What’s Next? More Awesome Stuff on the Way! We’re not stopping here. We’re working on even more exciting features to take your forecasting experience to the next level. While we can’t reveal everything just yet, trust us, you’re going to love what’s coming! Stay tuned for future updates. Time to Upgrade - Here's What You Need to Know For Beta Users:   If you were part of the beta, ask your Jira admin to upgrade now so you can continue to use the app and access the latest updates. For Users on the Legacy version:   Still using the older version? Now’s the perfect time to upgrade! Version 1.0 will no longer be available after April 2nd , so make the switch today. Need help? Your Jira admin can assist. Need help upgrading?  Check out our documentation page  for step-by-step guidance.   Got questions? Our support team is happy to help! Reach out HERE . Want to discuss this new release? Join the conversation in our community !   Haven't used Portfolio Forecaster before? Now's the perfect time to start. Experience how it will help your team streamline forecasting and decision-making. Try it out for free !     Thank You for Helping Us Build This! Your feedback made this possible, and we’re so grateful to our beta testers and early adopters for shaping Portfolio Forecaster 2.0 into the powerhouse it is today. Upgrade now and start forecasting with confidence!

About Fruugo Fruugo enables retailers everywhere to sell to shoppers anywhere. As a global marketplace, Fruugo understands the complexities and intricacies of cross-border trade and offers the quickest, easiest, and most risk-free way of selling your products globally. Customers can shop in their language and currency from anywhere in the world, enjoying a truly localized experience with exchange rates and shipping costs, all managed by Fruugo. As more customers shift to shopping online, they are increasingly shopping internationally. Fruugo’s highly diverse range of customers and retailers is empowered by its innovative technology and knowledge, allowing both to connect seamlessly in over 40 countries around the world. Introducing Ben Cutler and his role within Fruugo Ben is the Lead Scrum Master at Fruugo, where he’s been making an impact for nearly two years. With a strong background in development, testing, automation, DevOps, and leadership, Ben brings a wealth of hands-on experience to his role. While Ben serves as Scrum Master for multiple teams, he also plays an integral part in driving transformative initiatives across the organization. His work focuses on improving predictability at Fruugo and fostering a culture of continuous improvement. In addition to his Scrum Master duties, Ben also dabbles as a Jira administrator, customizing and automating workflows to streamline processes. His goal is to empower teams, provide valuable insights into the business, and ultimately help the teams move faster. Background Fruugo started using ActionableAgile® Analytics in January 2023 - shortly before Ben started with the company. Ben did however drive the adoption and integration of Portfolio Forecaster in 2024 which is now used alongside ActionableAgile® Analytics. Using data to support decision-making and agile practices is something the team at Fruugo has come to have at the core of its team.  Fruugo are heavily invested in the Atlassian suite and along with Jira and Confluence they also use Jira Service Management, Bitbucket, Bamboo and Status Page. The Situation When Ben joined Fruugo in 2023, the team had already started using ActionableAgile® Analytics. While its adoption across the engineering teams was still in its early stages, the Agile team had started to embrace a data-driven decision-making mindset and a strong focus on continuous improvement. ActionableAgile® Analytics, seamlessly integrated with Jira, quickly became a valuable tool in supporting these efforts. At the time, Fruugo’s teams were primarily Scrum-based, with a few Kanban teams. Many teams were still relying on Story Points and deterministic estimates. Ben introduced his teams to a more forward-thinking approach: forecasting with Throughput data and Monte Carlo simulations within ActionableAgile® Analytics. After reviewing the data and finding it to be more reliable and engaging the team in more meaningful conversations, the teams quickly adopted this new method. Other Scrum Masters have since followed suit, applying the same approach with their teams, and found similar success How Fruugo uses ActionableAgile® Analytics Ben mentioned that the team at Fruugo use ActionableAgile® Analytics every day in some shape or form. This can be seen in many of the traditional Scrum Events.  Sprint Planning At the team level, ActionableAgile® Analytics plays a crucial role in sprint planning. By leveraging Monte Carlo simulations, teams address the key question: “What should our commitments be?”   This data-driven approach prevents overloading teams with work that cannot be delivered, improving predictability and ensuring sustainable commitments. Monte Carlo: When Chart in ActionableAgile® Analytics Daily Stand Ups:   During daily stand-ups, the Work Item Age Chart is a standout feature for Fruugo. This chart helps teams decide what to prioritize, a practice Ben described as invaluable.  Inspired by Daniel Vacanti’s guidance to “focus on your oldest ticket first,”  the teams use this visual tool to identify and address aging work items effectively. Aging Work In Progress Chart in ActionableAgile® Analytics Retrospectives:  In team retrospectives, Fruugo often incorporates data from ActionableAgile® Analytics to review key metrics like Cycle Time and Throughput. While retrospectives don’t always rely heavily on data, Ben emphasized that these insights are especially valuable when the team wants to assess performance and identify areas for workflow improvement. As Ben put it, reviewing this data is "great for assessing performance and focusing on the team’s flow." This flexible approach allows teams to strike a balance between reflective discussions and actionable insights, ensuring retrospectives remain both meaningful and productive. Large-scale Project Planning with Portfolio Forecaster Outside of events  At Fruugo, Portfolio Forecaster is an essential tool that extends beyond the typical day-to-day Agile events. It plays a critical role in facilitating broader conversations, setting realistic expectations, and supporting key decision-making processes. One of its uses is in Fruugo's Portfolio Review meetings, which take place every two weeks. These meetings bring together senior leadership and representatives from all product areas to review progress on the current strategic priorities.  To prepare for these sessions, Scrum Masters and their Product Owners (POs) will meet to review the most up-to-date forecasts provided by the Portfolio Forecaster. This ensures everyone is equipped with clear, data-driven insights to present during the Portfolio Review meetings. What sets the Portfolio Forecaster apart is its flexibility. Ben highlights that it’s not tied to a rigid schedule and can be used dynamically to support planning and prioritization whenever needed. For example: When a batch of new work is refined, the team consults the forecaster. After completing a significant amount of work, the forecast is updated. When requests arise to introduce new work, the tool can show the potential impact. This continuous forecasting approach allows the team to make informed decisions in real-time. By having access to accurate and up-to-date data, Fruugo can engage in meaningful conversations at both the business and portfolio levels. While the tool isn’t monitored every hour, its availability ensures clarity and confidence in decision-making whenever it’s needed. Fruugo’s Results 50 Work Items, 4,000 Days: A Workflow Overhaul Success Story  Ben shared a remarkable transformation story from Fruugo's IT Support team, which handles internal tech support and requests.  Initially, the team faced significant challenges in managing their workload efficiently. Balancing dual responsibilities and providing support while also contributing to project work, meant they couldn’t focus fully on either. As a result, their work-in-progress began to spiral out of control. Using ActionableAgile® Analytics, the team discovered that in September 2023, they had approx 50 work items in progress, with a staggering total age of 4,000 days  ( data provided by the Work In Progress Run Chart ). By leveraging the insights gained from ActionableAgile® Analytics, the team initiated focused discussions—similar to retrospectives—about prioritization and process improvement. They used the WIP Run Chart and Cycle Time data to: Identify request types taking longer to resolve. Prioritize efforts to improve these request types. Gather more data to address recurring bottlenecks. Enhance processes, such as improving request submissions to prevent inefficiencies. The results were dramatic. Today, the team has reduced their work-in-progress to 12 items with a total age of only 77 days. This shift has transformed their service desk from "not very good" to a fit-for-purpose, highly effective service. Reflecting on the achievement, Ben shared: “I like to look at that graph on difficult days or when I feel like I haven’t made much progress. It reminds me that we’ve achieved something.” Teams more informed  Ben shared how ActionableAgile® Analytics is empowering teams to make more informed, data-driven decisions. While the organization is still refining its overall approach, many teams, especially the Product Owners (POs), are now much clearer on their direction and how they will achieve their goals. By utilizing ActionableAgile® Analytics and Portfolio Forecaster, the teams can now make more strategic decisions about prioritization. This ranges from small-scale decisions, like evaluating the impact of adding an extra ticket, to larger, more strategic choices, such as assessing the consequences of taking on an additional week’s worth of work. This deeper understanding allows teams to better weigh trade-offs and fully understand the implications of their decisions on ongoing projects. Ben emphasized that this level of clarity and insight is something they aim to extend to the wider organization, ensuring that everyone can benefit from a consistent, data-driven approach to planning and prioritization. Summary of Outcomes Improved Team Alignments/ Expectations   Dramatically Improved Service Response Time Improved Predictability At A Team and Business Level.  Faster Value Delivery Informed Conversations and Decision-Making What Does the Future Look Like for Fruugo? Placing Greater Emphasis on Flow Looking ahead, Fruugo is prioritizing improvements in team flow, with a specific focus on optimizing the end-to-end work delivery process. Challenges such as limited automated testing and deployment delays have highlighted key areas for improvement, which will be addressed to create a smoother, more efficient delivery pipeline. By leveraging Cycle Time  and Throughput data  from ActionableAgile® Analytics, Fruugo will identify bottlenecks and inefficiencies in team workflows. The goal is to collaborate closely with teams to increase throughput, streamline processes, and ensure a seamless flow of work from start to finish. This renewed focus on flow is vital to Fruugo’s future, as it will help reduce delays, boost efficiency, and ultimately deliver more value to the business. Experimenting with OKRs At Fruugo, Ben has been piloting Allan Kelly’s Agile OKR framework, which is now being rolled out across all teams. The adoption of OKRs is designed to empower teams to design solutions that can be delivered within a committed timeframe, while also fostering a shared understanding and alignment across the business. When combined with complementary practices like Story Mapping and slicing, OKRs are driving greater focus, reducing distractions and increasing value. Additionally, tools like ActionableAgile® Analytics and Portfolio Forecaster provide valuable data and insights to guide conversations around work sizing, prioritization, and ongoing stakeholder discussions.

About Wireless Logic Wireless Logic – IoT connectivity for any device, anywhere   Wireless Logic is a global leader in Internet-of-Things (IoT) connectivity, dedicated to bridging the physical and digital worlds with seamless, secure, and scalable solutions.   With more than 14 million devices connected across 165 countries and direct partnerships with 50+ mobile and satellite operators, they offer global coverage and end-to-end IoT services which accelerate the success of IoT projects.  Conexa, a purpose-built platform and dedicated IoT network, enables customers to securely connect and manage assets across any network and any number of deployments. This simplifies operations, accelerates time to market, lowers the total cost of ownership, and ensures ultra-reliable connectivity.  Their IoT services are meticulously designed, tested, deployed, and managed to meet the specific needs of each customer device fleet. They strive to deliver the most reliable, flexible, and secure connectivity services in the market.  Wireless Logic's customers range from global enterprises and governments to startups and SMEs, and they operate across a wide range of market sectors, including agriculture, healthcare, manufacturing, security, transport, energy, utilities, and smart cities.   Backed by Montagu, a leading mid-market private equity firm, Wireless Logic benefits from unrivalled financial strength and continued investment in growth and innovation.  Introducing Susan Ridgeon and her role within Wireless Logic Susan has been a Scrum Master at Wireless Logic for two and a half years, playing a pivotal role in guiding her teams toward better processes and outcomes. With her experience spanning over five years in Agile practices, Susan brings expertise in fostering realistic planning and continuous improvement, even amidst the challenges of rapid company growth. Coming from a Microsoft Azure environment, Susan faced an initial challenge adapting to Atlassian and Jira upon joining Wireless Logic. Early on, she realized the limitations of out-of-the-box Jira reports, especially for metrics like Cycle Time. In her role, she actively works to optimize Wireless Logic’s use of tools like Jira and integrate probabilistic forecasting methods to drive better delivery accuracy. Background Wireless Logic started using ActionableAgile® Analytics in Jira Cloud in mid-2023 as part of their overhaul to move away from time-based estimates. While working in three-week sprints, probabilistic forecasting has since proved to be not only more accurate but also a better fit for Wireless Logic’s growing needs. The Situation Wireless Logic faced a critical challenge: shift away from outdated time-based estimation methods that led to over-optimistic, unrealistic plans. This challenge was deeply felt by teams composed of Technical Leads, Technical Product Managers and Engineers, who, despite best efforts, often failed to complete deliverables and meet expectations. One of the biggest pain points Susan faced was the “false sense of accuracy” that reinforced the reliance on time-based estimates. Though developers were eager to move away from this approach, alternatives like story points posed their own challenges. There was a lot of mis-understanding surrounding story points and the lack of usability as a delivery date.  As the company recognized the need to prioritize more realistic planning and foster a culture of continuous improvement, Susan embarked on a mission to introduce approaches, like probabilistic forecasting with ActionableAgile® Analytics, to align the teams’ goals with achievable outcomes.  Along with ActionableAgile® Analytics, T-Shirt sizing proved more suitable for the team to help shape the conversations and drive discussions. Limited Visibility and Manual Efforts To bridge the gap, Susan started conducting her own experiments. After starting a free trial of ActionableAgile® Analytics, Susan would compare the teams time-based estimates to the forecasts provided by the Monte Carlo simulations from ActionableAgile® Analytics. Susan vividly recalled a pivotal sprint where one of the teams planned 20 tickets but managed to deliver only nine. In the background Susan had used the Monte Carlo simulation which forecasted that the team would complete 10 tickets—accurately reflecting the outcome.  This result highlighted the inadequacies of their time-based estimation approach and underscored the value of probabilistic forecasting.  Another challenge was extracting critical metrics like Cycle Time from Jira. “It’s just really hard to get that information from Jira,” Susan explained: Implementing ActionableAgile® Analytics - What happened after? The implementation of ActionableAgile® Analytics at Wireless Logic was a straightforward process. Susan built a business case highlighting the tool’s potential to improve accuracy and efficiency, which gained buy-in from the Technical Leads and the Engineering Manager. The team started out with approval for a monthly subscription before moving over to an annual subscription in February 2024.  During the initial phase of the rollout of ActionableAgile® Analytics, Susan recalled an experience seeking assistance for a few technical questions which required some assistance from the support team at 55 Degrees.  “ Your support team are amazing—feel free to include that in your post! They’re so responsive, getting back to me quickly and even setting up a call with more of the team when we run into an issue. Truly impressive service! " The Rollout – One Sprint team at a time When Wireless Logic decided to implement ActionableAgile® Analytics it was decided to roll it out team by team. Initially this began with one sprint team which were chosen due to their experience and capacity.  After proving success with this team, the tool was expanded to other teams one by one. During the rollout, Wireless Logic primarily used three main charts: Aging Work in Progress, Cycle Time Scatterplot, and Monte Carlo ("How Many"). ActionableAgile® Analytics is now used during retros – During which the team will look at the data from the Cycle Time chart and look for any outliers. This helps the team break down where work stalled which initiates valuable conversations.  Conversations which are not to drive blame, but to actively seek improvements – The team tries to understand if there were any lessons learnt and if they could have done anything different to formulate actions for improvement to take forward.  “ On occasions we have aging work, we will review in the daily scrum as the app visualises it much better than anything we get in Jira.  It then helps teams to know where to focus efforts, prioritise and make their daily plan. Engaging Teams and Encouraging Discussion To facilitate the rollout of ActionableAgile® Analytics, Susan conducted internal workshops focused on flow metrics such as Work Item Age and Cycle Time. “I’ve been showing the teams how to get the information and explained why it’s important for Daily syncs” she shared. These efforts also prompted a cultural shift in how teams approached estimation. Inspired by insights from meetups and Monte Carlo simulation results, Susan introduced T-shirt sizing as another discussion point to complement estimates from ActionableAgile® Analytics—a strategy to spark more dialogue.  “We also use T-shirt sizes now. If someone says large and someone else says small, that starts a conversation, it encourages discussion and shared understanding. The conversation that follows helps uncover hidden complexities, risks, or gaps in knowledge, leading to a more accurate shared understanding of the task. ” This change helps to identify where they can split tickets into smaller chunks of work. Perhaps the most significant outcome was the cultural change within the teams. Susan described a shift in focus from starting new tasks to finishing work in progress: The Result  A Shift to Realistic Planning Implementing ActionableAgile® Analytics has transformed Wireless Logic’s planning approach, leading to more achievable commitments. Susan highlighted this shift: “We are more likely to finish everything that we’ve committed to in planning than we were before. The data is much more realistic, and we’re not overplanning like we used to.” With Monte Carlo simulations, teams now base their plans on throughput and capacity, resulting in plans that align more closely with their actual delivery potential. Fostering a Culture of Finishing The cultural shift toward completing work rather than starting new tasks has been a standout result of using ActionableAgile® Analytics. Susan noted the change in team conversations: “Stop starting, start finishing. That became the focus. It was really positive to see tech leads and team members championing this mindset. ” One team lead exemplified this change, encouraging the team to prioritize completing aging work instead of pulling in more tasks. By comparing the same day a year apart Susan explained the Work In Progress was 20 a year ago and now the team has 5 items in progress. This focus has made the work more manageable with less risk.  Empowering Teams with Insights The adoption of actionable data through metrics like WIP aging and cycle time has driven meaningful conversations in retrospectives and daily scrums.  By incorporating these insights, teams are improving their processes and gradually achieving greater efficiency and predictability. “ With the flagged option in ActionableAgile® Analytics, we can easily identify delays in our workflow. For instance, when reviewing cycle time, we can see that a task was blocked for 10 days and investigate the reasons behind it. This insight helps us address bottlenecks and improve our processes”. Summary of Outcomes Increased Predictability  Improved Collaboration and Focus Streamlined Data Insights Increased Focus on Prioritization Structured Conversations What Does the Future Look Like for Wireless Logic? Integration of ActionableAgile® Analytics into the business Wireless Logic is exploring opportunities to expand the use of ActionableAgile® Analytics beyond its current teams to create a unified approach across the business. While the tool has already driven significant improvements in planning and collaboration within engineering and Dev Support teams, Susan sees potential for wider adoption. “We’re still working on encouraging more teams to use the tool. Everyone has access, but it’s mainly myself and our other Scrum Master, who are actively leveraging it,” Susan explained. By embedding flow metrics and data-driven practices across all teams, Wireless Logic aims to foster a culture of transparency and shared accountability. Using Sprint Lengths for Monte Carlo Forecasting While using Monte Carlo Forecasting within the app, Susan encountered a challenge related to her team's sprint-based approach. Her teams currently work in 3-week sprints, which means throughput data is only updated at the end of each sprint. As a result, forecasting based on recent data can be difficult, since the ActionableAgile® Analytics Monte Carlo model relies on a continuous flow of completed work. Ideally, Susan and her team would be able to generate forecasts in the context of their sprint cycle, rather than being constrained by a time-agnostic approach. However, Monte Carlo Forecasting is primarily designed for workflows with steady, incremental throughput—like Kanban—rather than batch-based sprint systems. Since providing this feedback, 55 Degrees has now put this on our public roadmap ( See the Under Consideration tab -  “ Forecast by additional time units” – If this is a feature you would like to see added   you can view the roadmap of ActionableAgile® Analytics here  and vote on features such as this that you would like incorporated into the app.