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About BNP Paribas Bank Polska In Poland, BNP Paribas Bank Polska S.A. provides services to retail customers and other segments, including Wealth Management, microbusinesses, SMEs, and corporate banking.  As part of the BNP Paribas group, a prominent European bank with a global presence , BNP Paribas Bank Polska offers responsible and innovative financial solutions that help their customers change their world and support the Polish economy.  Damian Rybiński and his role at BNP Paribas Bank Polska As an Agile Coach at BNP Paribas Polska, Damian plays a pivotal role in supporting agile transformation goals. His responsibilities include working with Tribe Leaders, coaching and mentoring Scrum Masters, and ensuring overall effectiveness, efficiency, and predictability within the tribes.  "It's a wide role in our organization. In a nutshell, as an Agile Coach, I support tribes by being a lean-agile leader, coach, mentor, and doer—depending on the situation." Background BNP Paribas Bank Polska has been using ActionableAgile™️ Analytics for Jira for over a year now. 55 Degrees is excited to build a long-term relationship with BNP Paribas Bank Polska as they continue their Agile Transformation.  The Situation Generally speaking, being Agile at Scale  is an incredibly hard feat to pull off. With over 8000 employees in Poland and nearly 4.2 million customers, BNP Paribas Bank Polska faces the monumental challenge of implementing agile practices across a vast and diverse organization. The complexity of the banking sector added another layer of difficulty, making it essential to find solutions that could simplify processes while maintaining high standards of quality and predictability. BNP Paribas Bank Polska was motivated to move away from the limitations of out of the box gadgets and filters within JIRA and Confluence, which they found to be cumbersome and inefficient. Instead, BNP Paribas Bank Polska sought a solution that seamlessly integrated with JIRA and offered a comprehensive visualization of flow metrics and data that could enhance decision-making and operational efficiency. Limited predictability and metrics BNP Paribas Bank Polska faced challenges in accurately tracking metrics and achieving predictability. Their previous reliance on native JIRA features and Excel for flow metrics visualization didn’t provide the insights they were looking for, despite the extra effort required to use them. The lack of a seamless and intuitive tool for connecting and visualizing data meant that the organization struggled to build reliable forecasts. It was nearly impossible to make the informed, data-driven decisions required to manage project timelines and expectations effectively.  "We knew that if we wanted to build predictability, we had to take care of flow metrics." The standard process at scale Achieving standard processes at scale was a significant challenge for BNP Paribas Bank Polska. The organization struggled to establish a straightforward connection with Jira to visualize data metrics. This led to them prioritizing “ease of use” as one of the key criteria when assessing new tools. The complex banking environment further complicates processes' efficient implementation and standardization, making it difficult to ensure consistent and effective practices across all teams. For instance, different regulatory requirements across regions, varying legacy systems, and the high volume of transactions all contribute to the complexity. The Solution ActionableAgile™️ Analytics for Jira BNP Paribas Bank Polska considered several alternative solutions but ultimately chose ActionableAgile Analytics for its ease of implementation, advanced flow metrics, and customer support around the tool. Recognizing that a successful rollout of the ActionableAgile Analytics was of utmost importance, the BNP team held multiple sessions with 55 Degrees while assessing the tool. To prepare, 55 Degrees conducted introductory training courses with the Scrum Masters, after which BNP held internal training sessions for their leadership.   BNP Paribas Bank Polska attributes their success not only to choosing the right solution, but also to their thorough preparation. This is evident in the rapid and widespread adoption of their practices by over 150 squads across the company. Catalyzing change across 150 squads Implementing ActionableAgile Analytics across 150 diverse squads has been a transformative force at BNP Paribas Bank Polska. The tool has addressed the need for ease and scalability in a challenging banking environment by simplifying complex processes and providing straightforward metrics visualization. The comprehensive training sessions for Scrum Masters and other leaders ensured effective adoption, which was crucial given the complexity and scale of operations. This facilitated a unified approach to managing flow metrics, making spreading these practices easier across all teams.  With the value of ActionableAgile Analytics proved internally among the initially trained squads, BNP felt confident in rolling the tool out to approximately 150 existing squads across the company in 2023. "In agile transformation, Agile Coaches and Scrum Masters often do things that the business doesn't see. Having the data to demonstrate the benefits makes it much easier to collaborate." Common language around metrics ( It IS possible! ) BNP Paribas Bank Polska is deep into its third year of Agile transformation, and the integration of ActionableAgile Analytics has played a pivotal role in establishing a common language around flow metrics. Before the implementation, many people within the organization had never heard of flow metrics.  Establishing a shared language and cultivating a unified mindset has been pivotal for successful implementation. Damian highlighted that Scrum Masters now use flow metrics during events and meetings, enabling diverse teams to benefit from a standardized approach, regardless of their specific frameworks or methods. Scrum Masters can fully leverage the tool while addressing any blockers or impediments that arise inside their tribes. "The actions of standardizing processes and implementing ActionableAgile™️ Analytics to predictability have been crucial factors in our success." Happy Business Leaders In today's financial climate, demonstrating the business value of third-party tools to leaders is crucial. At BNP Paribas Bank Polska, showing business leaders the value of ActionableAgile Analytics has been quite effective. Damian: "We showed the relationship between reducing Work In Progress and shortening Cycle Time or between reducing Work In Progress and increasing Throughput—the number of items we delivered. This allowed business leaders to see these correlations clearly. And since they see the benefits and the real data, they are more eager to work with us to make it even better. It's important for them to see the real data and benefits."  Real data is displayed to leadership during strategic events. This image notes the correlation between reducing Work in Progress and reducing Cycle Time. Real data is displayed to leadership during strategic events. This image shows how the team asses release plans based on Monte Carlo Simulations. Predictable Predictability The implementation of ActionableAgile Analytics has significantly enhanced predictability at BNP Paribas Bank Polska.  By leveraging Monte Carlo simulations and metrics such as Cycle Time and Throughput, teams can now provide accurate forecasts and answer the critical question, "When will it be done?" This level of predictability is essential for planning effectively and for meeting stakeholder expectations.  Additionally, The tool has enabled a common approach to monitoring work progress and delivery dates, making it a valuable asset for ensuring timely and reliable outcomes. Simplified complex processes for over 150 teams, making metrics easier to understand and improving scalability. Successfully implemented unified flow metrics management along with a common language for flow metrics. Strengthened collaboration with business leaders by showing clear benefits and real data, gaining their support for agile projects. Improved predictability with accurate forecasting and timely delivery using metrics and simulations, meeting stakeholder expectations. What does the future look like for BNP Paribas Bank Polska? Continuing the standardization of metrics - step-by-step BNP Paribas Bank Polska plans to continue standardizing the Cycle Time and Work Item Age metrics across the organization to further enhance predictability and efficiency. This ongoing effort involves gradually implementing additional ActionableAgile Analytics metrics and ensuring that all squads are aligned in their use.  The bank recognizes that achieving high levels of consistency and quality in its processes is crucial for maintaining its competitive edge in the complex banking sector. Damian: “ We believe these follow-ups are crucial when it comes to standardizing the tool. You have to have a plan because it's impossible to implement everything at once .” Cultivating agile excellence 🥚 Maintaining an agile mindset is a key internal objective driven by the need to enhance predictability, quality, and process efficiency.  Thus, internal chapters within BNP Paribas Bank Polska, the Agile Excellence Chapter and Efficiency Excellence Chapter, have established a dedicated ‘ Flow Metrics Circle   🥚’ ( informally known as EGG internally ) to support the standardization of flow metrics.  Damian: “ We need to build predictability. We need to take care of our quality and we need to shorten our feedback cycles in the most innovative and complex areas. We need to have better processes that are not only faster, but also better in terms of quality and more accurate when it comes to market’s requirements ."

September has brought exciting developments as we focus on enhancing your experience with ActionableAgile™ Analytics. We’ve listened to your feedback, and we’re thrilled to share the latest features designed to improve your workflow and data management capabilities. Here’s a look at what’s new this September! Save and Access Data Sets Easily We’ve been listening to your feedback and are excited to share the latest updates we’ve been working on to improve your experience with ActionableAgile™ Analytics for Azure DevOps! Launching in Q4, we're excited to release the beta version of the new Saved Data Sets feature. We recognize that many of our users frequently return to specific configurations, often spending valuable time reloading and setting up their preferred data sets. By allowing you to save your customized configurations, we help you streamline your workflow, enabling you to focus on what truly matters—driving insights and making data-driven decisions. You'll have a simple dialog to open or edit your saved data sets, giving you full control and flexibility. What Problems Does the Saved Data Sets Feature Aim to Solve? The Saved Data Sets feature addresses several key challenges faced by our customers: Time Efficiency:  Users often spend significant time reconfiguring data sets to match their previous setups. By allowing the saving of custom configurations, we eliminate repetitive tasks, enabling you to load your preferred settings instantly. Consistency:  For users relying on specific configurations for reporting or analysis, the risk of human error during manual setup can lead to inconsistencies. Saved Data Sets ensure that you can retrieve your exact configurations, enhancing accuracy and reliability in your work. User Experience:  Navigating through complex data configurations can be overwhelming. This feature simplifies the process, making it more intuitive and user-friendly, especially for new users or those managing multiple projects. Enhanced Focus:  By reducing the time spent on setup, users can dedicate more attention to analyzing data and deriving insights, ultimately improving productivity and decision-making. But that’s just the beginning! Shortly after the beta release, we’re adding more improvements based on what you’ve asked for. You’ll be able to mark your favorite data sets and access quick links to recent and favorite data sets directly from the data sets dropdown —making navigation a breeze. Once we've gathered your feedback and fine-tuned the experience, we aim to move out of beta in early January. Our goal is to make it effortless for you to manage and update your data sets in a way that best fits your workflow. We can’t wait for you to try it out! External File Loader Last month, we shared an update on loading external data files, and we’re thrilled to announce that it’s officially live for our Standalone version! After a successful validation period, we’re gearing up to roll it out to all other versions soon, with full deployment expected by the end of the month. Why the Update?  Our previous data loading system often felt restrictive, leading to user frustration with unclear error messages and cumbersome formatting requirements. To resolve these issues, we’ve implemented several key enhancements: Simplified File Formatting:  We’ve integrated direct links to file format requirements within the loader and added a download button for the template—eliminating the need to check the documentation. Smart Column Mapping:  Users can now map file columns to specific types such as ID, Title, or Workflow Stage. While the system is designed to map certain column types, you have the flexibility to override our suggestions. Date Format Detection:  The system now displays the detected date format (e.g., MM/DD vs. DD/MM), allowing you to verify and adjust as needed, resolving a common pain point. Comprehensive File Analysis:  After mapping, we analyze the entire file and identify any cleanup actions needed. You can correct these for future uploads or proceed with loading the file as is. If there are rows with errors, we’ll provide detailed information and allow you to export a list, noting that any problematic rows will be removed if they continue. New Global Date Range Filter This month, we will be rolling out the new global date range filter, which allows you to choose which portion of your data set you want to analyze at any given time. Once set, it will apply to all charts until you change it. You will no longer have to go into the date control of each chart to analyze a smaller time period. Explore Our Product Roadmap Page! Have you explored our  product roadmap page  yet? If not, you’re missing out! It’s your exclusive gateway to all the exciting developments in ActionableAgile™ Analytics. Curious about what features might be next? Our "Under Consideration" section showcases ideas inspired by your feedback. Our "Planned" features highlight what we’re prioritizing based on your input. Stay informed with "In Progress" updates to follow the development of actively worked-on features. Plus, check out the "Released" section to see the latest improvements we’ve rolled out to enhance your experience. Your insights truly matter to us! If you have ideas or features you’d love to see, please share them. By visiting our product roadmap page, you can stay updated on our latest developments and actively contribute to the future of ActionableAgile™ Analytics. Take a moment to explore the roadmap and let your voice be heard—together, we can create something amazing! Thank you for being a valued member of the ActionableAgile™ Analytics community! We look forward to your feedback on these new features.

About KFC UK & Ireland KFC is a global chicken restaurant brand with a rich, decades-long history of success and innovation. It all started with one cook, Colonel Harland Sanders, who created a finger lickin’ good recipe more than 75 years ago—a list of 11 secret herbs and spices scratched out on the back of his kitchen door. Benjamin Richards and his role at KFC UK & Ireland Ben serves as a Systems Coach at KFC UK & Ireland, where his primary focus is aligning teams and individuals to drive greater efficiency while reducing waste across the organization. In his role, Ben oversees the Agile Guild,  supporting and fostering change across different business functions. His responsibilities extend beyond just delivery; he invests in building cohesive teams and fostering a culture of continuous improvement. Ben has been a user of the Atlassian suite of tools for 10+ years. Background KFC UK&I has been using ActionableAgile™️ Analytics in Jira for four years - since 2019. 55 Degrees is proud to witness over 4 years of growth alongside KFC UK&I. The Situation KFC UK&I wanted to prioritise quick and consistent delivery of value to customers whilst understanding the health of their products. To level up the business, they began to embark on a journey of continuous improvement with the aim of becoming more predictable and having more meaningful conversations internally about how they work to meet their goals and drive meaningful outcomes. Enhancing Efficiency and Reducing Errors To improve, KFC UK&I needed to reduce the administrative burden of, and impact of human errors in, maintaining large spreadsheets for tracking team and product health metrics. Automating data to drive decision-making became an intriguing direction for the team. " There was the admin overhead of, as well as the human error within, the spreadsheets and additional data you are trying to work with. There was a lack of standardisation and continuity " Advancing Towards Standardised Processes A lack of standardisation and continuity in data analysis processes made it difficult to get a consistent and accurate view of team and product health. It was clear that this created barriers to communicating data and language across teams. Facilitating Seamless Data Exchange and Contextual Understanding: Sharing data and insights between teams and stakeholders was challenging, leading to inefficiencies and misinterpretation. There was an incomplete bigger picture between the teams thanks to data silos and standardisation. " The spreadsheet that I was operating from was easy for me, because I'd gone through the pain of curating and creating it. However, maintaining at scale and taking others on the journey to take ownership a struggle, and rightly so. " The Solution: ActionableAgile™️ Analytics When KFC UK&I decided to move toward a data-driven and automated approach to agility, it was important that they were able to use their existing data in Jira. ActionableAgile™️ Analytics for Jira was ideal, because users don’t have to leave Jira, and because the data in Jira stayed safely in Jira at all times.  Once starting with ActionableAgile™️ Analytics , KFC UK&I saved time from the very start by avoiding manual spreadsheets and loading their existing historical data directly from Jira in minutes (or less). There was no more waiting - discussions could start immediately! " With ActionableAgile™️ Analytics we could work with data at its source and leverage our existing system (Jira) - with ease. Being able to define data, manipulate it, and assess different time periods - that was a massive win. " Overcoming Data Silos and Misinterpretation For KFC UK&I, the team can now center conversations around work items and business goals. The teams are able to view the same data and trust in its integrity. This shift promotes a true team mentality, focusing not only on execution, but also on intentional collaboration and alignment with business objectives.  This aspect, often overlooked in many companies, is now a focal point for KFC UK&I, enhancing overall team effectiveness and cohesion. " It’s great to see the recent releases [to ActionableAgile™️ Analytics ], such as the ability to share datasets. Even though I am not in the weeds of the day-to-day [in teams], I can still at least sing from the same hymn sheet that the scrum masters are working from, rather than second-guessing the data filtering ."  Actionable data to paint a bigger picture Data should serve a purpose and, above all, be actionable. If you stop at consuming a chart, you are robbing yourself of huge rewards. The big win is using this data to have the right conversations at the right times, so you can take the right actions and make the right decisions.  That is why, across all teams, at least once a quarter, the Agile Guild team at KFC UK&I will now take a snapshot of data from ActionableAgile™️ Analytics and complement with narration and context around that data.  This ensures everyone knows how the data is being interpreted, sees the context that goes into that interpretation, and allows deeper discussion among everyone who consumes it. " We might see a 5% decrease in throughput - okay, well, that is a number. But actually, the context applied brings the meaning and driving action.  We are able to take data and have meaningful conversations as a collective. We tried x and saw y improvement – in doing so we look to standardise, consolidate, and normalise practices if there is a shared consensus that there is value and benefit from it. "  Supporting real conversations with stakeholders By providing accurate and transparent data, ActionableAgile™️ Analytics has built greater trust among team members and stakeholders in the insights derived from the data. There has been an enhanced ability to have meaningful, data-backed conversations with business partners and stakeholders, aligning them to the value stream and helping ensure everyone is on the same page. An example of this can be seen during a recent quarterly review process meeting, where the team presented data with context in the following areas: Arrival Rate vs. Throughput '24  – Using ActionableAgile™️ Analytics , the team demonstrated that 36% more work had entered the backlog over the quarter. It's encouraging that, alongside this increase, throughput also rose by 16%, meaning work is flowing smoothly through the team, and they are managing the higher demand without causing significant backlogs. However, this will be something to monitor closely going forward. Cycle Time  – ActionableAgile™️ Analytics highlighted that the team’s cycle time had increased by 27% during the quarter. While this could be alarming to stakeholders without context, the team was able to explain that the increase was due to external factors, such as a recent code freeze and reduction of team " 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. "  Continuous improvement using data With one of the main drivers being continuous improvement, KFC UK&I run a quarterly Kaizen (that’s Japanese for improvement) event through a two-week sprint focused on identifying problems and fostering innovation throughout the business. ActionableAgile™️ Analytics has provided supporting data used during this period and daily throughout team level Kaizen. Results by the numbers Having implemented ActionableAgile™️ Analytics some years ago we took a look back at how KFC UK&I has progressed on their journey over the last 2-3 years. Let’s look into how some numbers are around their Product Value Stream goals. Has KFC UK&I been able to cut down on down time while being more efficient, predictable and deliver more? Overall Average Flow Efficiency : This is the time work is spent idle (waiting) as opposed to actually being worked on.  2021 : 69.5% 2024 : 77.6% (+7.95%) Overall Cycle Time:  Cycle time is a key performance metric, reflecting how quickly work moves through the system. Shorter cycle times indicate efficiency in delivering work. 2021 : 9.3 days   2024 : 6.3 days (-3 days) What assumptions can we make from this? The increase in Flow Efficiency (+7.95%) suggests that KFC UK&I has significantly reduced idle time in their product value stream, indicating an improvement in how quickly work is prioritised and completed.  Furthermore, the reduction in Cycle Time by 3 days reflects that they are not only working faster but also have become more efficient at delivering value to their customers. These metrics strongly imply that KFC UK&I has optimized their process flows, minimized bottlenecks, and improved predictability, positioning themselves for sustainable, scalable delivery. Summary of Outcomes Automated data analysis, reducing manual errors and administrative burden. A standardized approach to tracking and analyzing metrics, ensuring consistency and reliability. AKA - a data source they can trust. Common data and language that teams can bring to regular Kaizen periods. Implementation of automation to identify and address aging work in progress, signaling and enforcing the cleaning of the backlog. What does the future look like for KFC UK&I? Finding their flow Having started with Scrum, Scrumban, and Kanban at the core of their agility, KFC UK&I has transitioned into a product-centered style of working, leading the teams to lean methods and applications to support flow and the associated flow metrics to help tell the tale. To support this transition, KFC UK&I has also begun using Jira Product Discovery alongside ActionableAgile™️ Analytics because these tools offer flexibility for strategic initiatives and prioritisation. " Over the last two to three years, we have been on a journey of stepping it up a gear in terms of driving and moving away from projects, and stepping into a product-oriented structure.  We have focused on forming value streams, and with this, a greater view of how we find flow in our product teams and overall alignment, to deliver value to our customers. "

You would be easily forgiven for answering “Mount Everest” to the title question.  After all, that’s what most of us are taught in school (assuming we were awake during geography class).  But if you have read any of my material over the years, you know that most of what we have been taught is misleading, if not downright wrong. To determine the tallest one, we first have to measure all of the mountains on the planet. And it turns out that measuring the world’s mountains is much more complicated than you might think.  In fact, over the years, there have been several contenders for the crown: In 1792, Mount Chimborazo (Ecuador) was thought to be the tallest Then, in 1808, it was Mount Dhaulagiri (Nepal) Then, in 1847, it was Kangchenjunga (Nepal/India) It wasn’t until 1852 that the peak we now know as Mount Everest was officially named the tallest mountain in the world. Why did it take so long to name a winner?  As you’ve probably guessed, changes in technology, measuring methodology, and even changes to the earth’s crust due to plate tectonics have all contributed to updated rankings over time (as an interesting side note, because of plate tectonics, Mount Nanga Parbat is currently growing faster than Everest and in 241,000 years will be the world’s tallest, ceteris paribus).  But in reality, the hardest thing about measuring a mountain is defining what it is. In other words, when we call something a mountain, we first need to decide where it " starts” and where it " finishes.”  To most of us, spotting where a mountain “peaks” is fairly obvious.  Just look for the one above all of the other ones.  But finding the top is only half of the equation.  It’s deciding where a mountain starts, that is the tricky bit.  For example, Mauna Kea is the highest point in Hawaii—that’s easy to see.  However, more than half of Mauna Kea's mountain is submerged.  Measuring Mauna Kea from its base (underwater) to its summit gives 10,211 meters—which is about 20% taller than Everest. If we only consider mountains with a base on land, then Denali in Alaska is actually “taller” (with a base-to-summit measurement about 1,000 meters more than Everest). So why have all of us been taught that Everest is the tallest?  That’s because when measuring mountains, scientists generally use a “sea level” hack as the starting point for all their calculations.  Why is the sea level a hack?  As you may have guessed, the sea isn’t so level.  The following graphic from the European Space Agency is a brilliant illustration of this point: Due to factors like the earth’s rotation, tides, differences in the strength of gravity at different spots, etc., the sea actually has dramatically different levels around the globe.  Scientists attempt to “smooth out” these differences by calculating an arithmetic mean to use as “sea level.”  That’s right, they use an average.  And as we all know, an average is (generally) a hack.  Therefore, Everest gets its award due to an arbitrary starting point for a calculation that creates a baseline that doesn’t in any meaningful way exist anywhere on Earth. I don’t know about you, but that doesn’t seem quite right to me.  Why not pick a more objective starting point to measure from the center of the earth?  If we do that, then our good friend Mount Chimborazo, whom we saw earlier, regains the crown (due to the fact that the planet bulges around the equator). I’ll leave it up to you to decide what you think the tallest mountain is, but the point here is that all measurements require agreement on what started and finished mean.  And you can get radically different answers depending on those agreed points.  Flow metrics, in particular, are not immune to this difficulty.  When they think about predictability, most people think only about the ability to answer the question, “When will it be done?”  But that question ignores half the problem.  How long something will take depends BOTH on when something starts AS WELL AS when something finishes.  Your process could be Everest, Mauna Kea, or Chimborazo, depending on how you look at it. Therefore, take the time to carefully consider what “started” and “finished” mean in your context.   Your customers—and your predictability—will thank you for it. *This post was inspired by and based on the “Be Smart” video series on America’s Public Broadcasting Service (PBS).  If you haven’t checked that series out, do so.  Most videos are available on YouTube.

August has been a busy month for ActionableAgile™ Analytics , with exciting updates and enhancements designed to improve your user experience. From streamlined data loading processes to critical bug fixes and new control features, here's a rundown of what's new this month. Enhanced data file loading experience Starting this week, we’re rolling out a significant upgrade to the process of loading external data files across all versions of ActionableAgile™ Analytics. The improvements address longstanding challenges, ensuring a smoother and more intuitive experience. Why the Update? Our previous data loading system was often restrictive, leading to frustration as users encountered unclear error messages and cumbersome formatting requirements. To resolve these issues, we’ve implemented the following key enhancements: Simplified File Formatting:  We’ve integrated direct links to file format requirements right in the loader, and added a download button for the template—eliminating the need to go to the documentation. Smart Column Mapping:  Users can now map file columns to specific types such as ID, Title, or Workflow Stage. While the system is designed to map certain column types, you have the flexibility to override our guesses. Date Format Detection:  We now show the detected date format (e.g., MM/DD vs. DD/MM), allowing you to verify and adjust as needed, resolving a common pain point for users. Comprehensive File Analysis:   After mapping, we analyze the entire file and identify any cleanup actions needed. Users can correct these for future uploads or proceed with loading the file as is. If there are rows with errors, we display the details and allow users to export the list, noting that any problematic rows will be removed if they continue. These enhancements are designed to make your data loading process more seamless, allowing you to focus on what matters most—your analysis. Key Bug Fixes to the Beta Process Behavior Chart Starting this week, we're rolling out key bug fixes to the Beta Process Behaviour Chart across all versions to improve readability in dark mode and correct inaccuracies in tooltip data. Saved Data Sets for Azure: Major Update Work on Saved Data Sets for Azure is progressing well, with the goal of eliminating the need to recreate data set configurations. Stay tuned here or subscribe to our newsletter for beta release updates.  Global Date Range Control To view a segment of your data set, you currently need to apply date range changes to each chart individually. With the new global date range control, you can set the date range in one place and apply it across your entire data set at once. This feature is next and on its way. Stay tuned for more updates.  Additional Display options for stalled or blocked items Our current dimming feature is designed to highlight non-stalled or non-blocked items but lacks support for users who need to see all items while distinguishing between them. The upcoming feature will offer new options for displaying stalled and blocked items in charts, supporting more use cases. Penetration testing underway for ActionableAgile™ Analytics for Jira Cloud We’ll be making the report available for download via our Trust Center  as soon as it is ready. Did you know that you can subscribe to our trust center to be updated of changes and additions as they happen? Explore Our Product Roadmap and Shape the Future We invite you to explore our  Product Roadmap —your go-to page for all things development! Here, you can get a behind-the-scenes look at what we’re working on and have a say in what comes next. Under Consideration:  Explore the ideas and features we’re currently evaluating based on user feedback. Planned:  Get a sneak peek at the features and updates that have been prioritized and are scheduled for development. In Progress:  Stay updated on the features that are actively being worked on, and track their progress. Released:  Check out the latest features and improvements that are now live. Submit Your Idea:  Have a feature in mind that you’d like to see? Submit your ideas and help shape the future of ActionableAgile. Visit our Product Roadmap  today to stay informed, give feedback, and make sure your voice is heard. Together, let's build something amazing! Stay tuned for more updates and continue exploring all that ActionableAgile™ Analytics has to offer.

In today's fast-paced software development environment, achieving predictable workflows and rapid delivery is essential for developers, agile coaches, project managers, scrum leaders, and leadership alike. ActionableAgile™ Analytics offers a solution that enhances efficiency, reduces delays, and boosts productivity, ultimately leading to substantial time and cost savings. The Problem Software development's primary challenge is the necessity for predictable workflows and rapid delivery. This issue affects all roles, causing misalignment within teams, frequent delays, and a diminished ability to adapt swiftly to market changes. Repercussions include lower customer satisfaction, delayed product releases, and financial losses. The Importance of Predictability and Speed According to the 2023 'The State of Agile' report by Praecipio, a leading Atlassian Solution Partner, velocity (the amount of work delivered in a given time frame) and predictability (the ability to accurately forecast delivery times) are critical metrics for measuring team performance. Teams that can deliver predictably and quickly are better positioned to meet customer expectations and achieve business goals. Case Analysis of a UK-based Financial Services Firm implementing ActionableAgile™️ Analytics Current Situation Significantly Low Productivity:   The team faced extended lead times and cycle times. High Work-in-Progress (WIP):  An unusually large amount of WIP for a single team. Challenges in Prediction:  Difficulty in accurately forecasting completion times. Manual Tracking:  Cycle time is tracked through spreadsheets, leading to challenges. Desired Improvements Better Prediction of Delivery Outcomes. Utilization of Flow Metrics. Increased Planning Accuracy Time and cost savings on manual tasks. The result after five sprints? Over a year - What would the savings from automating cycle time calculations look like?  How do our users save time with ActionableAgile ™️  Analytics? Joanna Doyle, Agile Delivery Manager: "Before ActionableAgile™️ Analytics, I was nothing numbers within an Excel sheet. Now , it's really easy to discuss with stakeholders without having to explain what the numbers mean ." Alex Priestley, Product Leader at John Lewis / Pro Kanban Trainer: ‘We use the Aging Work In Progress chart during our daily standups to see in real time the issues to jump on and fix as a team. If we want to make decisions today that will make demonstrable improvements for tomorrow, that's my go-to'  Elaine Tittanegro Correa, Agility Transformation Director: "ActionableAgile™️ Analytics' proactive nature makes it a powerful tool for driving efficiency and addressing challenges promptly" Ben Parry, Partner & Integration Delivery Lead:  "The best benefit of ActionableAgile™️ Analytics is speed. The time between loading a board from JIRA and inspecting the related CFD is less than 5 minutes . There is no data refinement required." To sum it all up… ActionableAgile™ Analytics cuts through the clutter of misleading metrics, focusing on what truly matters—time and money. By leveraging Monte Carlo simulations for real-time forecasting and using the Aging Work In Progress Chart to highlight delays, teams can make informed decisions swiftly and streamline their workflow for better results.  Spend your time completing work, not forecasting, with ActionableAgile™ Analytics. If you or someone you know is suffering from a manual data entry prison, help is on its way! Start a free trial here.

In my 3.5 years of working with diverse teams across various industries, one thing is crystal clear—everyone hopes to leverage agile methodologies to enhance work delivery. With a plethora of options available, it’s puzzling why issues persist. Could it be that everyone is doing it wrong? Or could bad habits be holding them back from success? I’m betting on the latter, and I’d wager that you’re making at least one of these five mistakes: 1. A Poorly Designed Workflow A workflow, as the name implies, should illustrate how work progresses through your system. While this may sound straightforward, it can be complex depending on your team’s dynamics. There's no one-size-fits-all formula for creating the perfect workflow, but keeping a few key principles in mind can help you visualize the various stages your tasks undergo from start to finish. The primary challenge often lies in identifying bottlenecks and issues within the workflow. To address this, it is essential to find a method to measure the time it takes for work to progress from start to finish. To achieve more precise metrics for decision-making, design a detailed workflow that isolates specific stages of the work’s lifecycle. For example, by distinguishing between active and waiting stages, you can determine whether issues stem from prolonged waiting times or inefficiencies during active phases. Once bottlenecks are identified, you can collaborate with your team to investigate the causes and develop an action plan to improve the situation. 2. Lack of WIP (Work in Progress) Management Starting tasks without focusing on finishing them and allowing work to age without valid reasons will eventually cause problems. If you let items age, you'll eventually need to close these tickets and might be facing some that are several hundred days old, which can drastically impact your Cycle Times. If 85% of your tasks are completed within 13 days or less, imagine the repercussions of finalizing items that are over 100 days old! To prevent accumulating too many "old" tickets, discuss them with your team and decide on a course of action, such as moving them back to the backlog for resizing or splitting them up into smaller items. By using Work Item Age as an indicator of potential issues, you can initiate conversations early and address problems before they escalate. While it may not be possible to eliminate all old items, you will likely have significantly fewer on your board. Consequently, if items move faster through your workflow, your Cycle Times will decrease. Additionally, the fewer outliers you have, the more predictable you will become. 3. Having a Blocked Column This is the worst approach of them all. It doesn’t do what you think it does. You might argue, “But Margaux, it brings visibility to all currently blocked items and helps us manage the blockage better.” (Insert skeptical Margaux face). Yes, it does provide visibility, but does it actually help manage the blockage? To handle blockages or dependencies effectively, you need to identify where the blockage occurred in your workflow: Was it during the testing phase? The development phase? Or while waiting for UAT? You also need to know how long the item has been blocked because different durations may require different approaches (e.g., escalation). Moreover, if you have WIP (Work In Progress) limits, what happens when the item is unblocked but no slots are available in the workflow to address it? It will have to gather dust until a slot opens up, extending the time required to handle it. There are better ways to highlight blocked items without moving them to a different column or status (don’t do that either). For example, in Jira, you can flag an item, which will then change color to make it easily identifiable. This method provides more accurate analytics on time spent in each workflow stage, helping you identify bottlenecks where work often gets stuck. This insight can help you improve your processes and hopefully reduce blocked time. 4. Focusing too much on WIP Limits WIP limits are an effective way to maintain a balanced workflow, ensuring you neither overload nor underutilize your system. However, they can sometimes mask underlying issues in your process. Consider a scenario where your In Progress stage has a WIP limit of 10, and you adhere strictly to this limit. While you may not exceed or fall short of this number, the age of the items in progress becomes a critical factor. You might be advancing 6-7 items swiftly, but what about the remaining 3 that have stagnated for days? Merely observing WIP limits won't prevent work from aging. To manage your flow effectively, you must monitor both the quantity and age of your WIP. This relates to our third point: actively monitoring and managing the age of your work can resolve numerous issues. 5. Not Slicing Your Work When work takes too long, it might be because the task is too complex and could benefit from being divided into smaller yet still valuable pieces. What are the smallest deliverable components that still provide value? If a component doesn’t add value, should it even be worked on or released? Having this discussion ensures that you always focus on potential value and avoid discovering at the end of the sprint or project that half of the work was irrelevant. More refined tasks are likely to move smoothly through your workflow, ensuring a steady stream of completed work ready for release. This approach enables you to receive feedback much faster, allowing you to adapt and make better data-driven decisions. In Conclusion Let's recap the 5 points and use more familiar terms: #1: Bad workflow design- ineffective Cycle Time  management. #2: Ignoring WIP Management - overlooking Work Item Age . #3: Maintaining a blocked column - poor WIP  oversight. #4: Mismanaging WIP limits - inadequate handling of Work Item Age  and poor WIP  control. #5: Failing to break down tasks - poor Throughput  management. You might be thinking, "Wait a minute... aren't these the key flow metrics?" And you'd be right! Flow metrics are crucial to achieving better workflow and agility. They're a fundamental concept in Kanban, but they are equally beneficial whether you practice Scrum or SAFe. Definitions: Cycle Time: total elapsed time an item took to be completed from start to finish Work Item Age: total elapsed time since the work started WIP:  amount of work that has started but not yet finished Throughput: amount of work finished in a given time period Ultimately, these metrics help you better predict the amount of work that can be planned for the next PI or sprint. Imagine spending minimal time on metrics while confidently forecasting and achieving accurate predictions. It’s entirely possible! You don't have to choose between methodologies; you can integrate them to achieve your desired outcomes. Shake things up and give it a try 😄 Interested to hear more? Curious but not yet convinced? Feel free to reach out to me at support@55degree.se  or on our community at https://community.55degrees.se/ .

When you talk about forecasting with your colleagues you are apt to hear about a wide range of experiences and opinions. There is almost always a difference of opinion on how to actually determine a forecast and there's often disagreement on whether or not you should be forecasting at all. #NoEstimates anyone? I believe that both of these disagreements exist because traditional estimation processes are so time-consuming, and often inaccurate. At 55 Degrees we don't think forecasting should be avoided, but we do need to find a way to create forecasts that can work in uncertainty and doesn't waste time and sanity in the process. What forecasting technique does ActionableAgile use? The strategy we use in our flagship product, ActionableAgile Analytics, uses a technique called probabilistic forecasting. As the name implies it helps you provide a forecast that examines possible outcomes and the likelihood of each. This makes it perfect for situations where there's not a single obvious, inarguable outcome. A probabilistic forecast includes a range of possible outcomes and the probability you'll land on one of the outcomes in that range. Here are some examples: "We have an 85% chance of finishing a work item in 7 days or less." "There's a 95% chance we can finish 6 items or more in a sprint." "There's a 70% chance that we'll finish these 20 items on or before November 1." In ActionableAgile we figure out the range of outcomes and related probabilities by using your data and looking at the Cycle Time and Throughput of your past work. What can I forecast with Cycle Time? Using our Cycle Time Scatterplot you can forecast how long it is likely to take to finish a future work item based on how long it took to complete items in the past. Read more about our Cycle Time Scatterplot! What can I forecast with Throughput? While with Cycle Time you can create forecasts for individual items, Throughput allows you to create forecasts for larger efforts containing multiple work items. This can mean a number of things such as sprints, projects, releases, and more. Using your Throughput, our Monte Carlo simulations take the rate at which you finished work in the past to answer questions like "How many items can I finish by X date?" (Fixed date) and "When will this set of work be finished?" (Fixed Scope). Learn more about how our Monte Carlo Simulations work. What do I need to get started? If you haven't already, start a free trial of ActionableAgile - either in our SaaS version or install our app in your Jira or Azure DevOps instance. Then load in your data and you're off to the races. Our charts and simulations will automatically provide you with forecasts you can use based on your data. You can use the included chart controls to segment or filter your data any way you want to have ultimate control over what you're forecasting. We are always available to help via our support portal located at https://55degrees.se. We hope to hear from you soon!

One of the things leaders often say they want most is predictability. Fortunately there are 2 things you can do every day to be more predictable. Predictability is defined as the consistent repetition of a state, course of action, behavior, or the like, making it possible to know in advance what to expect. Dictionary.com If you read that definition carefully, you may notice that the words good or bad are missing. Predictability itself is value-neutral. Being predictable is the attainment of the skills required to allow customers and other interested parties to have the advantage of knowing what to expect. Now, we all can think of an organization that we think of as predictably horrible at customer service (erm… certain cable TV companies come to mind) or a restaurant that has predictably bad food. So, it is fair to say that leaders want value-driving predictability, especially in regards to an organization’s ability to deliver value to its customers on a regular basis. How can we tell how predictable we are? Pretty easily! You can tell how predictable you are just two pieces of data for each work item you finish: the date you started the work item and the date you finished it. Using that data, you can create a Cycle Time Scatterplot and get a very visual view of your predictability. Each dot on a Cycle Time Scatterplot represents a single piece of work that was completed and where the dot is placed tells us how long it took to deliver that piece of work. This duration is referred to as cycle time. If more than one item finishes on the same day with the same Cycle Time, you just make the dot bigger and write the number of items on the dot. In the beginning, when you have little to no predictability, the dots will be widely scattered all over the chart. As you learn how to become more predictable, the range of space the dots inhabit will become vertically narrower. Read our previous blog post “Quick and accurate forecasts with ActionableAgile” to learn how to use this chart to make your forecasts reliable, even when your work and circumstances aren’t varied. Two things you can do daily to be more predictable! Now, that you know how to determine your level of predictability, you probably want to know what to do to improve it! Well, there are two simple, domain-agnostic things that you can do each day to improve your predictability: Thing 1: Stop starting, start finishing One of the biggest culprits of lengthening Cycle Time is multi-tasking. Well, we think we’re multitasking but what we’re actually doing is something called task switching — putting one thing aside to work on another and then coming back to the first thing later. Implementing a limit to the number of things you will work on at a time can help individuals, teams, or even organizations focus on fewer items at a time, allowing them to deliver the items faster and, usually, with better quality. This limit is called a work-in-progress limit, or WIP limit, and is a key component of a Kanban system. These limits can be implemented per person, per team as a whole, or even by activity in a workflow or value stream as seen above. The important thing is that you establish a threshold and adhere to it as often as possible. So, each day, as you make choices about what to work on, finish what you’ve already started before you pick up anything new! Thing 2: Pay attention to the age of work-in-progress Once you have WIP limits in place you can start an effort to make sure no one work item ever sits and ages for too long. To do this, you need to be able to see how old an item is. If you are tracking your cycle time, you already know when you started an item and you probably know the current date. With those two data points, you can get a good picture of how old your work-in-progress is. That information can be used in daily meetings to ensure you prioritize work that’s in danger of getting old. Much like the Cycle Time Scatterplot, the Aging Work In Progress Chart has a dot for each work item but the axes are different. The Y-axis is the age of the item so far. The X-axis represents which stage of your workflow the item is in. The dot is placed at the intersection of these two data points. It is important to remember that all of these dots will (hopefully) end up as a dot on your cycle-time scatterplot. So, we want to make sure they don’t get super old and, even better, that they stay within an expected range. When you take the percentile lines from your Cycle Time Scatterplot and overlay them onto your Aging Work in Progress Chart, you can get a much more detailed understanding of how at risk an item is for finishing in your expected timeframe. The percentile lines tell you how likely your work is to finish by a certain age. So, if you are at the very beginning of your process for an item and it is already older than 60% of your items are when they’re finished, you know that item is at risk of negatively impacting your predictability. And, while one item may be nothing to freak out about, if this happens often enough, you become less predictable and have to provide longer estimates to your stakeholders. So, if you’ve been looking for a leading indicator of predictability, the Aging Work in Progress Chart is just what you asked for! Yes, you can really do it! What’s great about this is that you really don’t need to be a data geek and you don’t need to be using a specific methodology, framework, or toolset to improve your predictability. By focusing on finishing over starting by implementing WIP limits and working on older items first, you can greatly improve your predictability without a huge overhaul of your processes — though it might end up being the start of something quite transformative! If this topic gets you pumped up and wanting to learn more, consider taking a workshop offered by 55 Degrees AB or try out ActionableAgile for free!

Cycle Time is the total elapsed time it takes a work item to travel from one point of your workflow (a start point) to another point (the finish point). 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. Calculating Cycle Time Cycle Time is often expressed as: (Finish TU – 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? By adding one, this allows us to include both the start and the finish time unit in the Cycle Time so no time is left out. Cycle time is the total elapsed time it took from start to finish. This means it includes active working time as well any time that a work item is sitting there idle. So, whether work is waiting on someone, you’re blocked by a technical issue, or being interrupted by evenings and weekends, the time is included in an item’s Cycle Time. Sometimes teams visualize work time and wait time. It is all included in Cycle Time. Cycle Time cannot be calculated for a work item until it has reached your designated finish point. This means it is a lagging metric – one based on historical data. It is best to speak in the terms of your customer. They think in calendar days and not business days so we calculate cycle time in calendar days and not business days. It reduces misunderstandings. It's not inflating the metric, its reality. Julia Wester, Co-Founder @ 55 Degrees AB Why measure Cycle Time? 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. 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. Quickly looking at a Cycle Time Scatterplot chart above, in which each dot represents the Cycle Time of a given work item, we can easily say that we finish 95% of these work items in 23 days or less. This type of forecasting is reliable and extremely quick, allowing us to spend time on what’s truly valuable – doing the actual work. 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! Interested in tracking flow metrics like Cycle Time? Try out ActionableAgile for free today!

Neither does Throughput Many scrum teams use a metric called velocity as their primary metric. Velocity is commonly defined as the number of story points you finish over time, usually measured by sprints. The input of story points are assigned by scrum teams, often using the Fibonacci sequence, to pieces of work based on their relative complexity. A simple piece of work might be assigned 1 story point while a really complex piece of work could be assigned 13 points or more. A team’s velocity would represent how much complexity reached their definition of done in a given sprint. Measured from sprint to sprint, it becomes a sort of gauge of success. If it stays the same or goes up, that’s great. If it goes down repeatedly, there’s a problem that needs to be investigated. Teams using Throughput, another rate metric that counts the number of individual work items delivered instead of relative complexity, do the same. They measure throughput from across time periods, often sprints, and use it to determine their success is increasing, staying stable, or waning. No single metric defines success Unfortunately, success is more complicated than that and it can rarely if ever, be measured with a single metric. Most teams and organizations don’t just want to deliver things. They want to deliver good quality things and deliver them quickly on top of regularly delivering things. If you don’t see a distinction, let me share the story of a team I started coaching in 2019. This HR team came to me with a (perceived) Scrum process in place. They were using 2-week sprints for planning and retrospecting. But, they were still completely overwhelmed and unhappy. Though it could be higher, their throughput at least looked pretty consistent. To dig deeper, we looked at their Scrum board. The good news is that they understood their workflow. Their board had columns representing their entire workflow from planning to delivery. The bad news? It was chock full of work items. They were close to the end of their sprint yet many items were still in the sprint backlog. Despite the obvious fact that many of the items planned for the current sprint would not be finished by the beginning of their next sprint, they had already filled up the planning column for their upcoming sprint. Looking from multiple angles gives you a well-rounded picture In the end, it was clear that they suffered from a “too much WIP” (work-in-progress) problem. They started so much work that everything took much longer to finish than it would have needed if they had just started less work at one time. This was something we noticed almost immediately when we looked at a metric called Cycle Time. This is a measure of how long it takes a piece of work to be completed (you can define your start- and end-points for this metric). Using a chart called a Cycle Time Scatterplot, we could see that 85% of their work took up to 30 days to complete, while the remaining 15 took even longer! Now, remember their sprints were 14 days long. Houston, we have a problem! This team had unfortunately settled into a pattern of delivering a consistent number of work items — but they were very old work items. They hardly ever finished work items within the same sprint they were started in. By adding the Cycle Time metric to our arsenal we were able to get more insight into what was happening, find a problem, and start experimenting with solutions. In Summary The moral of the story is that one metric can be misleading at best, but it can be immensely harmful at worst. If you were to optimize for one metric without keeping an eye out for unintended consequences, you could end up causing bigger problems than you had when you started. When measuring team success, try to consider multiple aspects of success and establishing measures to represent each. Then look at them together so if you see one improve, you can make sure a negative impact didn’t happen elsewhere. For the curious, here’s what came next for our HR team: We identified 3 major things that would help this team improve their Cycle Time metric, and maybe even their Throughput metric too: Break work items down into smaller valuable pieces. Many of the items took so long because they simply had too many deliverables in them. Many process dysfunctions can be helped by breaking work down. Remove board columns that weren’t necessary. Every board column is a place to store more work-in-progress. There’s always a balance to be struck but it is ok to get rid of columns if they cause more pain than gain. This also meant we talked about not planning too early. Implement WIP limits. Using Velocity or Throughput as a planning tool for a sprint provides a WIP limit for your sprint backlog in that you limit the number of items you allow into a sprint. However, in a sprint, limiting the number of items you start at one time can often cause items to finish earlier and at a steadier pace throughout the sprint.

When you hear Monte Carlo you probably have thoughts of the Formula One Grand Prix and extravagant casinos. At 55 Degrees, when we talk about Monte Carlo Simulations we are talking about forecasting. 🤓 What is a Monte Carlo Simulation? Here's a great definition from Investopedia Monte Carlo simulations are used to model the probability of different outcomes in a process that cannot easily be predicted due to the intervention of random variables. It is a technique used to understand the impact of risk and uncertainty in prediction and forecasting models. (Investopedia) Monte Carlo simulations are widely used in many industries whenever uncertainty exists. Investment firms use them to project potential earnings across different investment options. Insurance companies use them to forecast risk in different populations or areas. Companies like yours can use them to forecast when you'll finish a group of work! Simulations help make sense of uncertainty Rarely can you give someone a forecast and be absolutely, positively certain that you'll be right. There are just too many uncontrollable factors that can get in the way. When we are uncertain about something, one way to learn more is to run experiments. Are you unsure how likely it is that a flipped coin will land on heads? To find out, you can flipping it over many times and use the results to calculate the likelihood. Do you need to know how long it will take to complete a specific work item? You can use your past cycle times and calculate how long it's likely to take. Do you need to know when you'll finish a group of work instead of a single item? That's a bit more complex! Instead of manually doing groups of work repeatedly in real-time, we use Monte Carlo simulations to simulate doing the work thousands of times. Fortunately, it only takes seconds! Two questions you can answer When you're forecasting with Monte Carlo simulations you're likely trying to answer one of these two questions: When will this specific number of work items be completed? (Fixed scope) How many items can we complete by this specific date? (Fixed date) Projects aren't the only situations that require us to answer these questions. You may need to forecast when you'll get to an item that's near the top of your backlog or you may need to help decide how many items to plan for in your upcoming Sprint. I'm sure you can think of more if you stop and think! Running a Monte Carlo simulation To run a Monte Carlo simulation you'll need to provide a few things: A start date - Just as your GPS can't tell you when you'll arrive without knowing when you'll be leaving, the Monte Carlo can't tell you when you're likely to be finished if you don't provide a start date. Throughput data - The simulation samples real Throughput data to project how much you might finish on each day of every trial run by the simulation. There are thousands and thousands of these trial runs in each simulation. The outcome of each of those trial runs is recorded and allows you to calculate the odds of what might happen in the future. The fixed aspect - This is the desired end date when you have a fixed date or the number of items when you have a fixed scope. Using the results to create a forecast The tool you use for running the simulations controls how the results are presented. No matter how it is presented, it should provide you with the tools to create a probabilistic forecast - something like "There's an 85% chance that we'll finish in on or before August 4th." or "There's a 90% chance that we can do 15 or more items by ." Like those above, every probabilistic forecast should have two pieces of information (shown in bold above): a probability a range of outcomes The results can be shown in various ways depending on the tool you use. Here are two that we use in ActionableAgile: Histogram view - this is what you might think of as the raw data view. It shows the different outcomes that happened and the number of times each one happened. With this chart, it is quite simple to calculate probabilities. To find the 50% line, simply find the place where 5000 trials have taken place and then draw a line. For 85%, keep going until you find 8500 trials and do the same. Calendar view - this is a user-friendly view of the information in the histogram. This view makes it simple to talk about other dates what it might take to change the odds of hitting those. Considerations to keep in mind Monte Carlo simulations only factor in conditions that were present when you generated your historical data (team size, skill set, work policies, etc.). If those conditions significantly change, you'll need to generate new data under the new conditions in order to have reliable forecasts using Monte Carlo simulations (as you would with any forecasting method that uses historical data). Monte Carlo simulations often use random sampling of your historical data during the trial runs. When this is the case, as it is with our products, this means that every data point is as likely to be randomly selected as another. If you deliver 0 items most days, then most days in the simulation are likely to also have 0 delivered. In other words, you can deliver 10 items in the span of a week, but it matters how predictably those are distributed. You'll get better results if you work to have a more consistent delivery of a couple each day than aiming to deliver nothing for 4 days and 10 items on the fifth. To improve your predictability, and the resulting forecasts, focus on reducing Work Item Age. Want a tool to help you get started with Monte Carlo Simulations? You've definitely come to the right place. 55 Degrees offer two different products that use Monte Carlo Simulations to provide forecasts: Portfolio Forecaster for Jira and ActionableAgile (available as a SaaS app or embedded in Azure DevOps and Jira). In ActionableAgile you can use Monte Carlo Simulations to provide forecasts for fixed scope and fixed date efforts. Portfolio Forecaster uses Monte Carlo simulations to forecast your Jira Epics or Versions, taking into account how many are in progress at one time and your historical Throughput. Start now by trying either product for 30 days at no cost. We're always here to support you if you have any questions. Happy forecasting!