devopsy.kinsta.cloud

Author: Jeff Keyes

  • Why You Should Rip Up Your Org Chart and Reorganize Around Value Streams 

    Why You Should Rip Up Your Org Chart and Reorganize Around Value Streams 

    To compete in the digital economy, teams must optimize both efficiency and effectiveness–it’s a tough ask. The traditional dominator hierarchy is a problem because organizations that use command and control create silos and handoffs that stifle innovation at the team level. Coordinated autonomy releases the creative spirit of individuals–but how can leaders assure alignment with their vision?

    One way to eliminate the delays and waste associated with silos and foster autonomy is to arrange around value streams. A value stream is anything that has a customer or anything that delivers a product or a service. All value streams begin with ideas and end when a change is delivered. Or, even better, when our cherished customers realize the intended value outcome. The original intent of Agile was to provide “value” to our customers faster … but we hardly ever talk about what this value is or measure its existence.

    Traditional organizational charts arrange resources according to expertise. This means that work is handed off to different teams as it progresses through the value stream. Value streams exist whether or not people call them that and whether or not they actively manage them. Every time a hand-off occurs, it creates an opportunity for delays or errors. It also means it’s hard for people to understand the end-to-end system. Instead, they do as they are told to optimize their piece of the puzzle without really understanding how their actions impact work that’s upstream or downstream.

    More progressive ways of working, like Agile and DevOps, do promote cross-functional, autonomous teams, but they typically leave a big gap between planning and delivery and are not always smart at connecting feedback from customer experience back into planning. Value stream orientation promotes team diversity by recognizing the individual background (cross-functionally) into the value delivered to the customer.

    Organizing around value streams is the foundation for nurturing the value stream-centric ways of thinking and working that drive both efficiency and effectiveness. Then, customer experience is improved and higher levels of organizational performance are achieved. This is because:

    • Putting customer experience front and center brings higher retention and referral rates
    • Elevating the concept of flow (see below) gives teams permission to prioritize removing waste
    • Balancing innovation with improvements reduces technical debt, toil and unplanned work so employee experience is better, too
    • Connecting planning to delivery to feedback in a continuous loop aligns teams and helps with decision-making

    Flow is about how fast ideas can move through a value stream and be made available to the people they’re designed for. It’s about outputs and efficiency. Customer experience tells teams about the outcomes of their work and their effectiveness. Used together, these measures ensure the teams are doing the right things right and moving in the right direction.

    In this model, leaders share their authority and empower their teams. They support teams’ self-discovery of improvements and make sure those local learnings go global. They help their teams understand how much time they spend innovating compared to reacting or improving. Then the leaders can help their teams reduce distracting and stressful unplanned work and instead invest in increasing capacity for innovation in the future.

    Follow these steps to become a value stream-centric organization:

    1. Identify your value streams
    2. Identify who is involved in each step of your value stream
    3. Make your value stream visible by visually collaborating with your value stream team
    4. Use tooling to gather value stream data and generate insights for your team
    5. Meet regularly to inspect and adapt your value stream

    All organizations are composed of value streams, regardless of whether they have been made visible, and most large organizations have complex networks of interconnecting value streams. These dependencies are not easily decoupled. They’re not easily visible, either. But once you’ve mapped them, you can start seeing where they connect and manage the dependencies. Ultimately the goal is to break these dependencies since they cause delays and risk, but it’s hard to do that when they can’t be seen.

    Connecting the organizational vision with the value streams becomes much easier with this model. Organizational structures are flattened. Leaders understand which value streams they are accountable for and how they work. The time between having an idea and getting feedback on how it’s received is shorter and amplified. They can see the work being done, measure what impact it’s having and make better decisions about what to innovate or invest in next.

  • Measuring Value Streams by Analyzing Flow Metrics

    Measuring Value Streams by Analyzing Flow Metrics

    There is increasing pressure and demand for IT administrators to bring software to market quickly and cost-effectively. To achieve that goal, IT departments frequently compile various metrics that measure specific development processes.

    Data and metrics are important tools, but administrators sometimes don’t think to look at projects through a wider lens—by measuring total production workflows. This can curb production and adversely affect business outcomes as inefficiencies go undetected across the value stream.

    In other words, total visibility across the entire value chain is needed to accomplish lean project management and optimize DevOps workflows.

    Let’s talk about the different kinds of flow metrics, why they matter and how to begin measuring them.

    Flow Metrics: What do They Mean?

    Flow metrics indicate the rate of value delivery with regard to desired business outcomes. This allows organizations visibility into production at a macro level so that they can make more effective decisions. Flow metrics can also produce an analysis of historical performance over time, which can be valuable for businesses that focus on continued growth.

    IT leaders now widely use flow metric analysis to bridge the gap between business teams and technology, because it can uncover a variety of problems that affect critical objectives. For instance, teams can identify prioritization challenges, why projects take longer than expected, pinpoint technical debt and more.

    When evaluating flow metrics, IT departments measure individual work units—also known as flow items—that clients or product teams hand over to their colleagues. This may be a request for a new software feature, a security flaw or something that needs a fix.

    What Are the Different Kinds of Flow Metrics?

    With a better understanding of flow metrics, we can now delve deeper into the five flow metrics that organizations should assess throughout the development process.

    1. Throughput

    Throughput measures the rate at which total number of flow items are completed. By measuring this, throughput can determine the total amount of value attained and distributed during a specific project.

    Throughput is generally measured week-over-week. Measuring and evaluating throughput on a historical basis also helps work out how clashes in priorities affect value overall.

    Optimizing throughput results in greater end user experiences and customer satisfaction, which naturally drives revenue and growth.

    2. Cycle Time

    Cycle time is the speed at which an IT team can deliver value. This means timing how long it takes for flow items to progress from work start to work complete, taking into account active and wait times.

    Cycle time is just as important as throughput when it comes to driving revenue and growth. Measuring cycle time can streamline the process of determining project estimates and allocating resources, resulting in stronger workflows and enhanced efficiency.

    3. Flow Efficiency

    Flow efficiency is the proportion of active time to wait time. Measuring flow efficiency uncovers where roadblocks are happening and whether process waste is rising or falling. The desired flow efficiency rate should be higher than 15%.

    Most flow time comes from waiting, resulting in roadblocks and increased production costs. Measuring flow efficiency can enable teams to pinpoint when workflows are taking too much time, and they can then take active measures to accelerate individual processes.

    4. Work in Progress

    Work in progress involves measuring demand. Teams achieve this by measuring the amount of flow items either pending or in active development within a value stream. Measuring work in progress generally enables IT leaders to identify whether value streams are overused or underused.

    Nobody’s perfect. That’s why leaders have to be conscious of how each employee’s time is being used. By doing so, they can systematically assign workloads and enhance quality where required. This, in turn, lowers burnout and turnover while also cutting down on mistakes that need to be reworked.

    5. Work Profile

    Measuring work profile is important when determining priority alignment. It’s analyzed as a ratio of the four other flow items completed in a specific time period.

    Assessing work profile allows for optimal quality and productivity. This raises customer experience (CX), which results in better retention and reviews.

    Why Should I Track Flow Metrics?

    In the past, DevOps professionals largely went without the necessary tools for tracking and enhancing flow metrics, but recent improvements in tracking these metrics have made the process much more efficient and easy.

    There are three key reasons for flow tracking:

    1. Boost Communication

    A lack of communication can be a common issue between business and software teams. Flow tracking enables IT administrators to delve into flow metrics and gain insight into the value stream. This helps teams provide accurate project estimates along with real-time updates, which makes communication with stakeholders much easier.

    2. Determine Desired Business Outcomes

    Having total visibility across a value stream helps IT and business leaders cooperate on identifying and prioritizing key projects and establishing goals.

    Improving software quality could be a goal, for example. Teams can examine various flow metrics to determine what is causing issues in software quality, how to fix those issues and how prioritizing adjustments to software quality could affect other workflows.

    3. Act on Intelligence

    Software organizations spend a lot of time and money attempting to expedite software development. They hold meeting after meeting, hire consultants or even bring on new team members, but the solutions they need to streamline workflows can be simple. They just need to be discovered.

    Organizations can gain total and real-time visibility into the value stream with flow tracking. This makes it quick and simple to act on intelligence and enhance development, leading to accurate, data-driven decisions that don’t require guessing.

    Elevate Your Value Stream With Flow Metrics

    Achieving optimal efficiency in workflows requires end-to-end visibility, which is made easy by analyzing flow metrics.

    Using flow metrics to maximize the value stream improves visibility, accelerates development and cuts software production costs.

  • The Digital Transformation of Decision-Making

    The Digital Transformation of Decision-Making

    The ability to make effective business decisions has historically relied on factors such as experience, qualifications, talent and, in many cases, luck. Strategic and tactical choices are also informed by ‘borrowing’ the insight and knowledge of others. In many situations, ‘follow the leader’ can be an effective approach, and one need only study any sector of industry or commerce to see organizations everywhere mimicking each other, with competitive advantage based on relatively small acts of incremental innovation.

    Today’s ambitious businesses have many more options at their disposal to inform decision-making than previous generations. The extent, for instance, to which businesses can now collect, store and analyze data has been transformed by improvements in technology. The result is many businesses are now sitting on enough data for advanced analytics to inform smarter, more efficient decisions.

    The Role of Advanced Analytics

    Stepping back for a moment, advanced analytics is a set of tools and techniques that use existing data to develop insights to inform business decision-making – in particular, improving the accuracy of predicting future events based on certain actions. In practical terms, it is particularly useful when organizations are planning to implement a new business activity, where it is employed ahead of legacy processes that simply can’t deliver the same levels of insight.

    Online shopping is a prime example. When a consumer clicks on various items on a website, they will often be given other suggestions – sites often will present this information with the words ‘Frequently bought together,’ for example. This is based on what has already been viewed and uses advanced analytics to predict what else the shopper might be interested in. It’s just one of many ways retailers now rely on advanced analytics to intelligently cross-sell, and as many people will know, it’s very effective.

    In a wider sense, advanced analytics can be used in a huge variety of scenarios, irrespective of industry, market or type of customer. In doing so, businesses stand to reap a number of potential benefits, including:

    • Smarter decision making. By generating data based on strong logic and reliable information, advanced analytics can draw on both past and present data to make more reliable, more confident choices about what to do in the future.
    • Focused knowledge. By understanding the factors that improve business outcomes alongside those that are negligible, leaders can focus their attention and investment on what is more likely to deliver the desired impact.
    • Risk management. In an era of heightened risk, and the need for greater risk management, advanced analytics can identify risk based on available data, helping to inform business models that minimize risk, or help businesses make decisions based on insight rather than hope. This can also help improve governance and mitigate regulatory and compliance risk.
    • Strategy automation. Because advanced analytics takes care of analyzing data, teams have greater flexibility and time to focus on wider strategic considerations, which can help make the business better.

    Advanced Analytics in Action

    Advanced analytics employs various techniques across an ever-increasing range of use cases. For example, data mining is applied to ensure organizations make use of the most relevant data, extracting what is useful from the raw data. This data can then be subjected to further analysis for a multitude of objectives, such as understanding customers better or building more effective strategies.

    In addition, ‘big data’ analytics can be applied to both structured and unstructured data, and can be particularly helpful when the data generated isn’t in a proper format, or for organizations with massive data volumes to process.

    Then there’s predictive analysis, where machine learning algorithms are applied to current and past data to identify relationships and patterns. The algorithms then build a prediction model that can inform decision making. In general, the more data that is available, the more accurate predictions will be. It can’t, however, deliver 100% accuracy, and random, unpredictable factors such as a mass healthcare crisis illustrate the obvious limitations on predictive analysis.

    Taking this a step further, prescriptive analysis helps determine the best way to implement a business decision. Even when a decision is close to being made, there can be more than one possible implementation path to follow. Prescriptive analysis uses current and past data to suggest possible outcomes before actions are set in stone, recommending the best implementation options.

    Advanced analytics is a rapidly growing niche of the technology industry, regularly described by industry experts and commentators as among the most important, transformational IT trends. Given the pivotal role of decision-making across every organization, this perhaps shouldn’t come as a surprise, and for businesses everywhere, it’s now only a question of when they apply it to improve their fortunes, not if.