Skip to main content
flow metrics
Last Updated Nov 18, 2021 —

Need for Speed: Flow acceleration for software-driven business value

Organizations should recognize that accelerating flow is more important than ever for successfully delivering value. 

Accelerate software driven innovation
and eliminate bottlenecks with AI-powered analytics

Hear from Forrester and Digital.ai how leading organizations can use AI-powered analytics to predict, manage, and gain visibility into their software delivery lifecycle.

Watch now
AI-Powered Analytics

In today’s world, the rate at which digital technology is transforming organizations is increasing rapidly. With many companies adapting to a remote workplace, the need for a successful digital environment is more important than ever. It is essential that companies stay agile enough to not only produce but innovate. So, what can businesses do to ensure they have a competitive edge?  

The answer is flow metrics. 

What are flow metrics? 

Flow metrics measure the flow of business value through every activity involved in producing business value through a software value stream. Business value refers to the net benefit realized by the customer. These metrics can help provide a clear indication of whether or not an organization’s value stream flow is sufficient to support its desired business outcomes.  

The four Flow Metrics for measuring value streams are: 

  • Flow Velocity determines whether the value delivery is accelerating. 
  • Flow Efficiency identifies when waste is increasing or decreasing within your processes. 
  • Flow Time measures the time to market. 
  • Flow Load monitors if your work in progress is overloading teams. 

Together, these metrics center around the principle that all software-related work, from design to delivery, must create value for the business. It’s essential to measure each metric and take advantage of the data provided in tandem, as measuring only one flow subset will result in optimizing a single segment of a value stream. 

Flow acceleration in value stream management 

Utilizing an end-to-end AI-powered value stream platform can help orchestrate the delivery of software-driven business value. To ideally accelerate flow, an organization requires a solution that plans and creates lenses, releases and deploys lenses, monitors threat analytics, integrates and tests lenses, and operates and monitors lenses. 

“Each solution on their own are [beneficial], but each one on their own are still just islands of automation,” says Joe Foley, Insight Architect at Digital.ai. “You need more than just these islands of automation; you need the ability to bring all that data together to actually get good insight.” 

The data from these individual systems comes together with the proper use of an end-to-end platform to create a standard set of flow metrics. The metrics, and the data required to calculate them, is gathered together in lenses and are presented in a way that makes reporting and visualizing trends simple.  

These lenses provide all the standard metrics, but at the end of the day, they are still just standalone metrics that won’t necessarily solve problems. That’s where Digital.ai’s intelligence solutions come into play. They are built on top of those lenses and provide purposeful analyses built on insights that help your organization get to specific opportunities in reaching your goals. 

Increasing complexity holds back the flow of delivery 

Once you have all this information in a centralized repository, what can you do to keep the flow of delivery from being held back from the siloed teams? Some questions to consider include: 

  • How can we reduce feature cycle time? 
  • How can we identify bottlenecks and dependencies across teams? 
  • Which activities consume resources but don’t add value? 
  • How can we standardize and streamline our release processes? 
  • What factors are predictive of improving flow and on-time delivery? 

Questions like these are not easily answered with the data from a singular solution. You need the ability to bring the data together from the various tools into a common view in order to properly answer. Flow acceleration requires a comprehensive set of analytical capabilities starting with identification, to investigation, to correlation, and finally ending with prediction to be brought together in a unified data model. 

Key component #1: Predict and reduce cycle times 

One of the main critical challenges that teams face when accelerating flow is attempting to predict and reduce cycle times. The first step to accelerating flow is identifying the central issues. Stop to think about which of your upcoming features–or stories or epics–are at risk of missing scheduled end dates, as well as what features are contributing to this risk. Teams typically have a target timeline in mind so that their feature will be finished in accordance with other teams features to meet a business objective. Knowing which features are at risk of missing the target date allows for you to be proactive.  

With a proper solution, you can benefit from machine learning to generate predictions and take into account dozens of factors that contribute to the issue. Using this data will allow your team to take targeted action ultimately creating throughput by predicting and reducing cycle times as well as increasing predictability by preventing delays. 

Key component #2: Detect and eliminate bottlenecks 

There are various reasons why bottlenecks can occur in such a complex process. Some of the most common sources of these bottlenecks include blocker dependency hotspots, CI/CD pipeline blockages, and team members being over assigned with tasks tracked in multiple systems. With an end-to-end value stream platform, you can collect the data all in one place and pinpoint which areas are at risk of creating those bottlenecks. An end-to-end value stream platform will consist of all the necessary tools needed to accelerate delivery, improve business agility, and make data-driven decisions that will help scale DevOps and SAFe across your organization. 

With that information at your disposal, your teams can take preventative action. They can prioritize tasks to resolve the blockers causing bottlenecks, improve peer response, streamline long-running builds, and adjust assignments for overallocated team members. Knowing exactly what the problem is will move the needle by eliminating the bottlenecks that are slowing down the most work. 

Key component #3: Reduce non-value-added work 

Non-value-added work sounds like a contradictory statement. All work being done will ultimately be valuable to somebody. For example, if the primary goal is to increase customer retention and market share, then it's crucial to focus on the contributing factors to meet that goal and not get distracted by different initiatives. Other forms of non-value-added work consist of needing to redo something already done, such as reopening a defect. 

One way to achieve this is to reap the benefits of your planning tools to ensure teams are doing work that is aligned with the priority at hand. It will surface which value streams have the most non-value-added work and the impact those implications have on accelerating flow. In the end, your teams can track the progress of reducing non-value-added work over time, subsequently increasing agility and reducing cost. 

Key component #4: Increase automation 

Organizations today are very focused on automating. Although crucial, this comes with the risk of potentially automating the wrong things or having teams that are incongruous on where they are in the automation pipeline. Using an end-to-end platform that collects data from the most common types of automation (Build, Test, and Deploy automation), you can identify which value streams have the most success in accelerating flow through increased automation. 

Your teams can take action by having the right information to analyze how much impact automation has on key flow metrics and track progress in increasing automation over time, which will serve your system-wide goals through improved accuracy, reliability, and consistency with targeted learning. 

Key component #5: Preventing late deliveries, effort overruns, and spillover 

The final component of accelerating flow is all about preventing late deliveries, effort overruns, and spillover. It centers around the question of whether or not you are setting plans and also achieving them. It’s imperative to set goals and plans for what you intend on accomplishing, how much effort it will take, and how much of it will successfully get done. Forecasting these factors inaccurately will cause issues further down the value stream. 

By completing tasks perpetually late, or even finishing them early, can leave others unprepared. This problem can be avoided with the help of a flow acceleration solution that increases predictability, allowing your teams to prevent delays and zero in on the areas that need attention the most. 

 

To learn more about how your organization can accelerate flow, check our webinar “Accelerate software driven innovation and eliminate bottlenecks with AI-powered analytics.” 

More from the Blog

View more
Aug 23, 2021

Is Data Analytics Missing From Your Digital Transformation?

DevOps
Nearly every major enterprise is already in the process of digital tra ...
Read More
May 27, 2021

Leverage analytics to improve change management

AI-Powered Analytics
One of the main objectives of agile product management is to speed up ...
Read More
Contact Us