Last Updated Feb 23, 2022 —

In today’s completely digital world, organizations and individuals alike are acutely aware of the speed of change and how it affects them. Read on to discover the importance of DORA metrics and how AI/ML can make DORA metrics more actionable.

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As American engineer Edward Demmings once said, “It is not necessary to change. Survival is not mandatory.” No matter your situation, it’s undeniable that people have been struggling to keep up with the speed of change and its effects. With a year of change like no other, there have been countless enterprises that have fallen victim; but there have been many others who have emerged victorious.

Although Demmings’ notion may be true, survival is certainly recommended for individuals and organizations alike! So how can organizations keep up and adapt to these new conditions? A good first step is to start measuring your organization’s DORA metrics and leveraging them to their fullest to advance your organization’s digital transformation.

What are DORA metrics?

Founded by the DevOps Research and Assessment (DORA) team, the program’s goal was to understand the practices, processes, and capabilities that enable teams to achieve high performance in software and value delivery. The four core DORA metrics are:

  • Lead time: Measures the total time between when work on a change request is initiated to when that change has been deployed to production and thus delivered to the customer
  • Change failure rate: Measures the rate at which production changes result in incidents, rollbacks, or failures
  • Mean time to recover: Measurement of the time from an incident having been triggered to the time when it has been resolved via a production change
  • Deployment frequency: Measures how often a team pushes changes to production

Where is the data coming from? Where lead time, change failure rate, and mean time to recover are all obtained from Service Management tools or any other kind of agile planning tool, deployment frequency comes from a deploy tool (like Deploy) or from a CI integration tool like Jenkins.

The DORA metrics are used to rank a software team’s performance, either high or low, as it pertains to software development and delivery capabilities. In the end, these metrics give your teams the capabilities to improve where they are today; it’s your baseline and allows you to form your desired future goals from there.


So, how do the DORA metrics tie into value stream management? What we often see in a lot of customers are siloed processes, ranging from the business to the IT end. Between cultural shifts and internal knowledge transfers, the question becomes, how can we keep up with the speed of change and transform your organization into one that can benefit from these changes?

Within the VSM lifecycle, DORA metrics are the essence of the “efficiency and tech flow improvement” area.

Essentially, the DORA metrics are measuring the outputs of your DevOps organization. Moving farther to the right side to “operations improvement” where fully automated change management processes are brought into play.

Overall, a successful VSM platform will be two-dimensional.

“You have different organizations, and different hierarchies within these organizations,” says Mattias Zieger, Technical Director at “This is the second dimension. It starts at the team level, but it shouldn’t stop there. It should go up to the product level, then to the department level, then to an enterprise level until it reaches the whole organization.”

To accomplish this amongst a worldwide agency can be extremely difficult, but this is where VSM can help. VSM provides different aggregation levels for different stakeholders who all have unique dashboards and KPI’s that they want to measure. Ultimately, the value stream is there to connect the outcomes of the business with the activities of your SDLC. Utilizing DORA metrics within a VSM platform helps concentrate on tracking how good an organization’s inputs are while using outputs, but DORA is just one small part of the equation.

How to enable DORA metrics within your VSM platform

So how do we enable these DORA metrics? What do we need to track and measure these metrics? Looking from an architecture standpoint, the most important factor for DORA metrics is an analytics platform. A successful analytics platform has three layers, starting with the first being heterogeneous sources. This is the tool landscape within your enterprise where the work is done (ex. JIRA, Jenkins, ServiceNow). The next layer is the actual analytics platform, also called the unified information model; this is where information is given structure, starting to the planning side all the way down to individual tasks. Lastly there are analytic lenses which are specific panorama lenses that improve decisions by leveraging purpose-built analytics solutions to learn how and why issues are occurring.

Extending DORA beyond

Now that we have seen what the DORA metrics can do, is this the end point? Certainly not. Visibility is the first step to actionable insights and with all this data at your disposal, there’s now opportunities to extend your use of DORA metrics even further.

Some questions to consider when leveraging your DORA metrics:

  • Can we predict which changes will fail ahead of time?
  • How can I enable teams with a Green Light API that allows high performing teams to deploy more frequently with limited oversight?
  • Can we establish a base for all teams to identify and measure improvement?
  • Can we identify bottlenecks in the development lifecycle to reduce lead time?
  • Can we predict when work will be done for major features and releases?

Equipped with DORA metrics, your teams and systems can detect risk factors, minimize failure probability, discover what’s preventing you from being more agile than you are today, and much more. So why stop at simply tracking and measuring?

The difference

Go one step beyond the typical architecture framework and add an AI-solution to your VSM platform. Including AI to your DORA metrics will help connect the outcomes from the business perspective with the activities of your SDLC. offers four AI-solutions including:

  • Flow Acceleration: Reduce risk, increase throughput, and accelerate agility
  • Quality Improvement: Detect/predict quality problems and guide teams to the problematic area in your application landscape
  • Change Risk Prediction: Identify risky changes and proactively take steps to manage and reduce risk
  • Service Management Process Optimization: Break down silos across fragmented teams and accelerate IT service delivery with data-driven decision

DORA is the first layer on top of all these tools you may have already in your toolchain. It’s not necessary to have all four solutions in one go. Start by identifying where you have the most pain and work from there.

Are you ready to scale your enterprise?


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