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Last Updated Jul 15, 2022 —

AI and  analytics help keep your  release pipelines flowing

Ankur Trivedi, Senior Sales Engineer at Digital.ai, shares his insight on the common challenges faced while delivering software and how to employ best practices for a better and faster release pipeline.

Is your DevOps stuck?
How to use AI and analytics to keep your release pipelines flowing

Check out this webinar to learn how a holistic value stream delivery platform can meet you where you are, heightening the focus on your closer-in opportunities, from pipeline orchestration and analytics to AI-driven DevOps improvements.

Watch now!

If you don’t have the right tools and analytics to address DevOps challenges faced at different stages of the journey, then your transformation efforts will drive only limited impact. While DevOps toolchains have made continuous delivery possible, software delivery pipelines are still plagued with long cycle times, lack of visibility into causes of issues, and disconnected processes with no governance. If not addressed, then these issues could potentially lead to a ​damaged reputation, employee ​dissatisfaction​, and lost ​market share.  

It’s critical that your Dev and IT Ops teams understand the key challenges in software delivery, as well as best practices to address in order to smoothen the path to better and faster software delivery.

Challenge 1: Orchestration

According to The State of DevOps 2022 report by Forrester, the days of best-of-breed DevOps toolchains are at an end. What is left are highly interdependent, yet disconnected, tools and processes without rigid governance​. It’s difficult to manage complex dependencies among teams because there is no single source of truth. Each team has their own instances of whatever, making it extremely challenging to scale processes and establish an enterprise-wide governance framework.

To address these challenges, teams first need to track and understand these metrics that are dependent on processes and tools: release cycle time, time spent in manual approval gates, time spent on manual testing, time spent during handoffs, deployment time, and build time. After that, it’s important that teams kickstart the following activities:

  • Establish center of excellence to standardize the pipeline tools to increase compliance and consistency across teams 

  • Identify common release patterns to make releases more repeatable and predictable  

  • Group release patterns by technology or business function 

  • Onboard teams on standard release process to make sure everyone is working from the same information 

  • Socialize early successes of the onboarding process at various forums to increase buy-ins, visibility, and collaboration 

Challenge 2: Efficiency

Long cycle times and frequent delays​ in software releases are among problems that directly affect the bottom line. The causes of the inefficiency vary. For example, release delays could be due to the manual effort required to manage audit compliance and ensure stage gate approvals are respected. Teams could have long cycle times because testing is fragmented and requires manual hand offs. Another reason could be that there are many disconnected but interdependent manual steps to deploy complex solutions.

Below are four key activities commonly done by teams that manage to increase the performance metrics such as release frequency, deployment time, change management process time, number of manual approval gates, and percent of tasks automated:

  • Build automated pipelines based on identified patterns to accelerate your delivery process and reduce risks

  • Identify early adopters for the automated pipelines to test the efficacy, reliability, security and scalability of the automation tool. 

  • Build a roadmap for onboarding and share results with the community on the successes  

  • Implement feedback from the onboarding back into the pipeline 

Challenge 3: Visibility

Eliminating issues in your software release pipeline completely is ideal, but not realistic. What’s more feasible is to get the right analytics that provide full visibility into root causes of issues or inefficiencies when (or before) they happen​. But many teams are not even measuring important metrics that can help identify bottlenecks in their software release process. This leads to high failure rate of production deployments and service disruptions. And the lack of visibility lead to high mean time to resolution when something goes wrong in the release process. 

In order to increase the visibility into their software delivery pipeline, it’s advised that teams:

  • Evaluate your current performance by measuring these key metrics​: Change Failure Rate, Time to Restore Service, Application Availability​, and Lost Revenue or Productivity due to Outages  

  • Improve resilience​ by establishing or expanding the focus on SRE practices 

  • Use feature flagging and/or blue-green deployments​ 

  • Assess risk using a data-driven approach and shift testing left in the value stream to identify failures early 

You need an application release automation solution that automates deployments, orchestrates releases, and provides insights into your end-to-end software release pipelines.  

 

Check out our webinar "Is your DevOps stuck? How to use AI and analytics to keep your release pipelines flowing" to learn more about how a holistic value stream delivery platform can meet you where you are. 

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