DevOps Analytics Strategy

Posted by Aditya Chourasiya on Fri 25 February 2022

Objective, Method and Maturity model for business analytics across an enterprise.

As a business user, I want to make sense of all the data coming from my production system and make sure my product meets business requirements. This article summarizes different maturity model and action-sequence to achieve snow-ball effect for typical organization.


Let's start with Why

  • To continue DevOps and Agile development and leverage one of the DevOps principles – “to measure”
  • To understand the current baseline and be able to measure progress
  • To answer common question of - how close we are to 'done'?
  • To raise awareness and instill team wide 'measure' from the very inception
  • To give stakeholders and senior management on-demand visibility into systems dev and ops


  • To promote Business objectives of measurements and organization wide 'manifesto'
  • To define metrics for each group of stakeholders
  • To develop best practices for measurements
  • To define delivery methods for metrics
  • To define measurement and metric delivery cadence


Phase 1 of 4 (Awareness)

This phase is focused on developing Descriptive analytics capabilities, which can help answer: “What has happened?"

  • Release Dashboard- Showing all releases went/going to prod
  • Teams assessment / DoD dashboard- Are teams doing well in assessment/ meeting DoD
  • Data Stewards rollout - Identifying who is responsible for keeping data updated/correct
  • Standard Team dashboards - Same dashboards across teams with auto login and rotation enable
  • Analytics Strategy awareness - CoPs, Demo, micro learnings and Lunch and Learns about Analytics in DevOps
  • CoP - PowerBI - PowerBI CoP establish and monthly meeting scheduled
  • Team Business Objectives [PI/Sprint Goals] Dashboard - PI and Sprint goal dashboards from AzDo Enabler to experimental releases
  • Business value dashboard requirement gathering - Need to gather requirement for business value reporting (Enabler for business value)

Phase 2 of 4 (Desire)

This phase is focused on developing Diagnostic analytics capabilities, which can help answer the question, “Why did it happen?"

  • Experimental release support
  • SAFe Metrics rollout
  • Focus on and communication of DevOps Strategy
  • Gamification of Team's matrices
  • Analytics as a code
  • Business value delivery prediction
  • Make data accessible so people can play with it freely and innovate
  • Relevant messaging and alerts through reports
  • ROAMing Risks and Risk Registry

Phase 3 of 4 (Knowledge)

This phase is focused on developing Predictive Analytics capabilities, which can help answer the question, “What could happen?”

  • Business value delivery confirmation
  • Continuous analytics delivery through automated pipelines
  • DevOps Program Risk (ROAM) analytics
  • Building people skills for digital transformation based on Risk area
  • Establish Standards and Practices for Analytics
  • Skill requirement based recruitment
  • Big Data integration for better analytics

Phase 4 of 4 (Ability)

This phase is focused on developing Prescriptive Analytics capabilities, which can help advise on possible outcomes and answer: “What should we do?”

  • Skill based resource movement
  • Machine learning integration to other then business value delivery
  • Analytics Governance body
  • Business requirement generation through analytics
  • Measure how analytics is changing business
  • Strategic decision making through automation

Summary and invite

This article was intended to be written in V2MoM format, however due to different organizational structure and needs, it will vary a lot. Based on my experience in different government and non-government organizations, the above holds true. Please feel free to submit a pull request to suggest any changes. Thank you for reading.