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.