To deliver compelling products and solutions to market by:

• Architecting platforms using microservices or monolithic patterns, supported by virtualization or container technology.
• Implementing reusable ALM frameworks for automating builds, testing and deployment.
• Leveraging cloud-based technologies as appropriate to accelerate application integration and usage.
• Deploying dashboards to get better and real-time visibility into a delivery pipeline and to help decision-making.

To mitigate risk and improve reliability by:

• Streamlining QA through assessments, skills and tool evaluations, and recommending solutions.
• Recommending QA strategies, applying agile test execution models, and employing defect management and root cause analysis.
• Designing better organization models to suite the organization’s needs and objectives – from federated to centralized, COEs & Guilds, embedded to hybrid.
• Automating testing across the SDLC, to aid developers and testers across various test environments.
• Automating test data management employing both organic and synthetic approaches.
• Implementing Performance Management through load testing and active monitoring practices.
• Leveraging metrics using not only well-known quality measures but emerging ones that support agile development.

To achieve continuous delivery and agility at scale by:

• Embedding QA earlier in the lifecycle to be the voice of the customer, specifying features using TDD/ATDD/BDD, and developing automated tests in sync with development.
• Implementing Release Automation using well-known enterprise deployment reference architectures.
• Enabling fast feedback loops by integrating automated build and deployment with automated tests.
• Automating environment management to ensure reduction in lead and wait-times through any release pipeline.
• Monitoring pipeline metrics that would inform stakeholders on both ‘flow’ and ‘feedback’.
• Facilitating culture change employing a deliberate but flexible approach while keeping organization need and constraints front and center.

To effectively manage software product lifecycles using data and information by:

• Performing gap analysis across dimensions including process quality, delivery, product quality, performance, and operations data, and identifying improvements.
• Articulating a roadmap for employing data analytics to better manage software delivery with agility.
• Recommending analytics strategies to improve products and solutions through innovative use of data assets.
• Employing prescriptive and predictive analytics to improve delivery and operational effectiveness and efficiency.
• Enabling software delivery analytics by planning, designing and architecting Hadoop and other Big Data platforms.