Why AI is Essential for Value Stream Management

In the second installment of his “How to Run and Transform” blog series, Achmad Chadran reviews how Value Stream Management brings the customer experience perspective into key software development and delivery processes and resources to better align operations with business objectives. The challenges of processing DevOps metrics and transforming them into actionable prescriptive analytics makes AI technologies – which make up an essential component of the Micro Focus ValueEdge platform – indispensable for VSM.

In my first blog in this series, “How to Run and Transform at the Same Time,” I discussed the two often contradictory missions that all organizations face: to keep the day-to-day operations running smoothly, and to keep pace with all the changes occurring at once, from all directions. I went on to enumerate the five key strategies for successful achieving digital transformation:

  • Accelerate Application Delivery
  • Modernize Core Applications
  • Simplify IT Transformation
  • Strengthen Cyber Resilience
  • Analyze Data in Time to Act

This second installment in the series explores the first strategy on the list. As the set of coordinated services orchestrated to deploy applications to end users, application delivery management demands supremely high priority from business leaders. To this end, value stream management is the most important application delivery practice to emerge to date. What is value stream management, and how can it benefit the application delivery process?

The Goals of Value Stream Management

Value stream management (VSM) is a lean business practice that enables the management and monitoring of discrete stages of the software delivery life cycle to improve the flow of value to the organization. Among its key benefits, VSM brings the customer experience perspective to these stages to better align operations with business objectives and to scale agile and DevOps transformations. In this regard, VSM identifies and analyses value streams (as opposed to features and functions) to provide more detailed measures of software delivery success, to enable teams to focus more energy and time on what works, and pivot away from what doesn’t.

VSM Builds Upon and Enhances DevOps

VSM is about return on investment (ROI), prioritisation, successful delivery, process improvement, and change prioritisation. Process leaders should therefore not view VSM as a replacement for DevOps, but as a DevOps enhancement. VSM empowers organizations to understand which changes deliver the highest value and what adjustments managers can make to their resource allocations to deliver the greatest returns.

Value stream management leverages many DevOps measurements, including:

  • Lead Time (LT)
  • Cycle Time (CT)
  • Percent Complete and Accurate (%C&A)

These metrics are necessarily diverse and often massive in quantity and volume.

VSM is a lean business practice that brings the customer experience perspective to key software development and delivery processes and resources to better align operations with business objectives and to scale agile and DevOps transformations.

For example, Lead Time measures the time it takes for an idea to be implemented for customer use. It helps answer the question, “I have an idea; how long will it take to implement?”

Cycle Time measures the amount of time developers spend working on a change before deploying the change to production.

The Percent Complete and Accurate metric measures how a project progresses along its timeline by comparing the amount completed to date against the total amount of time estimated.

By aligning the concept of value with DevOps metrics and methods, VSM creates a common language that stakeholders in both business and IT organizations can understand.

What Makes AI Vital for VSM

Artificial Intelligence (AI) technologies excel at performing image recognition, understanding humans’ natural language and writing patterns, finding connections among different types of data, identifying pattern anomalies, and predicting outcomes. These capabilities make AI essential for processing data collected from distinct stages of the software development life cycle (SDLC).

And while DevOps metrics typically encompass those listed above (in addition to Deployment Frequency, Change Failure Rate, Mean Time to Recovery (MTTR), Customer Ticket Volume, and Defect Escape Rate), they can include myriad other measurements directly related to a product’s functionality and value proposition.

AI thus simplifies the modelling of a software’s past, through its ability to account for nearly endless variables, to sort through tons of data, and to include all of it in an analysis that builds a complete picture of the current state of the product or platform.

But an equally vital attribute of AI is its ability to perform high-quality prescriptive analyses of process and resourcing tweaks to find the best possible outcomes. Moreover, users can train AI systems to recognize metrics and patterns associated with both high customer value and opportunities to reduce wasteful processes.

AI Technologies Power Micro Focus ValueEdge

Micro Focus ValueEdge value stream management platform applies AI-powered analytics to deliver actionable insights based on deep learning and predictive analytics. ValueEdge streamlines trend analyses, anomaly detection, and the delivery of insights and actionable analytics within its intuitive, customizable user interface. In addition, the platform’s smart-policy-based pipeline tracking, and management capabilities enable autonomous delivery from ideation to release.

Paired with ValueEdge’s ability to manage key continuous quality and functional testing activities, the solution minimizes waste and risk while maximizing ROI for faster application delivery, improved levels of customer satisfaction and increased competitive advantage.

With well over 100 pre-built commercial and open-source integrations, users can effortlessly integrate their existing and future toolchains using ValueEdge’s REST-based Application Programming Interfaces (APIs) and Software Development Kit (SDK), while gaining greater visibility across the entire software development lifecycle.

Conclusion

To achieve this alignment, VSM leverages DevOps measurements, which provide rich data on process and task durations, problem resolution costs, customer satisfaction, and other performance indicators. The breadth and diversity of DevOps data – collected across all stages of the SDLC – makes AI technologies essential for effective value stream management.

Micro Focus’ ValueEdge value stream management platform applies AI to deliver actionable insights to help maximize the alignment of customer value, business objectives, and agile and DevOps processes. In other words, to deliver value, intelligently.

For more information about Micro Focus ValueEdge, visit the ValueEdge home page.

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