Accelerating the Release Lifecycle with Deployment Paths: Part 1

For many companies, the journey to the cloud reduces technical debt costs and meets CapEx-to-OpEx goals. This includes redesign to microservices, lift and shift, replatforming, refactoring, and replacement. As practices such as DevOps, cloud native, serverless, and site reliability engineering (SRE) mature, the focus is on significant levels of automation, speed, agility, and business alignment with IT (helping enterprise IT transform into an engineering organization). ) is moving to.

Many businesses struggle to derive real value from their cloud journey and may continue to overspend. Several analysts have reported that over 90% of enterprises continue to overspend on cloud without realizing significant returns.

The true essence of value is when business and IT work together to create new features at a rapid pace, resulting in improved developer productivity and faster time to market. These goals require a target operating model. Rapidly deploying applications to the cloud involves not only accelerating development through continuous integration, deployment, and testing (CI/CD/CT), but also accelerating the supply chain lifecycle, which includes several other groups such as governance risk and compliance (GRC) and change management. is also needed. , operations, resilience and reliability. Companies are constantly looking for ways to help product teams implement and deploy concepts faster than ever before.

Automation-first and DevSecOps-led approach

Enterprises often retrofit cloud transformation elements within their existing application supply chain processes rather than considering new life cycles and delivery models suited to their speed and scale. Enterprises reimagining their application lifecycles through an automation-first approach encourage engineering-driven product lifecycle acceleration that unlocks the potential of cloud transformation. Examples include:

• It is a pattern-based architecture that standardizes architecture and design processes (teams have autonomy to choose patterns and technologies or co-create new patterns).
• Patterns that address security and compliance dimensions ensure traceability to these requirements.
• Patterns as code that help organize many cross-cutting issues (this also promotes an internal source model for pattern maturity and promotes reusability).
• DevOps pipeline-based activities that can be leveraged throughout the lifecycle.
• Automatic generation of specific data required for security and compliance reviews.
• Review operational readiness with limited or no manual intervention.

As enterprises embrace cloud native and everything as code, the code-to-production journey has become a critical aspect of delivering value to customers. This complex process is often “Distribution path,” involves a complex series of steps and decisions that can have a significant impact on an organization’s ability to deliver software efficiently, reliably, and at scale. From architecture, design, code development, and testing to deployment and monitoring, each step in the deployment path presents unique challenges and opportunities. As you navigate the complexities that exist today, IBM® aims to help you discover strategies and target health modes to achieve a smooth and effective deployment path.

We explore best practices, tools, and methodologies that can help organizations streamline their software delivery pipeline, accelerate time to market, improve software quality, and ensure robust operations in production environments.

In the second post in this series, we provide a maturity model and building blocks to help companies accelerate their software supply chain lifecycle in the ever-evolving enterprise cloud-native software development environment.

Deployment Pathways: Current Perspectives and Challenges

The diagram below summarizes a view of the Enterprise Software Development Life Cycle (SDLC) using common gates. The flow is self-explanatory, but it is key to understand that there are many aspects of the software supply chain process that make it a combination of a waterfall model and an intermittent agile model. The problem is that an application’s build-to-deployment timeline (or any iteration thereof) is affected by multiple first and last mile activities that are typically maintained manually.

The main challenges due to the traditional nature of SDLC are:

1. There is a pre-development wait time of 4-8 weeks during the architecture and design phase to reach development. This is caused by:
   • We conduct multiple first mile reviews to ensure there are no negative impacts to your business, including privacy issues, data classification, business continuity and compliance (most of which are done manually).
   • Despite the agile principles of the development cycle (e.g., provisioning an environment only after full design approval), it remains waterfall or semi-agile, an enterprise-wide SDLC process that requires sequential execution.
   • Applications that are perceived as “unique” are subject to in-depth investigation and intervention with limited opportunities for acceleration.
   • Lack of cohesive efforts such as standardization and driving change agents makes it difficult to institutionalize pattern-based architecture and development.
   • A security culture that influences the speed of development by adhering to security controls and guidelines related to manual or semi-manual processes.
2. Development latency for environment provisioning and CI/CD/CT tool integration due to:
   • Manual or semi-automated environment provisioning.
   • The pattern (paper) is provided as normative guidance only.
   • Fragmented DevOps tools that require effort to connect together.
3. The post-development (last mile) wait time before actual release can easily be 6-8 weeks or more for the following reasons:
   • Manual evidence collection to complete security and compliance reviews beyond standard SAST/SCA/DAST (e.g. security configuration, day 2 controls, tagging, etc.).
   • Manual evidence collection for operational and resiliency reviews (e.g. cloud operations and business continuity support).
   • Review IT services and service transitions to support incident management and resolution.

Deployment Path: Target State

The path to deploying target states requires a streamlined, efficient process that minimizes bottlenecks and accelerates software supply chain transformation. In this ideal state, the deployment path is characterized by a seamless integration of design (first mile) as well as development, testing, platform engineering, and deployment phases (last mile) according to Agile and DevOps principles. This helps you deploy code changes quickly and automatically with the necessary (automation-based) validation into your production environment.

IBM’s vision for target health prioritizes security and compliance by integrating security checks and compliance validation into the CI/CD/CT pipeline to enable early discovery and remediation of vulnerabilities. This vision emphasizes collaboration between development, operations, reliability, and security teams through a shared responsibility model. We also establish continuous monitoring and feedback loops to gather insights for further improvements. Ultimately, Target Health aims to deliver software updates and new features to end users quickly, with minimal manual intervention and with a high level of confidence for all enterprise stakeholders.

The diagram below shows a potential target view of the deployment path to help you embrace a cloud-native SDLC model.

Key elements of the cloud-native SDLC model include:

• Pattern-based architecture and design are institutionalized throughout the enterprise.
• A pattern that integrates key requirements of security, compliance, resiliency, and other corporate policies (code).
• Security and compliance reviews accelerated by patterns and used to describe solutions.
• Core development including environment creation, pipeline and service configuration (powered by Platform Engineering Enterprise Catalog).
• A CI/CD/CT pipeline that builds connectivity to all activities across the lifecycle deployment path.
• Platform engineering builds, configures, and manages platforms and services using any enterprise policies (e.g. encryption) included in the platform policy.
• Security and compliance tools (such as vulnerability scanning or policy scanning) and automation that are integrated into your pipeline or available as self-service.
• Generate high-level data (from logs, tool output, and code scanning insights) for multiple reviews without manual intervention.
• Track everything from backlog to deployment release notes and change impact.
• Intervene only by exception.

Deployment pathways drive acceleration through clarity, accountability, and traceability.

By defining a structured deployment path, organizations can standardize the steps involved in the supply chain life cycle, allowing each step to be tracked and audited. This allows stakeholders to monitor the progress of individual steps from initial design to deployment, providing real-time visibility into program status. Assigning ownership at each stage of the deployment path holds team members accountable for deliverables, making it easier to track contributions and changes, as well as accelerating problem resolution with the appropriate level of intervention. Traceability through the deployment path provides data-driven insights to help improve processes and increase efficiency for future programs. A well-documented deployment path supports industry compliance and simplifies reporting because each part of the process is clearly recorded and searchable.

Read Part 2: Exploring maturity models and implementation approaches