DevOps' broken promise
Software delivery is in crisis, as delayed releases and quality failures block enterprise innovation.
Common symptoms like slow testing, unclear requirements, and data bottlenecks undermine productivity and quality. Enterprises struggle to fix the root cause of these challenges. They instead opt for quick-fix solutions, growing reliant on painkillers that provide temporary relief, but do not offer sustainable solutions.
To diagnose and solve poor productivity and software quality, enterprises must understand how their DevOps lifecycle has split into “inner” and “outer” loops.
INNER LOOP QUALITY
What is the inner loop?
The inner loop refers to the high-value development tasks associated with creating software, such as coding, solving problems, reviewing and debugging. These activities are essential for the initial creation and refinement of the software code.
The evolution of inner loop productivity
This inner loop has enjoyed sustained investment and innovation, unlocking ever-greater developer productivity:
1996
Code completion and “Intellisense” introduced to widely available IDEs.
2001
Apache Maven continues advancements in build automation.
2010s
New package managers and code libraries enable lightning-fast code changes.
2020s
AI-powered Co-Pilots and Agents further accelerate development.
“92% of U.S.-based developers are already using AI coding tools both in and outside of work.”
The outer loop
What is the outer loop?
Whereas the inner loop is the domain of the developer, the outer loop is the world of the business analyst, product owner, tester and data engineer.
The outer loop drives the initial software ideation, feeds the inner loop with all the high-quality assets it requires, and ensures that software is well-developed, robust, reliable, and ready for real-world use. This involves tasks such as requirements engineering, test case design, test data management, and quality engineering.
Inner/outer loop misalignment
Enterprises have invested heavily in inner loop productivity, often at the expense of the outer loop. Yet, a productive outer loop is equally important to the creation and delivery of high quality software.
High-quality assets delivered to the inner loop mean that developers can quickly implement features, fix bugs, and deploy new versions of the software, contributing to a rapid development cycle. A smoothly-operating outer loop enables rapid software delivery, with clear requirements, accurate test data and a reliable testing environment, alongside continuous feedback to ensure that software meets requirements.
When the outer loop becomes neglected and bottlenecked, it creates negative risk throughout software delivery.
4 Common symptoms of misalignment
Delayed releases
Your outer loop relies on repetitive manual work and maintenance when reacting to inner loop changes. Technical debt and blockers mount.
Poor release quality
Rapid development is not traceable to clear requirements. Unmeasured, repetitive tests cannot provide release readiness or confidence.
Misaligned teams
Unclear requirements, silos and miscommunication create rework, while teams must wait slowly for test data, testing and environments.
Talent turnover
Skilled practitioners want to collaborate on valuable software. Fighting fires, toil and a ”hero culture”, they seek new opportunities.
4 steps to rebalance your inner/outer loops
Where should enterprises start in eliminating inner/outer loop misalignment?
Living documentation
Clear specifications and requirements bridge silos and foster collaboration, reducing technical debt and uncertainty. Visual models are easy for humans and tools to process and maintain. They create unified understanding and traceability, enabling AI readiness and automation.
Critical assets upfront
Continuously agreeing and refining requirements shifts quality left, avoiding bugs and rework. Leveraging clear requirements to generate tests, data and environments unlocks outer loop quality and productivity, removing blockers like repetitive manual work, maintenance and toil.
On demand data
Inner and outer loops constantly need new data, but enterprises cannot risk provisioning sensitive data. Inaccurate, unavailable test data is one of software delivery’s biggest productivity blockers; self-service data automation one of the fastest wins for productivity and quality.
Continuous quality
Generating tests, data and environments from requirements builds traceability between inner and outer loops, while removing productivity blockers. Measurable tests target requirements and system changes, while visualising and refining requirements further avoids bugs and rework.
Living documentation
Clear specifications and requirements bridge silos and foster collaboration, reducing technical debt and uncertainty. Visual models are easy for humans and tools to process and maintain. They create unified understanding and traceability, enabling AI readiness and automation.
Critical assets upfront
Continuously agreeing and refining requirements shifts quality left, avoiding bugs and rework. Leveraging clear requirements to generate tests, data and environments unlocks outer loop quality and productivity, removing blockers like repetitive manual work, maintenance and toil.
On demand data
Inner and outer loops constantly need new data, but enterprises cannot risk provisioning sensitive data. Inaccurate, unavailable test data is one of software delivery’s biggest productivity blockers; self-service data automation one of the fastest wins for productivity and quality.
Continuous quality
Generating tests, data and environments from requirements builds traceability between inner and outer loops, while removing productivity blockers. Measurable tests target requirements and system changes, while visualising and refining requirements further avoids bugs and rework.
Living documentation
Clear specifications and requirements bridge silos and foster collaboration, reducing technical debt and uncertainty. Visual models are easy for humans and tools to process and maintain. They create unified understanding and traceability, enabling AI readiness and automation.
Critical assets upfront
Continuously agreeing and refining requirements shifts quality left, avoiding bugs and rework. Leveraging clear requirements to generate tests, data and environments unlocks outer loop quality and productivity, removing blockers like repetitive manual work, maintenance and toil.
On demand data
Inner and outer loops constantly need new data, but enterprises cannot risk provisioning sensitive data. Inaccurate, unavailable test data is one of software delivery’s biggest productivity blockers; self-service data automation one of the fastest wins for productivity and quality.
Continuous quality
Generating tests, data and environments from requirements builds traceability between inner and outer loops, while removing productivity blockers. Measurable tests target requirements and system changes, while visualising and refining requirements further avoids bugs and rework.
Living documentation
Clear specifications and requirements bridge silos and foster collaboration, reducing technical debt and uncertainty. Visual models are easy for humans and tools to process and maintain. They create unified understanding and traceability, enabling AI readiness and automation.
Critical assets upfront
Continuously agreeing and refining requirements shifts quality left, avoiding bugs and rework. Leveraging clear requirements to generate tests, data and environments unlocks outer loop quality and productivity, removing blockers like repetitive manual work, maintenance and toil.
On demand data
Inner and outer loops constantly need new data, but enterprises cannot risk provisioning sensitive data. Inaccurate, unavailable test data is one of software delivery’s biggest productivity blockers; self-service data automation one of the fastest wins for productivity and quality.
Continuous quality
Generating tests, data and environments from requirements builds traceability between inner and outer loops, while removing productivity blockers. Measurable tests target requirements and system changes, while visualising and refining requirements further avoids bugs and rework.
Living documentation
Clear specifications and requirements bridge silos and foster collaboration, reducing technical debt and uncertainty. Visual models are easy for humans and tools to process and maintain. They create unified understanding and traceability, enabling AI readiness and automation.
Critical assets upfront
Continuously agreeing and refining requirements shifts quality left, avoiding bugs and rework. Leveraging clear requirements to generate tests, data and environments unlocks outer loop quality and productivity, removing blockers like repetitive manual work, maintenance and toil.
On demand data
Inner and outer loops constantly need new data, but enterprises cannot risk provisioning sensitive data. Inaccurate, unavailable test data is one of software delivery’s biggest productivity blockers; self-service data automation one of the fastest wins for productivity and quality.
Continuous quality
Generating tests, data and environments from requirements builds traceability between inner and outer loops, while removing productivity blockers. Measurable tests target requirements and system changes, while visualising and refining requirements further avoids bugs and rework.
Living documentation
Clear specifications and requirements bridge silos and foster collaboration, reducing technical debt and uncertainty. Visual models are easy for humans and tools to process and maintain. They create unified understanding and traceability, enabling AI readiness and automation.
Critical assets upfront
Continuously agreeing and refining requirements shifts quality left, avoiding bugs and rework. Leveraging clear requirements to generate tests, data and environments unlocks outer loop quality and productivity, removing blockers like repetitive manual work, maintenance and toil.
On demand data
Inner and outer loops constantly need new data, but enterprises cannot risk provisioning sensitive data. Inaccurate, unavailable test data is one of software delivery’s biggest productivity blockers; self-service data automation one of the fastest wins for productivity and quality.
Continuous quality
Generating tests, data and environments from requirements builds traceability between inner and outer loops, while removing productivity blockers. Measurable tests target requirements and system changes, while visualising and refining requirements further avoids bugs and rework.
Living documentation
Clear specifications and requirements bridge silos and foster collaboration, reducing technical debt and uncertainty. Visual models are easy for humans and tools to process and maintain. They create unified understanding and traceability, enabling AI readiness and automation.
Critical assets upfront
Continuously agreeing and refining requirements shifts quality left, avoiding bugs and rework. Leveraging clear requirements to generate tests, data and environments unlocks outer loop quality and productivity, removing blockers like repetitive manual work, maintenance and toil.
On demand data
Inner and outer loops constantly need new data, but enterprises cannot risk provisioning sensitive data. Inaccurate, unavailable test data is one of software delivery’s biggest productivity blockers; self-service data automation one of the fastest wins for productivity and quality.
Continuous quality
Generating tests, data and environments from requirements builds traceability between inner and outer loops, while removing productivity blockers. Measurable tests target requirements and system changes, while visualising and refining requirements further avoids bugs and rework.
Living documentation
Clear specifications and requirements bridge silos and foster collaboration, reducing technical debt and uncertainty. Visual models are easy for humans and tools to process and maintain. They create unified understanding and traceability, enabling AI readiness and automation.
Critical assets upfront
Continuously agreeing and refining requirements shifts quality left, avoiding bugs and rework. Leveraging clear requirements to generate tests, data and environments unlocks outer loop quality and productivity, removing blockers like repetitive manual work, maintenance and toil.
On demand data
Inner and outer loops constantly need new data, but enterprises cannot risk provisioning sensitive data. Inaccurate, unavailable test data is one of software delivery’s biggest productivity blockers; self-service data automation one of the fastest wins for productivity and quality.
Continuous quality
Generating tests, data and environments from requirements builds traceability between inner and outer loops, while removing productivity blockers. Measurable tests target requirements and system changes, while visualising and refining requirements further avoids bugs and rework.
Living documentation
Clear specifications and requirements bridge silos and foster collaboration, reducing technical debt and uncertainty. Visual models are easy for humans and tools to process and maintain. They create unified understanding and traceability, enabling AI readiness and automation.
Critical assets upfront
Continuously agreeing and refining requirements shifts quality left, avoiding bugs and rework. Leveraging clear requirements to generate tests, data and environments unlocks outer loop quality and productivity, removing blockers like repetitive manual work, maintenance and toil.
On demand data
Inner and outer loops constantly need new data, but enterprises cannot risk provisioning sensitive data. Inaccurate, unavailable test data is one of software delivery’s biggest productivity blockers; self-service data automation one of the fastest wins for productivity and quality.
Continuous quality
Generating tests, data and environments from requirements builds traceability between inner and outer loops, while removing productivity blockers. Measurable tests target requirements and system changes, while visualising and refining requirements further avoids bugs and rework.
Living documentation
Clear specifications and requirements bridge silos and foster collaboration, reducing technical debt and uncertainty. Visual models are easy for humans and tools to process and maintain. They create unified understanding and traceability, enabling AI readiness and automation.
Critical assets upfront
Continuously agreeing and refining requirements shifts quality left, avoiding bugs and rework. Leveraging clear requirements to generate tests, data and environments unlocks outer loop quality and productivity, removing blockers like repetitive manual work, maintenance and toil.
On demand data
Inner and outer loops constantly need new data, but enterprises cannot risk provisioning sensitive data. Inaccurate, unavailable test data is one of software delivery’s biggest productivity blockers; self-service data automation one of the fastest wins for productivity and quality.
Continuous quality
Generating tests, data and environments from requirements builds traceability between inner and outer loops, while removing productivity blockers. Measurable tests target requirements and system changes, while visualising and refining requirements further avoids bugs and rework.
Living documentation
Clear specifications and requirements bridge silos and foster collaboration, reducing technical debt and uncertainty. Visual models are easy for humans and tools to process and maintain. They create unified understanding and traceability, enabling AI readiness and automation.
Critical assets upfront
Continuously agreeing and refining requirements shifts quality left, avoiding bugs and rework. Leveraging clear requirements to generate tests, data and environments unlocks outer loop quality and productivity, removing blockers like repetitive manual work, maintenance and toil.
On demand data
Inner and outer loops constantly need new data, but enterprises cannot risk provisioning sensitive data. Inaccurate, unavailable test data is one of software delivery’s biggest productivity blockers; self-service data automation one of the fastest wins for productivity and quality.
Continuous quality
Generating tests, data and environments from requirements builds traceability between inner and outer loops, while removing productivity blockers. Measurable tests target requirements and system changes, while visualising and refining requirements further avoids bugs and rework.
Living documentation
Clear specifications and requirements bridge silos and foster collaboration, reducing technical debt and uncertainty. Visual models are easy for humans and tools to process and maintain. They create unified understanding and traceability, enabling AI readiness and automation.
Critical assets upfront
Continuously agreeing and refining requirements shifts quality left, avoiding bugs and rework. Leveraging clear requirements to generate tests, data and environments unlocks outer loop quality and productivity, removing blockers like repetitive manual work, maintenance and toil.
On demand data
Inner and outer loops constantly need new data, but enterprises cannot risk provisioning sensitive data. Inaccurate, unavailable test data is one of software delivery’s biggest productivity blockers; self-service data automation one of the fastest wins for productivity and quality.
Continuous quality
Generating tests, data and environments from requirements builds traceability between inner and outer loops, while removing productivity blockers. Measurable tests target requirements and system changes, while visualising and refining requirements further avoids bugs and rework.
Living documentation
Clear specifications and requirements bridge silos and foster collaboration, reducing technical debt and uncertainty. Visual models are easy for humans and tools to process and maintain. They create unified understanding and traceability, enabling AI readiness and automation.
Critical assets upfront
Continuously agreeing and refining requirements shifts quality left, avoiding bugs and rework. Leveraging clear requirements to generate tests, data and environments unlocks outer loop quality and productivity, removing blockers like repetitive manual work, maintenance and toil.
On demand data
Inner and outer loops constantly need new data, but enterprises cannot risk provisioning sensitive data. Inaccurate, unavailable test data is one of software delivery’s biggest productivity blockers; self-service data automation one of the fastest wins for productivity and quality.
Continuous quality
Generating tests, data and environments from requirements builds traceability between inner and outer loops, while removing productivity blockers. Measurable tests target requirements and system changes, while visualising and refining requirements further avoids bugs and rework.
Living documentation
Clear specifications and requirements bridge silos and foster collaboration, reducing technical debt and uncertainty. Visual models are easy for humans and tools to process and maintain. They create unified understanding and traceability, enabling AI readiness and automation.
Critical assets upfront
Continuously agreeing and refining requirements shifts quality left, avoiding bugs and rework. Leveraging clear requirements to generate tests, data and environments unlocks outer loop quality and productivity, removing blockers like repetitive manual work, maintenance and toil.
On demand data
Inner and outer loops constantly need new data, but enterprises cannot risk provisioning sensitive data. Inaccurate, unavailable test data is one of software delivery’s biggest productivity blockers; self-service data automation one of the fastest wins for productivity and quality.
Continuous quality
Generating tests, data and environments from requirements builds traceability between inner and outer loops, while removing productivity blockers. Measurable tests target requirements and system changes, while visualising and refining requirements further avoids bugs and rework.
Living documentation
Clear specifications and requirements bridge silos and foster collaboration, reducing technical debt and uncertainty. Visual models are easy for humans and tools to process and maintain. They create unified understanding and traceability, enabling AI readiness and automation.
Critical assets upfront
Continuously agreeing and refining requirements shifts quality left, avoiding bugs and rework. Leveraging clear requirements to generate tests, data and environments unlocks outer loop quality and productivity, removing blockers like repetitive manual work, maintenance and toil.
On demand data
Inner and outer loops constantly need new data, but enterprises cannot risk provisioning sensitive data. Inaccurate, unavailable test data is one of software delivery’s biggest productivity blockers; self-service data automation one of the fastest wins for productivity and quality.
Continuous quality
Generating tests, data and environments from requirements builds traceability between inner and outer loops, while removing productivity blockers. Measurable tests target requirements and system changes, while visualising and refining requirements further avoids bugs and rework.
Living documentation
Clear specifications and requirements bridge silos and foster collaboration, reducing technical debt and uncertainty. Visual models are easy for humans and tools to process and maintain. They create unified understanding and traceability, enabling AI readiness and automation.
Critical assets upfront
Continuously agreeing and refining requirements shifts quality left, avoiding bugs and rework. Leveraging clear requirements to generate tests, data and environments unlocks outer loop quality and productivity, removing blockers like repetitive manual work, maintenance and toil.
On demand data
Inner and outer loops constantly need new data, but enterprises cannot risk provisioning sensitive data. Inaccurate, unavailable test data is one of software delivery’s biggest productivity blockers; self-service data automation one of the fastest wins for productivity and quality.
Continuous quality
Generating tests, data and environments from requirements builds traceability between inner and outer loops, while removing productivity blockers. Measurable tests target requirements and system changes, while visualising and refining requirements further avoids bugs and rework.
Living documentation
Clear specifications and requirements bridge silos and foster collaboration, reducing technical debt and uncertainty. Visual models are easy for humans and tools to process and maintain. They create unified understanding and traceability, enabling AI readiness and automation.
Critical assets upfront
Continuously agreeing and refining requirements shifts quality left, avoiding bugs and rework. Leveraging clear requirements to generate tests, data and environments unlocks outer loop quality and productivity, removing blockers like repetitive manual work, maintenance and toil.
On demand data
Inner and outer loops constantly need new data, but enterprises cannot risk provisioning sensitive data. Inaccurate, unavailable test data is one of software delivery’s biggest productivity blockers; self-service data automation one of the fastest wins for productivity and quality.
Continuous quality
Generating tests, data and environments from requirements builds traceability between inner and outer loops, while removing productivity blockers. Measurable tests target requirements and system changes, while visualising and refining requirements further avoids bugs and rework.
4 benefits of inner/outer loop alignment
Faster time to market
Traceability to inner loop changes focuses outer loop efforts and automates away toil. Outer loop productivity sky-rockets, focusing on value-added activity.
Release readiness
Requirements and quality engineering are measured against inner loop changes. Rigorous tests assure the bug-free delivery of requirements and code changes.
Collaboration
Inner and outer loop stakeholders work from a shared under-standing of systems, requirements and quality outcomes, without silos or cross-team constraints.
Talent retention
The best people want to work on the best software. Alignment frees up critical thinking and creativity, nurtures skills and maintains developer experience.
Redefine software delivery with Curiosity
Our platform prioritises outer loop productivity and quality, while fostering collaboration across your entire software delivery ecosystem.
Clarity through visual models
Use visual models to define data requirements and drive synthetic data generation early in the SDLC, uncover edge cases, minimise gaps and provide the right data to your delivery teams.
Complete data coverage
High-coverage, varied data sets enable comprehensive testing against all scenarios. Deliver accurate, outcome-driven data for your requirements and tests, from a single source of truth.
The right data, on demand
Enable teams to access and provision the data they need, when they need it. Simplify discovery, compliance, risk, coverage and quality for faster insights and higher productivity across delivery.
Continuous quality and compliance
Prepare, provision, and optimise compliant data delivery for your QA environments on demand. Continuously assess and analyse the quality of data across all connected systems.
Data discovery & insights
Gain real-time insights into your data with monitoring that identifies sensitive data, system changes, and quality issues. Stay proactive and ensure your data drives smarter decisions.
Clarity through visual models
Use visual models to define data requirements and drive synthetic data generation early in the SDLC, uncover edge cases, minimise gaps and provide the right data to your delivery teams.
Complete data coverage
High-coverage, varied data sets enable comprehensive testing against all scenarios. Deliver accurate, outcome-driven data for your requirements and tests, from a single source of truth.
The right data, on demand
Enable teams to access and provision the data they need, when they need it. Simplify discovery, compliance, risk, coverage and quality for faster insights and higher productivity across delivery.
Continuous quality and compliance
Prepare, provision, and optimise compliant data delivery for your QA environments on demand. Continuously assess and analyse the quality of data across all connected systems.
Data discovery & insights
Gain real-time insights into your data with monitoring that identifies sensitive data, system changes, and quality issues. Stay proactive and ensure your data drives smarter decisions.
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