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Beyond Lines of Code: How to Measure Software Productivity in Global Capability Centers?

Beyond Lines of Code: How to Measure Software Productivity in Global Capability Centers?

14 Mar, 2024

Peter Drucker’s timeless wisdom, “You can’t improve what you don’t measure,” remains relevant across industries, particularly in the context of Global Capability Centers (GCCs). Leaders in this space often grapple with the question of whether the global center can match the value and output of talent from the headquarters. The challenge, however, lies in determining how to measure Software Productivity in GCCs.

Measuring Software Productivity has always been a challenge for the software industry, with a myriad of metrics to choose from. The complexity escalates in GCCs, where teams are co-located, comprising a mix of HQ and local teams. To address this complexity, certain key questions must be answered to triangulate an effective strategy for Software Productivity.

Why measure Software Productivity in GCCs?

Scaling a software team to a new location diminishes visibility, making it imperative to measure the performance of the new center promptly. As the number of engineers and code volumes increases, tracking roadblocks and identifying productivity issues become increasingly challenging. The longer it takes to discover a mistake, the more costly it becomes to rectify.

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Setting up a new Software Development Center or GCC brings inherent pressures to achieve value as fast as possible – the need to have a team that is fully functional with outcomes equivalent to other global centers. In addition to this, if teams are being transitioned, they need to ensure that no customer/existing processes get impacted. While scaling a new GCC you tackle issues like onboarding partial teams, budgeting additional hours for knowledge transfer, and developing domain capabilities in an accelerated timeline. With nearly every company across industries pursuing technology-driven transformation, the demand for innovation at breakneck speed adds pressure to deliver software commits faster.

Amidst these challenges, having accurate metrics serves as an indispensable compass for new centers. Ensuring that the right metrics are measured allows a team to forecast its work and plan resources and bandwidth effectively, aiding the organization in understanding what can be accomplished in each period and committing accordingly to customers.

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What do we measure?

The general rule of thumb dictates that the yardstick should be the same for the global center and HQ (or the company overall). Only through an apples-to-apples comparison can the equivalence between the global center and HQ/other global centers be gauged.

Software Productivity measurement should be tied to business outcomes and designed to identify roadblocks for solutions. Conversations with GCC leaders in India revealed that Software Productivity metrics typically fall into three categories:

  1. Velocity – Time to Value
  2. Roadmap Completion
  3. Quality Metrics

All these attributes are complementary, and excelling in one at the cost of the other is counterproductive. Ideally, an upward trend in velocity and roadmap completion indicates team improvement, while improvement in quality metrics ensures control over technical debt. Despite the variety of metrics, it’s crucial to recognize the uniqueness of every engineer and team.

The focus should be on identifying roadblocks and continuously improving. This can be done at the team level – having sprint retros play an important part for a team to reflect on what went well and how to course-correct. This is a great time to reflect on what investment the team needs moving forward to make itself more efficient. Ultimately, we need to make metrics actionable. Simply accumulating measurements without the ability to act on the insights they provide renders the whole exercise hollow.


How are upcoming AI tools enhancing Software Productivity in GCCs?

  • AI Code Reviews: Studies suggest that the human mind can review 400 lines of code before saturation. AI review tools such as CodeQL and DeepCode conduct reviews in seconds, reducing review time by 20-30%.
  • Reducing Tech Debt: AI tools like Testim and Applitools offer automated testing, failure analysis at scale, and comprehensive test coverage. These tools increase efficiency, enabling developers to focus on high-level tasks and avoiding repetitive coding work, freeing up 10-20% of the budget typically allocated to fixing technical debt.
  • AI Pair Programmers: Github Copilot’s revolutionary predictive coding capabilities could assist experienced programmers by providing targeted prompts. The evolution of this technology warrants close observation for potential leverage.
  • Documentation: On average, engineers spend 10-20% of their time on documentation. AI-powered NLP tools can reduce this time to seconds, varying based on the project and company.

The mantra remains: measure, adapt, and innovate. The symbiosis of effective metrics, continuous improvement strategies, and cutting-edge AI tools propels global Software Development Centers toward enhanced productivity and sustained success. As the industry evolves, the integration of AI into Software Development practices promises to reshape the future, making efficiency and innovation more attainable than ever before.

Get in touch with us at to set up your GCC and realize the full potential of Software Productivity


  • Centers of Excellence
  • Enterprise Software companies
  • GCCs in India
  • global capability center
  • India GCCs
  • Software Engineering
  • Software Engineering Productivity
  • Software Talent
Nilesh Thakker, President, Zinnov
Amita Goyal, Partner, Zinnov
Rohit Nair, Engagement Manager, Zinnov

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