Improving R&D Efficiency Through High‑Quality Requirements: Practices and Metrics

Since last year, the topic of R&D efficiency has been hot, and with the hype around ChatGPT and large language models the author reflects on key questions such as how efficiency relates to business value, which metrics can measure this link, the purpose of R&D efficiency, and how to evaluate both efficiency and business outcomes.

Reading "High‑Quality Requirements" reveals that the core of improving R&D efficiency lies in delivering high‑quality requirements, as most waste stems from requirement rework and unclear specifications.

Data from Huawei and other industry sources show that a large proportion of developed features (up to 80% in some cases) are never used by customers, highlighting severe over‑production and the need for better requirement validation.

The author recounts the typical developer mindset of rushing code without understanding the business problem, which leads to massive late‑stage requirement changes and project risk, emphasizing the necessity of early involvement in the requirement phase.

Four fundamental practices are introduced: (1) Understand the problem before converging on a solution, using a checklist of questions about purpose, target users, solution scope, MVP, validation, and success criteria; (2) Define clear business goals; (3) Define the solution’s boundaries and context; (4) Establish the team, identify stakeholders, and manage ecosystem relationships.

Metrics such as North‑Star indicators, roadmaps, and MVP definitions are suggested to track progress, and stakeholder identification is highlighted as essential for successful execution.

The article concludes with a brief promotion of a DevOps engineer certification program, encouraging readers to enroll and acquire the 400+ technical skills needed for end‑to‑end R&D efficiency.