ENABLING ANALYTICS GOVERNANCE IN AGILE PRODUCT TEAMS: A SCALABLE TAGGING AND QA FRAMEWORK

Abstract

In modern digital product development, data analytics has become foundational for agile
decision-making, experimentation, and customer-centric design. However, as organizations
scale, analytics governance often becomes fragmented, leading to data quality issues,
inconsistent metric definitions, and a lack of trust in insights. This paper proposes a scalable
tagging and quality assurance (QA) framework tailored for agile product teams. The framework
addresses challenges such as inconsistent event instrumentation, lack of metadata management,
and siloed tracking strategies. It enables organizations to align business goals with measurable
outcomes while maintaining the flexibility and speed inherent in agile methodologies. By
combining governance, automation, and collaboration, this framework supports scalable and
trustworthy analytics that empower data-informed product development.