Technical insights to a scalable experimentation system

Establishing trust in experimental results is crucial for successful experimentation programs in the tech industry, where scalability often suffers due to information overload and managerial complexity, rather than tangible costs like databases or storage. Sophisticated tests can lead to p-hacking and undermine trust, while the managerial incentive structures often discourage addressing these issues. To ensure scalability, systems must be designed to keep operational costs increasing sub-linearly with scale, addressing both information processing and managerial challenges. Key insights for scalable experimentation include integrating feature flags for default-on AB testing, separating metrics from logging data, maintaining a single source of truth for data interpretation, and automating checks for business decision accuracy. A well-designed system not only reduces costs but also enables experimentation to become a collaborative effort, allowing different roles to contribute their strengths and fostering continuous value extraction through credible causal evidence, ultimately enhancing product development and returns over time.