What is an Experimentation program and Who is Involved? (Experimentation Program Series: Guide 02)

2022-04-02 · Source: ML in Production · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Project & Product Management · Depth: Intermediate, medium

Summary

An experimentation program (ExPr) is a structured mechanism enabling companies to use randomized controlled experiments for generating positive business results. It integrates people, processes, and infrastructure to optimize products and services. While technology for user splitting and results computation is crucial, a successful ExPr also requires cross-functional participation from various business units, data science, and technology teams. These primary stakeholders are responsible for ideating, planning, implementing, and analyzing experiments. Additionally, secondary stakeholders, such as other business units impacted by experimental outcomes, must be kept informed to ensure broad adoption of successful experimental results. Future discussions will detail the process and technology components of ExPrs.

Key takeaway

For AI Product Managers or Data Scientists looking to establish or mature an experimentation program, prioritize building a cross-functional team involving business units, engineering, and data science. Your data science team should strategically bridge business objectives with technical implementation, while ensuring all impacted secondary stakeholders are actively informed to maximize the adoption and impact of experimental results across the organization.

Key insights

Successful experimentation programs integrate people, processes, and infrastructure to drive business results through randomized controlled experiments.

Principles

• ExPrs require cross-functional stakeholder participation.
• Data science bridges business goals and engineering requirements.
• Informing secondary stakeholders ensures result adoption.

Method

An ExPr involves ideating, planning, implementing, and analyzing experiments, followed by change management to operationalize successful outcomes, requiring collaboration across business units, engineering, and data science.

In practice

• Involve engineering for proper telemetry instrumentation.
• Data scientists define experiment parameters like sample size.
• Keep impacted business units informed of experiment results.

Topics

• Experimentation Programs
• Randomized Controlled Experiments
• Stakeholder Management
• Data Science Roles
• Engineering Infrastructure

Best for: Data Scientist, MLOps Engineer, AI Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by ML in Production.