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Getting ROI from Experimentation: How AB Experimentation plays out in Organizations

Chetan Sharma Chetan Sharma | Founder & CEO | Eppo
Chad Sanderson Chad Sanderson | CEO | Gable.ai

Experimentation is a critical part of the modern product development lifecycle. In order to implement experimentation effectively, data teams must invest in robust, scalable assignment systems, metrics computation, and complex statistical analysis frameworks. Most companies get started with decentralized analysis, in Jupyter or mode notebooks. Others centralize piecemeal with a looker dashboard and homegrown feature flagging, but leave crucial workflows to be done ad hoc or not at all. This talk will review the core elements of the modern experimentation stack, which components must be closely vetted, and examples of how bad experimentation can be more damaging to an organization than no experimentation at all. Listeners will leave with an understanding of how to spot a faulty experimentation stack with a review of alternative solutions. 

Chetan Sharma
Chetan Sharma
Founder & CEO | Eppo

Che Sharma is the Founder & CEO of Eppo, a next gen AB experimentation platform that is designed to spur entrepreneurial culture. As the 4th data scientist at Airbnb and early data scientist at companies like Webflow, Che has been focused on the maturity curve of growth stage companies and how to establish data as a central stakeholder of decision making. Che previously led the team that developed Airbnb's knowledge repo, and has led data teams focused on production machine learning and instrumentation integrity.

Chad Sanderson
Chad Sanderson
CEO | Gable.ai

Chad Sanderson is passionate about data quality, and fixing the muddy relationship between data producers and consumers. He is a former Head of Data at Convoy, a LinkedIn writer, and a published author. He lives in Seattle, Washington, and is the Chief Operator of the Data Quality Camp. He is currently the CEO & Co-Founder of Gable, a collaboration, communication, and change management platform for data teams operating at scale.

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