The volume of data-focused communities has exploded over the past couple of years, along with a never-ending stream of think pieces on building a data culture. With so much buzz, we gotta ask: are we truly making strides toward building a more effective data industry, or are we merely swept up in the hype? Are our efforts yielding tangible results, and are they worth the investment?
Join us as our panel of industry experts share their unfiltered perspectives across a few targeted areas:
What's the impact of rapidly proliferating data communities on the broader industry? What are we getting right, and where are we fundamentally failing?
With rapid advancements in AI and data tooling, are we actually closer to having well-functioning and impactful data teams? Have we adequately addressed the underlying people/process problems that generally fall under the ""data culture"" umbrella?
How and why do companies and communities continue to fail to rally around data despite myriad tooling options and endless think-pieces about what it takes to get it right?
Benn Stancil is ThoughtSpot’s Field CTO. He joined ThoughtSpot in 2023 as part of its acquisition of Mode, where he was a cofounder and CTO. While at Mode, Benn held roles leading Mode’s data, product, marketing, and executive teams. He regularly writes about data and technology at benn.substack.com. Prior to founding Mode, Benn worked on analytics teams at Microsoft and Yammer.
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.
Caitlin Hudon is a data scientist at Figma. She has over a decade of experience in data science across many industries including tech, IoT, edtech, marketing, higher education, non-profits, and start-ups, which translate to 10+ years of learning from data mistakes.