This is a talk about how we thought we had Big Data, and we built everything planning for Big Data, but then it turns out we didn't have Big Data, and while that's nice and fun and seems more chill, it's actually ruining everything, and I am here asking you to please help us figure out what we are supposed to do now.

📓 Resources Big Data is Dead: https://motherduck.com/blog/big-data-... Small Data Manifesto: https://motherduck.com/blog/small-dat... Is Excel Immortal?: https://benn.substack.com/p/is-excel-immortal Small Data SF: https://www.smalldatasf.com/

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Mode founder David Wheeler challenges the data industry's obsession with "big data," arguing that most companies are actually working with "small data," and our tools are failing us. This talk deconstructs the common sales narrative for BI tools, exposing why the promise of finding game-changing insights through data exploration often falls flat. If you've ever built dashboards nobody uses or wondered why your analytics platform doesn't deliver on its promises, this is a must-watch reality check on the modern data stack.

We explore the standard BI demo, where an analyst uncovers a critical insight by drilling into event data. This story sells tools like Tableau and Power BI, but it rarely reflects reality, leading to a "revolving door of BI" as companies swap tools every few years. Discover why the narrative of the intrepid analyst finding a needle in the haystack only works in movies and how this disconnect creates a cycle of failed data initiatives and unused "trashboards."

The presentation traces our belief that "data is the new oil" back to the early 2010s, with examples from Target's predictive analytics and Facebook's growth hacking. However, these successes were built on truly massive datasets. For most businesses, analyzing small data results in noisy charts that offer vague "directional vibes" rather than clear, actionable insights. We contrast the promise of big data analytics with the practical challenges of small data interpretation.

Finally, learn actionable strategies for extracting real value from the data you actually have. We argue that BI tools should shift focus from data exploration to data interpretation, helping users understand what their charts actually mean. Learn why "doing things that don't scale," like manually analyzing individual customer journeys, can be more effective than complex models for small datasets. This talk offers a new perspective for data scientists, analysts, and developers looking for better data analysis techniques beyond the big data hype.