Narrator.ai: Intelligent Analyses with Ahmed Elsamadisi

A data-driven organization collects a wide variety of data to help in strategic decision-making. The cost of storing large amounts and variety of data has dropped dramatically in the last two decades, but too much unstructured data may not improve decision-making, and can even lead to “analysis paralysis.” Organizations react by extracting the most important, actionable data and placing it into a data warehouse, which has a predesigned structure meant to streamline the data in preparation for analysis. The key challenge with this approach is identifying what should be streamlined, and how to structure the data warehouse to focus on the most important, actionable items. This is especially important for organizations seeking to scale, as the necessary structure to generate the most relevant insights may change as the organization grows.

Narrator is building data intelligence that uses a simple, proprietary Universal Data Model to help organizations streamline their data warehousing. Narrator is built on the belief that data tells the story of a system, and its platform empowers organizations to use those stories to make better decisions.

Ahmed Elsamadisi is the founder and CEO of Narrator. Before founding Narrator, he spent several years working in data analysis and algorithm design for WeWork, Raytheon, and Cornell’s Autonomous Systems Laboratory. He joins the show today to talk about how Narrator generates the most actionable insights from a data warehouse, why a Universal Data Model is so important when scaling, and what makes Narrator’s approach to data analysis different.

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Transcript

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