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Insights on Data Quality, Management, and Prioritization in Enterprises
Exploring the importance of quality data management, challenges of accessing data from various applications, and the significance of prioritizing data sources for successful insights.
Snowflake has been foundational in the data space for years. In the mid-2010s, the platform was a major driver of moving data to the cloud. More recently, it's become apparent that combining data and AI in the cloud is key to accelerating innovation. Snowflake has been rapidly adding AI features to provide value to the modern data stack, but what’s really been going on under the hood?
At the time of recording, Sridhar Ramaswamy was the SVP of AI at Snowflake, being appointed CEO at Snowflake in February 2024. Sridhar was formerly Co-Founder of Neeva, acquired in 2023 by Snowflake. Before founding Neeva, Ramaswamy oversaw Google's advertising products, including search, display, video advertising, analytics, shopping, payments, and travel. He joined Google in 2003 and was part of the growth of AdWords and Google's overall advertising business. He spent more than 15 years at Google, where he started as a software engineer and rose to SVP of Ads & Commerce.
In the episode, Richie and Sridhar explore Snowflake and its uses, how generative AI is changing the attitudes of leaders towards data, how NLP and AI have impacted enterprise business operations as well as new applications of AI in an enterprise environment, the challenges of enterprise search, the importance of data quality, management and the role of semantic layers in the effective use of AI, a look into Snowflakes products including Snowpilot and Cortex, the collaboration required for successful data and AI projects, advice for organizations looking to improve their data management and much more.
About the AI and the Modern Data Stack DataFramed Series
This week we’re releasing 4 episodes focused on how AI is changing the modern data stack and the analytics profession at large. The modern data stack is often an ambiguous and all-encompassing term, so we intentionally wanted to cover the impact of AI on the modern data stack from different angles. Here’s what you can expect:
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