CTO at Mode & Field CTO at ThoughtSpot, Benn Stancil, discusses the counter-intuitive success of AI-assisted coding in analytics workflows. He explores the potential of generative AI in problem-solving, future analytics workflows, and AI systems providing qualitative data. The podcast delves into the impact of AI on analytics, the evolution of data tools, and the necessary skill sets for data practitioners in the era of large language models.
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Quick takeaways
Generative AI tools are revolutionizing analytics workflows by assisting in data work and problem-solving.
AI's potential for unpredictable innovations in data analytics requires analysts to adapt and leverage AI capabilities effectively.
Deep dives
The potential of AI to bring about unexpected and weird changes
AI, especially large language models (LLMs), have the potential to usher in unprecedented and unpredictable changes. Just like the iPhone brought about new and unimaginable applications like TikTok, AI could lead to innovations that we can't currently conceive. LLMs, with their creative and open-ended nature, can go beyond simple tasks like chatbots and offer unexpected and unconventional solutions. As we further explore AI's capabilities, our understanding will expand and new ideas will emerge.
The impact of AI-assisted analytics workflows
AI-assisted analytics workflows, powered by generative AI tools and co-pilots, have emerged as one of the most successful applications of generative AI. This podcast episode explores how AI is transforming the modern data stack and the analytics profession. It discusses the counter-intuitive success of AI in assisting with data work and problem-solving. With AI as a companion, organizations and individuals can achieve greater success in their data and AI endeavors.
The future of data interfaces and analyst's skill set
The future of data interfaces and analytics will be influenced by the growing capabilities of AI, specifically large language models. While it's hard to predict exact changes, analysts will need to adapt their skill sets to leverage the power of AI. This may involve learning how to effectively navigate and use large language models, particularly in summarizing unstructured data and evaluating their insights. Analysts may need to become proficient in understanding and extracting valuable information from AI-generated analysis. The ability to moderate, interpret, and apply critical thinking to AI-generated insights will be crucial.
The short-term prospects of AI and analytics
In the short term, AI and analytics will go through an experimental phase as organizations and individuals explore the potential of AI tools. There will be a range of attempts, some successful and others unsuccessful, to integrate AI into analytics workflows. Chatbots and co-pilots will be experimented with, but their effectiveness will vary. The key will be finding the right applications and use cases where AI truly enhances analytics processes. Through experimentation, we can uncover valuable and innovative ways to leverage AI in analytics and data-related tasks.
One of the biggest surprises of the generative AI revolution over the past 2 years lies in the counter-intuitiveness of its most successful use cases. Counter to most predictions made about AI years ago, AI-assisted coding, specifically AI-assisted data work, has been surprisingly one of the biggest killer apps of generative AI tools and copilots. However, what happens when we take this notion even further? How will analytics workflows look like when generative AI tools can also assist us in problem-solving? What type of analytics use cases can we expect to operationalize, and what tools can we expect to work with when AI systems can provide scalable qualitative data instead of relying on imperfect quantitative proxies? Today’s guest calls this future “weird”.
Benn Stancil is the Field CTO at ThoughtSpot. He joined ThoughtSpot in 2023 as part of its acquisition of Mode, where he was a Co-Founder 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.
Throughout the episode, Benn and Adel talk about the nature of AI-assisted analytics workflows, the potential for generative AI in assisting problem-solving, how he imagines analytics workflows to look in the future, and a lot 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:
Why the Future of AI in Data will be Weird with Benn Stancil, CTO at Mode & Field CTO at ThoughtSpot — Covering how AI will change analytics workflows and tools
How Databricks is Transforming Data Warehousing and AI with Ari Kaplan, Head Evangelist & Robin Sutara, Field CTO at Databricks — Covering Databricks, data intelligence and how AI tools are changing data democratization
Adding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at Snowflake — Covering Snowflake and its uses, how generative AI is changing the attitudes of leaders towards data, and how to improve your data management
Accelerating AI Workflows with Nuri Cankaya, VP of AI Marketing & La Tiffaney Santucci, AI Marketing Director at Intel — Covering AI’s impact on marketing analytics, how AI is being integrated into existing products, and the democratization of AI