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Is Self-Service BI a False Promise? Lei Tang of Fabi.ai Thinks So
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Manage episode 502930968 series 3418247
- The limitations of traditional self-service BI and how AI is addressing them
- Building secure, context-aware AI systems for data analysis
- The future of human-AI interaction in business intelligence
- Technical insights into modern BI platform architecture
- Vision for proactive, AI-driven business insights
- Why traditional self-service BI has failed to deliver on its promises and how AI can bridge the gap
- How to build an AI-native BI platform that combines SQL, Python, and natural language processing
- The framework for implementing "Vibe-analytics" - a new paradigm of AI-powered visual analytics
- Why context engineering and semantic understanding are crucial for accurate AI-driven analysis
- How to balance security and accessibility when deploying AI-powered analytics tools
- The future of BI platforms as proactive insight generators rather than passive dashboards
- Why caching and stateful environments are essential for responsive AI-powered analytics
- How to leverage AI to translate business questions into accurate technical queries while maintaining data integrity
If you enjoyed this episode, make sure to subscribe, rate, and review it on Apple Podcasts, Spotify, and YouTube Podcasts. Instructions on how to do this are here.
"For the past decade, it's really difficult to make sure the self-service BI can work. And then now with AI, the worst part is that it can run properly, but the numbers are wrong." - Lei
"If you talk to anybody working in the BI space, like self-service BI, that has been termed for maybe for the past decade. But I have to say that is a false promise." - Lei
"We're saying that we really want those data team to be able to, like, say, what type of data is exposed to, like, say, less technical folks." - Lei
"In order to build AI native BI, I would say the focus should be how human interact with AI." - Lei
"We believe that, essentially, this BI system or, like, AI BI system would be more like a agent, and then it'll actually looking for, like, business opportunities and insight and surface to you." - Lei
"The one common theme I have been experiencing is that normally would work with other business stakeholders, could be marketing, could be operations, could be sales." - Lei
"We strongly believe that BI should be stored as code." - Lei
"Enterprise data tends to be very noisy, very complex." - Lei
"The semantics of itself becomes part of the context for the AI engine." - Lei
"Most organizations, the data, like the schema, the kind of business, like metrics and logic, has been constantly evolving." - Lei
Resources
- Fabi.ai - AI-native BI platform
- Firebolt (firebolt.io) - Cloud data warehouse platform
- Firebolt Core - Free self-hosted query engine
- Looker - BI Platform
- Tableau - BI Platform
- Sisense - BI Platform
- Snowflake - Data Warehouse
- BigQuery - Data Warehouse
- PostgreSQL - Database
- SQL Alchemy - Database toolkit
- Pandas - Data analysis library
- Join Firebolt Discord Community
- Join Firebolt GitHub Discussions
- Firebolt Core Github Repository
- [email protected]
Previous guests include: Joseph Machado of Linkedin, Metthew Weingarten of Disney, Joe Reis and Matt Housely, authors of The Fundamentals of Data Engineering, Zach Wilson of Eczachly Inc, Megan Lieu of Deepnote, Erik Heintare of Bolt, Lior Solomon of Vimeo, Krishna Naidu of Canva, Mike Cohen of Substack, Jens Larsson of Ark, Gunnar Tangring of Klarna, Yoav Shmaria of Similarweb and Xiaoxu Gao of Adyen.
Check out our three most downloaded episodes:
63 에피소드
Fetch error
Hmmm there seems to be a problem fetching this series right now. Last successful fetch was on October 07, 2025 11:41 ()
What now? This series will be checked again in the next day. If you believe it should be working, please verify the publisher's feed link below is valid and includes actual episode links. You can contact support to request the feed be immediately fetched.
Manage episode 502930968 series 3418247
- The limitations of traditional self-service BI and how AI is addressing them
- Building secure, context-aware AI systems for data analysis
- The future of human-AI interaction in business intelligence
- Technical insights into modern BI platform architecture
- Vision for proactive, AI-driven business insights
- Why traditional self-service BI has failed to deliver on its promises and how AI can bridge the gap
- How to build an AI-native BI platform that combines SQL, Python, and natural language processing
- The framework for implementing "Vibe-analytics" - a new paradigm of AI-powered visual analytics
- Why context engineering and semantic understanding are crucial for accurate AI-driven analysis
- How to balance security and accessibility when deploying AI-powered analytics tools
- The future of BI platforms as proactive insight generators rather than passive dashboards
- Why caching and stateful environments are essential for responsive AI-powered analytics
- How to leverage AI to translate business questions into accurate technical queries while maintaining data integrity
If you enjoyed this episode, make sure to subscribe, rate, and review it on Apple Podcasts, Spotify, and YouTube Podcasts. Instructions on how to do this are here.
"For the past decade, it's really difficult to make sure the self-service BI can work. And then now with AI, the worst part is that it can run properly, but the numbers are wrong." - Lei
"If you talk to anybody working in the BI space, like self-service BI, that has been termed for maybe for the past decade. But I have to say that is a false promise." - Lei
"We're saying that we really want those data team to be able to, like, say, what type of data is exposed to, like, say, less technical folks." - Lei
"In order to build AI native BI, I would say the focus should be how human interact with AI." - Lei
"We believe that, essentially, this BI system or, like, AI BI system would be more like a agent, and then it'll actually looking for, like, business opportunities and insight and surface to you." - Lei
"The one common theme I have been experiencing is that normally would work with other business stakeholders, could be marketing, could be operations, could be sales." - Lei
"We strongly believe that BI should be stored as code." - Lei
"Enterprise data tends to be very noisy, very complex." - Lei
"The semantics of itself becomes part of the context for the AI engine." - Lei
"Most organizations, the data, like the schema, the kind of business, like metrics and logic, has been constantly evolving." - Lei
Resources
- Fabi.ai - AI-native BI platform
- Firebolt (firebolt.io) - Cloud data warehouse platform
- Firebolt Core - Free self-hosted query engine
- Looker - BI Platform
- Tableau - BI Platform
- Sisense - BI Platform
- Snowflake - Data Warehouse
- BigQuery - Data Warehouse
- PostgreSQL - Database
- SQL Alchemy - Database toolkit
- Pandas - Data analysis library
- Join Firebolt Discord Community
- Join Firebolt GitHub Discussions
- Firebolt Core Github Repository
- [email protected]
Previous guests include: Joseph Machado of Linkedin, Metthew Weingarten of Disney, Joe Reis and Matt Housely, authors of The Fundamentals of Data Engineering, Zach Wilson of Eczachly Inc, Megan Lieu of Deepnote, Erik Heintare of Bolt, Lior Solomon of Vimeo, Krishna Naidu of Canva, Mike Cohen of Substack, Jens Larsson of Ark, Gunnar Tangring of Klarna, Yoav Shmaria of Similarweb and Xiaoxu Gao of Adyen.
Check out our three most downloaded episodes:
63 에피소드
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