The Data Splash

An Al Data Engineering Podcast

The Data Splash is a business podcast hosted by Upriver, with 5 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Upriver hosts The Data Splash, a business show with 5 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

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  Episode #6

  From Data Police to AI Enabler – The Evolution of Data Governance - with Tiankai Feng

  Aug 13, 202624 min

  Tiankai Feng became a governance leader because he wouldn't stop complaining about bad data. Completely understandable! He breaks down the three root causes of data quality problems - technical, process, and human - and why the human ones are nearly impossible to fix downstream. Plus what AI changed: Models need far more historical data than governance ever scoped, and GenAI shifted focus to unstructured documents nobody has governed. Also: the shared drive with fifty versions of the same PDF, and why use cases beat cleanup projects. Hosted by Ido Bronstein of Upriver. ⏱ CHAPTERS 0:00 Introduction 1:06 The 30-Second Splash 1:56 Meet Tiankai: career path and the two books 3:35 Why he left analytics for governance 6:51 Why data quality is so hard: intended vs. actual data use 8:42 Three root causes: technical, process, and human error 11:06 What maturity looks like: from reactive to proactive 13:24 Why use cases beat cleanup projects 16:23 How AI changed the governance mandate 19:44 Governing unstructured data 21:36 How AI is making stewardship leaner 24:05 The data governance songs 24:49 Advice for new stewards 26:11 Closing takeaways ABOUT DATA SPLASH: Data Splash is a podcast for data engineers, data leaders, and anyone trying to make sense of AI and data right now. Brought to you by Upriver. Subscribe for new episodes weekly. LINKS • Upriver: [ https://www.upriverdata.com/ ] • Connect with Tiankai Feng: [ https://www.linkedin.com/in/tiankaifeng/ ] • Connect with Ido Bronstein: [ https://www.linkedin.com/in/ido-bronstein/ ] #DataGovernance #AI #DataEngineering #LLMs #AIAgents

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  Episode #5

  How Data Teams Power AI - with Abe Gong

  Jul 8, 202628 minS0

  <p>Teams keep attaching AI agents to their CRM, then realize they need to deduplicate it first. Abe Gong, CEO and co-founder of Great Expectations, joins Ido Bronstein to explain why organizational memory – not the model – is the real bottleneck for AI. They cover why "context graph" is the most overhyped phrase in data, the curation test every knowledge base should pass, how agents ended the analyst bottleneck, and why data teams must evolve from technical function to the organization's arbiter of truth. Plus: why a good knowledge base makes company culture legible for the first time.</p><p></p><p>⏱...

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  Episode #5

  How Data Teams Power AI - with Abe Gong

  Jul 8, 202628 min

  Teams keep attaching AI agents to their CRM, then realize they need to deduplicate it first. Abe Gong, CEO and co-founder of Great Expectations, joins Ido Bronstein to explain why organizational memory – not the model – is the real bottleneck for AI. They cover why "context graph" is the most overhyped phrase in data, the curation test every knowledge base should pass, how agents ended the analyst bottleneck, and why data teams must evolve from technical function to the organization's arbiter of truth. Plus: why a good knowledge base makes company culture legible for the first time. ⏱ CHAPTERS 0:00 Welcome and guest intro 1:02 The 30 Second Splash 1:45 Abe's path from algorithms to engineering problems 4:03 What actually counts as a knowledge base 6:12 Curation: why storage alone isn't enough 8:24 How companies manage knowledge today 10:04 The landscape: search, semantic layers, and skills 12:31 Shifting bottlenecks: code review and the end of the analyst gate 14:23 Nine definitions of churn — democratization, solved? 15:30 How knowledge bases reshape the data team 18:49 The data team as the organization's judge 19:52 Planning for agents that do the technical work 22:04 The prize: human–agent collaboration 23:34 Your knowledge base is your culture 26:13 Takeaways and wrap 🎙 ABOUT DATA SPLASH: Data Splash is a podcast for data engineers, data leaders, and anyone trying to make sense of AI and data right now. Brought to you by Upriver. 🔔 Subscribe for new episodes weekly. 🔗 LINKS • Upriver: [ https://www.upriverdata.com/ ] • Connect with Abe Gong: [ https://www.linkedin.com/in/abe-gong-8a77034/ ] • Connect with Ido Bronstein: [ https://www.linkedin.com/in/ido-bronstein/ ] #DataEngineering #AI #DataPlatform #LLMs #AIAgents

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  Episode #4

  What Data Should You Rely On? (AI + Real-Time Data) - with Amaury Desrosiers, Nimble

  Jun 17, 202622 minS0

  <p>For a decade, the data moat was infrastructure—whoever could afford 20 engineers to maintain scrapers won. Nimble's Amaury Desrosiers explains why that moat is gone, how external web data became a first-class citizen alongside your warehouse, and why real-time will soon be a default property, not a category. Plus the three-circle model for internal vs. external data and the one move every data leader should make tomorrow.</p><p></p><p>⏱ CHAPTERS 0:00 Introduction 0:51 The 30 Second Splash 1:44 What Nimble does 2:08 Questions internal data can't answer 3:22 The three concentric circles 4:28 Ground truth vs. context — joining the two 5:40 Why web data used to be...

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  Episode #4

  What Data Should You Rely On? (AI + Real-Time Data) - Amaury Desrosiers, Nimble

  Jun 17, 202622 min

  For a decade, the data moat was infrastructure—whoever could afford 20 engineers to maintain scrapers won. Nimble's Amaury Desrosiers explains why that moat is gone, how external web data became a first-class citizen alongside your warehouse, and why real-time will soon be a default property, not a category. Plus the three-circle model for internal vs. external data and the one move every data leader should make tomorrow. ⏱ CHAPTERS 0:00 Introduction 0:51 The 30 Second Splash 1:44 What Nimble does 2:08 Questions internal data can't answer 3:22 The three concentric circles 4:28 Ground truth vs. context — joining the two 5:40 Why web data used to be a nightmare 6:27 Solving connection and parsing end to end 7:53 The moat moves from infra to usage 9:32 Why real-time value compounds 11:55 Batch vs. ad hoc pipelines 14:24 One layer, not one connector per site 15:34 Schema by use case 16:58 Scale changed, the data model didn't 18:58 Three predictions for the next three years 20:25 What data leaders should do tomorrow 21:19 Takeaways and wrap 🎙 ABOUT DATA SPLASH: Data Splash is a podcast for data engineers, data leaders, and anyone trying to make sense of AI and data right now. Brought to you by Upriver. 🔔 Subscribe for new episodes weekly. 🔗 LINKS • Upriver: [ https://www.upriverdata.com/ ] • Connect with Amaury Desrosiers: [ https://www.linkedin.com/in/amaurydesrosiers ] • Connect with Omri Lifshitz: [ https://www.linkedin.com/in/omri-lifshitz-8a531814a/ ] #DataEngineering #AI #DataPlatform #LLMs #AIAgents

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