982: In Case You Missed It in March 2026
Jon Krohn rounds up March’s interviews in this ICYMI episode. Hear from AI and data science experts across the fields of education and business in this wide-ranging series of clips that take listeners from the Renaissance to the near future. Guests include Lin Quiao (Episode 971), Chris Fregly (Episode 973), Zack Kass (Episode 975), Kyunghyun Cho (Episode 977), and Rohit Choudhary (Episode 979).
Additional materials: www.superdatascience.com/982
Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.
979: Agentic Data Management and the Future of Enterprise AI, with Rohit Choudhary
For years, Jon has been quoting the stat that the world's data is roughly doubling every year. His guest today says that’s way too conservative, he’s seeing enterprise data soon growing at close to 10x per year. And most organizations are nowhere near ready for what that means. In this episode, Rohit Choudhary, founder and CEO of Acceldata, explains how the agentic data management platform his team has built helps enterprises make their increasingly vast amounts of data self-aware, self-optimizing, and AI-ready. He breaks down why governance needs to be operational and real-time rather than a one-time compliance exercise, and shares his view on why the most valuable professionals in the age of AI won’t be the best programmers, they’ll be the ones with the clearest thinking and the deepest domain expertise.
Additional materials: www.superdatascience.com/979
Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.
In this episode you will learn:
(03:26) How Rohit coined the term “data observability”
(06:04) Agentic data management use cases
(12:46) Why fixing data at the point of consumption is 1000x more expensive
(30:49) Career paths and skills for the age of AI
(42:38) Why enterprise data will soon grow at nearly 10x per year
964: In Case You Missed It in January 2026
In this first of the year ICYMI episode, Jon Krohn selects his favorite moments from January’s SuperDataScience interviews. Listen to why incentivizing workers is the best way to get them to disclose their use of AI tools and pave the way for an AI-forward future, how AI continues to mimic human development in its own evolution, the importance of evaluation in building AI systems, and how to keep your best employees (and also: how to know your value) with guests Sadie St. Lawrence, Ashwin Rajeeva, Sinan Ozdemir, Vijoy Pandey, and Ethan Mollick.
Additional materials: www.superdatascience.com/964
Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.
957: How AI Agents Are Automating Enterprise Data Operations, with Ashwin Rajeeva
AI agents, data lakes, and managing data sprawl: Ashwin Rajeeva, cofounder and CTO of Acceldata, speaks to Jon Krohn about how the agentic data management startup raised over $100 million in venture capital to expand its business in automating data quality assurance as well as cataloguing and pipeline maintenance across enterprise environments. Acceldata utilizes multiple agents to solve enterprise-grade questions with company data. It also uses autonomous data pipelines that can detect and fix issues without human intervention, and the platform’s agentic data management system ADM also lets humans stay in the loop wherever needed.
This episode is brought to you by the Dell, by Intel, by Fabi and by Cisco.
Additional materials: www.superdatascience.com/957
Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.
In this episode you will learn:
(03:25) About Acceldata and xLake
(15:03) Autonomous data pipelines
(21:02) How and when to keep humans in the AI loop
(27:43) How Acceldata solves ‘data sprawl’
(31:53) Habits of successful tech leaders
846: Making Enterprise Data Ready for AI, with Anu Jain and Mahesh Kumar
In this Five-Minute Friday, Jon Krohn speaks to Anu Jain, CEO of Nexus Cognitive, and Mahesh Kumar, CMO of Acceldata. They talk about the importance of updating data, especially for predictive models that make key financial decisions for a company, as well as the current state of data governance and why it’s overdue its own update.
Additional materials: www.superdatascience.com/846
Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.
Data Observability: MultiCloud, GenAI Make Challenges Harder
Rohit Choudhary, co-founder and CEO of Acceldata, placed an early bet on data observability, which has proven prescient. In a New Stack Makers podcast episode, Choudhary discussed three key insights that shaped his vision: First, the exponential growth of data in enterprises, further amplified by generative AI and large language models. Second, the rise of a multicloud and multitechnology environment, with a majority of companies adopting hybrid or multiple cloud strategies. Third, a shortage of engineering talent to manage increasingly complex data systems.
As data becomes more essential across industries, challenges in data observability have intensified. Choudhary highlights the complexity of tracking where data is produced, used, and its compliance requirements, especially with the surge in unstructured data. He emphasized that data's operational role in business decisions, marketing, and operations heightens the need for better traceability. Moving forward, traceability and the ability to manage the growing volume of alerts will become areas of hyper-focus for enterprises.
Learn more from The New Stack about data observability:
What Is Data Observability and Why Does It Matter?
The Looming Crisis in the Observability Market
The Growth of Observability Data Is Out of Control!
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Taking A Multidimensional Approach To Data Observability At Acceldata
Summary
Data observability is a term that has been co-opted by numerous vendors with varying ideas of what it should mean. At Acceldata, they view it as a holistic approach to understanding the computational and logical elements that power your analytical capabilities. In this episode Tristan Spaulding, head of product at Acceldata, explains the multi-dimensional nature of gaining visibility into your running data platform and how they have architected their platform to assist in that endeavor.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I’m interviewing Tristan Spaulding about Acceldata, a platform offering multidimensional data observability for modern data infrastructure
Interview
Introduction
How did you get involved in the area of data?
Can you describe what Acceldata is and the story behind it?
What does it mean for a data observability platform to be "multidimensional"?
How do the architectural characteristics of the "modern data stack" influence the requirements and implementation of data observability strategies?
The data observability ecosystem has seen a lot of activity over the past ~2-3 years. What are the unique capabilities/use cases that Acceldata supports?
Who are your target users and how does that focus influence the way that you have approached feature and design priorities?
What are some of the ways that you are using the Acceldata platform to run Acceldata?
Can you describe how the Acceldata platform is implemented?
How have the design and goals of the system changed or evolved since you started working on it?
How are you managing the definition, collection, and correlation of events across stages of the data lifecycle?
What are some of the ways that performance data can feed back into the debugging and maintenance of an organization’s data ecosystem?
What are the challenges that data platform owners face when trying to interpret the metrics and events that are available in a system like Acceldata?
What are the most interesting, innovative, or unexpected ways that you have seen Acceldata used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Acceldata?
When is Acceldata the wrong choice?
What do you have planned for the future of Acceldata?
Contact Info
LinkedIn
Parting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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Links
Acceldata
Semantic Web
Hortonworks
dbt
Podcast Episode
Firebolt
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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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