talk-data.com

👉 Register here 👈

The future of data architecture is already here: distributed, cloud-native, and AI-ready. Is your organization equipped to meet the moment?

Join leading data experts Sanjeev Mohan, Dave Burgess, and Mark Donsky for a live panel discussion on why distributed SQL has become a critical building block for modern data platforms—and what that means for companies struggling with the limits of traditional databases, sharding complexity, and patchwork scalability.

The panel will unpack the macro trends accelerating adoption of distributed SQL, including the surge in data-intensive applications, demand for real-time analytics, elastic scalability, cost pressure, and the rise of AI workloads.

You’ll hear firsthand how enterprises and SaaS innovators are overcoming the limitations of legacy databases, simplifying operations, and unlocking new AI-driven capabilities with distributed SQL solutions like TiDB.

You’ll gain practical perspectives on:

• The evolution of distributed databases: Why is now the tipping point for distributed SQL?
• Business drivers: Scale, performance, cost efficiency, compliance, and AI-readiness.
• Architectural innovations: How technologies like TiDB solve challenges of multi-region deployments, always-on availability, and mixed workload processing.
• Real-world use cases: From e-commerce scalability to powering retrieval-augmented generation (RAG), agentic memory, and semantic search for AI applications.
• Future trends: The role of distributed SQL in shaping cloud-native and AI-powered data strategies.

Whether you’re a CTO, architect, or data engineering leader, this discussion will equip you with the knowledge to assess how distributed SQL fits into your organization’s modernization journey. Register now to secure your spot!

👉 Register here 👈

In this episode, I’m chatting with former Gartner analyst Sanjeev Mohan who is the Co-Author of Data Products for Dummies. Throughout our conversation, Sanjeev shares his expertise on the evolution of data products, and what he’s seen as a result of implementing practices that prioritize solving for use cases and business value. Sanjeev also shares a new approach of structuring organizations to best implement ownership and accountability of data product outcomes. Sanjeev and I also explore the common challenges of product adoption and who is responsible for user experience. I purposefully had Sanjeev on the show because I think we have pretty different perspectives from which we see the data product space.

Highlights/ Skip to:

I introduce Sanjeev Mohan, co-author of Data Products for Dummies (00:39) Sanjeev expands more on the concept of writing a “for Dummies” book   (00:53) Sanjeev shares his definition of a data product, including both a technical and a business definition (01:59) Why Sanjeev believes organizational changes and accountability are the keys to preventing the acceleration of shipping data products with little to no tangible value (05:45) How Sanjeev recommends getting buy-in for data product ownership from other departments in an organization (11:05) Sanjeev and I explore adoption challenges and the topic of user experience (13:23) Sanjeev explains what role is responsible for user experience and design (19:03) Who should be responsible for defining the metrics that determine business value (28:58) Sanjeev shares some case studies of companies who have adopted this approach to data products and their outcomes (30:29) Where companies are finding data product managers currently (34:19) Sanjeev expands on his perspective regarding the importance of prioritizing business value and use cases (40:52) Where listeners can get Data Products for Dummies, and learn more about Sanjeev’s work (44:33)

Quotes from Today’s Episode “You may slap a label of data product on existing artifact; it does not make it a data product because there’s no sense of accountability. In a data product, because they are following product management best practices, there must be a data product owner or a data product manager. There’s a single person [responsible for the result]. — Sanjeev Mohan (09:31)

“I haven’t even mentioned the word data mesh because data mesh and data products, they don’t always have to go hand-in-hand. I can build data products, but I don’t need to go into the—do all of data mesh principles.” – Sanjeev Mohan (26:45)

“We need to have the right organization, we need to have a set of processes, and then we need a simplified technology which is standardized across different teams. So, this way, we have the benefit of reusing the same technology. Maybe it is Snowflake for storage, DBT for modeling, and so on. And the idea is that different teams should have the ability to bring their own analytical engine.” – Sanjeev Mohan (27:58)

“Generative AI, right now as we are recording, is still in a prototyping phase. Maybe in 2024, it’ll go heavy-duty production. We are not in prototyping phase for data products for a lot of companies. They’ve already been experimenting for a year or two, and now they’re actually using them in production. So, we’ve crossed that tipping point for data products.” – Sanjeev Mohan (33:15)

“Low adoption is a problem that’s not just limited to data products. How long have we had data catalogs, but they have low adoption. So, it’s a common problem.” – Sanjeev Mohan (39:10)

“That emphasis on technology first is a wrong approach. I tell people that I’m sorry to burst your bubble, but there are no technology projects, there are only business projects. Technology is an enabler. You don’t do technology for the sake of technology; you have to serve a business cause, so let’s start with that and keep that front and center.” – Sanjeev Mohan (43:03)

Links Data Products for Dummies: https://www.dataops.live/dataproductsfordummies “What Exactly is A Data Product” article: https://medium.com/data-mesh-learning/what-exactly-is-a-data-product-7f6935a17912 It Depends: https://www.youtube.com/@SanjeevMohan Chief Data Analytics and Product Officer of Equifax: https://www.youtube.com/watch?v=kFY7WGc-jFM SanjMo Consulting: https://www.sanjmo.com/ dataops.live: https://dataops.live dataops.live/dataproductsfordummies: https://dataops.live/dataproductsfordummies LinkedIn: https://www.linkedin.com/in/sanjmo/ Medium articles: https://sanjmo.medium.com

Join us after the MongoDB.local NYC happy hour for the Generative AI Unconference. You will mingle with entrepreneurs, practitioners and experts for an evening of learning, sharing and networking as we dive into our theme of Generative AI for Data.

> Use code UNCONFERENCE100 to secure your complimentary registration to MongoDB.local NYC and the Generative AI Unconference

Hosted by Sanjeev Mohan and Dave Nielsen, this event promises to be an exciting opportunity to delve into the world of generative AI for data as we explore the exciting benefits as well as the very real pitfalls and concerns of this innovation.

This event is designed to foster interactive conversations and meaningful connections among participants who are passionate about exploring and leveraging AI.

What to expect Throughout the event, we'll have three rounds of breakout sessions. These sessions offer an opportunity for in-depth discussions, knowledge sharing, and exploration of various topics related to generative AI for data. We'll wrap up the Unconference with a sense of inspiration and new insights gained.

Learn something new Don't miss this exceptional opportunity to expand your understanding of generative AI for data and connect with professionals in the field.

Secure your spot Attendees must register for MongoDB.local NYC to secure a place at Unconference. Use code UNCONFERENCE100 * to save your seat at Unconference and secure your complimentary registration to MongoDB.local NYC. * Unconference100 code only applicable to new registrations.

What is an Unconference? Well, instead of powerpoint presentations, each session is a exploratory discussion about different aspects of Generative AI. Some topics we expect to discuss are:

• Popular uses
• Impacts on work today
• Who’s who in generative AI
• Open source models
• Large language models vs small language models
• Vector databases
• How to update your data strategy & ethical and social implications
• Plus so much more!

These sessions offer an opportunity for in-depth discussions, knowledge sharing, and exploration of various topics related to generative AI for data. Don't miss this exceptional opportunity to expand your understanding and connect with professionals in the field!

Agenda

4:50 pm Reception 6:00 pm Welcome & Begin Unconference 7:00 pm Breakout Sessions - Round 1 7:40 pm Breakout Sessions - Round 2 8:20 pm Breakout Sessions - Round 3 9:00 pm Wrap-up

The advent of big data, self-service analytics, and cloud applications has created a need for new ways to manage data access. New data access governance tools promise to simplify and standardize data access and authorization across an enterprise. Data management expert, Sanjeev Mohan, provides an industry perspective on this emerging technology and what it means for data analytics teams.