The Ravit Show

Your AI has a context problem. It’s not the model. It’s context!!!! That was the mic drop from Prukalpa, Co-Founder & Co-CEO, Atlan on The Ravit Show when we kicked off this conversation. And honestly, it set the tone for everything that followed.We spoke about why so many AI projects stall. Not because the model is weak. Not because the team is not smart. But because the data lacks shared meaning. No common definitions. No clear ownership. No business context.Here is what we unpacked:• What a “context layer” actually means in simple terms• Why this idea is suddenly everywhere at Gartner this year• Where the context layer fits in the modern data stack• What Atlan is building to make context usable, not theoretical• The one demo every data leader should see before leaving OrlandoOne big takeaway:If your AI does not understand your business context, it will confidently give you the wrong answer.If you are at Gartner this week, stop by Booth 313. See how context is being turned into something real, usable, and operational.#data #ai #gartnerda #atlan #theravitshow

"On-prem is the new cloud.” That statement is not just a headline. It reflects what many enterprises are quietly experiencing. I sat down with David Dichmann, VP Product Marketing and Evangelism, Cloudera, to unpack what is really driving this shift and how Cloudera’s Cloud Anywhere vision fits into the bigger picture.Here is what we discussed:-- Why rising cloud costs, data gravity, and regulatory pressure are pushing companies to rethink all-in cloud strategies-- What Cloud Anywhere actually means beyond marketing-- How enterprises can run advanced AI use cases without forcing massive data movement into one environment-- Why security, governance, and Private AI are central to this resurgence of on-prem-- The biggest roadblocks teams face when deploying AI across hybrid and multi-cloud environments-- How portability and consistency reduce friction for data and AI teams-- How Cloudera continues to lean into its open-source roots while evolving its platformOne theme stood out. AI does not require you to centralize everything into one cloud. It requires control, flexibility, and a consistent experience wherever your data lives. For enterprises balancing cost, compliance, and AI ambition, this conversation goes beyond trends. It is about architecture decisions that will shape the next few years.#data #ai #cloudera #gartnerda #theravitshow"On-prem is the new cloud.” That statement is not just a headline. It reflects what many enterprises are quietly experiencing. I sat down with David Dichmann, VP Product Marketing and Evangelism, Cloudera, to unpack what is really driving this shift and how Cloudera’s Cloud Anywhere vision fits into the bigger picture.Here is what we discussed:-- Why rising cloud costs, data gravity, and regulatory pressure are pushing companies to rethink all-in cloud strategies-- What Cloud Anywhere actually means beyond marketing-- How enterprises can run advanced AI use cases without forcing massive data movement into one environment-- Why security, governance, and Private AI are central to this resurgence of on-prem-- The biggest roadblocks teams face when deploying AI across hybrid and multi-cloud environments-- How portability and consistency reduce friction for data and AI teams-- How Cloudera continues to lean into its open-source roots while evolving its platformOne theme stood out. AI does not require you to centralize everything into one cloud. It requires control, flexibility, and a consistent experience wherever your data lives. For enterprises balancing cost, compliance, and AI ambition, this conversation goes beyond trends. It is about architecture decisions that will shape the next few years.#data #ai #cloudera #gartnerda #theravitshow

The mainframe is not going anywhere. It is evolving. I just wrapped up a powerful conversation with Matt Whitbourne, VP of Product Management & Design for the BMC Software AMI portfolio.We spoke about something many enterprises are quietly navigating right now:How do you modernize the mainframe without breaking what already works?Here’s what stood out to me from our discussion:* Modernization is no longer optional. It is expected.* Cyber resilience is now a board-level conversation.* Data accessibility on the mainframe is becoming critical for AI and enterprise-wide analytics.* Automation is helping teams move faster without compromising stability.Matt also shared how AMI Cloud is evolving, especially around resilience, operational efficiency, and AI-driven capabilities.What I appreciated most was this balance: Enterprises want innovation.But they also want reliability.The mainframe still runs some of the most critical systems in the world. The challenge is not replacing it. The challenge is transforming it. If you work in enterprise IT, data, or platform engineering, this conversation is worth your time.

Observability is no longer just about dashboards. It is about systems that can act. I caught up with Brian Emerson, Chief Product Officer at New Relic, at Advance 2026, and one thing was clear. Brian shared a bold view of where software is headed. More applications will be built in the next five years than in the last fifty. That creates a scale problem no team can solve manually. His answer is intelligence and automation, led by what New Relic calls the S agent. What stood out to me is how this shifts incident response. Instead of pulling people into late night war rooms, the idea is a digital war room where agents constantly watch behavior, surface issues, and even help resolve them. For customers, the starting point is simple. Turn it on and let it begin optimizing alerts and recommendations.We also talked about trust, which is the real conversation right now. Application health is no longer just uptime. It is behavior, outcomes, and user experience. AI systems can fail quietly, with wrong answers or poor decisions. That is why New Relic is pushing toward agentic monitoring, so teams can see not just the infrastructure but the actual experience their users are getting.One insight Brian shared surprised me. New Relic chose to lean into fast-improving general models instead of building niche ones, because the pace of change makes specialization short lived. That says a lot about how quickly this space is moving.His message for leaders was simple. The shift is from recommendations to action. The real opportunity now is letting agents handle the last mile of operations and moving closer to self-healing systems.#Data #AI #NewRelic #Observability #AI #SRE #AgenticAI #TheRavitShow

At New Relic Advance 2026 in San Francisco, one theme was impossible to miss. Observability is no longer just about seeing problems. It’s about fixing them. I caught up with Ashan Willy, CEO of New Relic, to talk about what this shift really means for customers.The biggest highlight was the launch of their SRE Agent and the broader move toward an agentic platform. What stood out in our conversation was not just the technology, but the reason behind it.Customers are overwhelmed by signals, alerts, and dashboards. They don’t need more data. They need faster action.That is exactly what New Relic is aiming to solve. Closing the gap between insight and execution so engineers spend less time diagnosing and more time delivering.Compared to last year, this feels like a real strategic inflection point. The conversation is no longer about adding AI features. It is about redesigning workflows around AI-native operations.The big question now is not whether agents will assist engineers. It is how much responsibility they will take on as trust grows. If this launch works, by next year we should see fewer alerts, faster resolutions, and teams spending more time building instead of firefighting.#Data #AI #NewRelic #Observability #AI #SRE #AgenticAI #TheRavitShow

Everyone is talking about AI agents. But very few conversations are grounded in real data.Databricks just released their new State of AI Agents report, and it gives a clear picture of how enterprises are actually using AI today, what is working, and where things are headed next.I sat down with Kunal Marwah, Mason Force, and Chengyin Eng from Databricks to break down what stood out to them from the report and what they are seeing directly with customers.We talked about why companies are moving from to multi-agent systems, how teams are choosing their first real business use cases, and how agents are driving the need for a new type of database called Lakebase.We also discussed what separates teams that get AI into production from those stuck in endless pilots. Governance, evaluation, and clear alignment to business outcomes came up again and again.If you are leading data, AI, or product initiatives, this conversation gives a practical look at what enterprise adoption actually looks like today and what leaders should focus on next.I have shared the link to the full report in the comments if you want to dig into the data yourself.Learn more about ---- Databricks Mosaic Research: https://www.databricks.com/blog/category/ai/mosaic-research-- Databricks Industry Solutions: https://www.databricks.com/solutions/accelerators#data #ai #report #agents #chatbots #api #business #databricks #theravitshow

What if AI is not just powered by master data… but actually improves it? At the DataDriven Conference, Ravit Jain sat down with Anjan Roy from Deloitte, who leads the global life sciences MDM practice, to talk about the real relationship between AI and Master Data Management.One line from our conversation stayed with me: AI supports MDM and MDM supports AI.Most companies talk about preparing data for AI. Deloitte is also focused on the reverse. Using AI to make MDM programs faster, smarter, and more scalable so organizations can realize value sooner.In life sciences, this is not theoretical. It currently takes 10 to 12 years to bring a drug to market. If better data and AI can reduce that timeline by even 10 to 20 percent, patients get access to treatment sooner. That impact is real.They also discussed how agentic AI is starting to change the pace of execution. Life sciences is one of the most regulated industries in the world, yet we saw vaccines come to market in nine months during COVID. The acceleration is possible when data, governance, and AI come together with the right intent.Looking ahead six to eight months, Anjan expects AI to become more operational, data to be more accessible, and organizations to move from gut-driven decisions to data-driven action.This is powerful conversation about speed, scale, and impact in one of the most critical industries.#data #ai #datadriven #reltio #theravitshow

LLMs can accelerate everything. But without context, they accelerate you in the wrong direction. At DataDriven Conference 2026 by Reltio, Ravit Jain had a powerful conversation with Satish Mallavolu, CEO of Blumetra Solutions, about the end-to-end data life cycle and what it really means to monetize data.One message came through clearly. Master data is not optional. It is the glue that holds the enterprise together.Satish described LLM acceleration like oil in an engine. It helps you move faster. But without strong context, and without master data as a core ingredient of that context, that speed will land you in a ditch.They spoke about why enterprise leaders are rethinking their foundations. If the data layer is weak, every AI use case built on top of it will struggle. Garbage in, garbage out is still true, even in the age of generative AI.Looking ahead, Satish believes every enterprise will need a serious metadata practice. He even advocates for a Chief Metadata Officer role. Without ownership of metadata and context, AI initiatives will continue to produce inconsistent results.They also discussed real use cases, from campaign activation and churn prevention to smarter upsell and cross-sell strategies, and better data retention.This was a grounded conversation about turning data into business value, not just dashboards.#data #ai #gartnerda #wisdomai #theravitshow

MDM is back. And this time, it is tied directly to AI outcomes. At DataDriven Conference 2026 by Reltio, I had a great conversation with Avinash (Avi) Deshpande, Field CTO at Workato, about how integration, MDM, and agentic workflows are coming together.One thing was clear. MDM is no longer a back-office data discipline. It is becoming the context engine for AI.Workato plays a critical role in this ecosystem. As a strategic partner with Reltio, they act as the integration hub powering the data layer. That means enabling inbound data, enriching it with external sources like Dun & Bradstreet, and activating it across the business for real consumption.But the bigger shift we discussed was agentic flow.This is where traditional MDM is being reimagined. Instead of static processes, agents operate within workflows to improve automation, user experience, and speed. The industry is moving past experimentation and toward business enablement. By mid-year, Avinash expects most enterprises to be done testing and focused on measurable impact.And that is what leaders care about. Real ROI.The combination of integration, trusted master data, and agentic capabilities is no longer theoretical. It is accelerating outcomes across enterprises.#data #ai #datadriven #reltio #theravitshow

Reltio crossed $185M in ARR and saw 58% growth in the second half of the year. That kind of acceleration in an enterprise platform signals strong market pull. At the DataDriven Conference 2026, I sat down with Alyson Welch, CRO of Reltio, to talk about what customers are actually asking for in the age of AI.Alyson owns the full customer journey, from acquisition to support, and also leads the partner ecosystem. Her lens is simple. If the customer does not see value in their data, nothing else matters.One insight stood out. Enterprises are drowning in applications. One former CIO shared she had 1,500 enterprise apps to manage. That means data is locked everywhere. The real demand today is not just dashboards or models. It is a trusted, unified data layer that sits between enterprise systems and AI.That is where Reltio is focused. Bringing accuracy, trust, and unification so companies can actually use their data across environments.We also discussed agentic workflows. In an agent-driven world, your data foundation cannot be average. Every automated decision depends on it. This is not just a tech shift. It is a cultural shift. Leaders now have to think about how humans and AI agents work together and how to build capability across both.The momentum reflects this shift. This conversation was about trust, scale, and what it really takes to lead in the AI era.#data #ai #datadriven #reltio #theravitshow