The Ravit Show

We have been building dashboards for 20 years. Now everyone is adding AI on top of them. But what if the real issue is the stack itself? That is where my conversation with Soham Mazumdar, Co-Founder and CEO, WisdomAI went at Gartner D&A on The Ravit Show!!!!WisdomAI calls what they are building “Agentic Analytics.” Not a chatbot on top of BI. Not a copilot that still depends on humans to interpret everything. We talked about what is fundamentally broken in today’s analytics world:- Dashboards answer questions you already thought of- Executives need answers to questions they did not know to askSoham shared how enterprises are moving from static reporting to agents that reason across metrics, detect issues, and explain why something happened. The trust problem came up quickly. Most AI analytics tools look impressive in a demo. Very few hold up under real enterprise scrutiny.We also discussed a real customer story with Cisco and what changed after deploying WisdomAI. The shift was not just faster answers. It was decision confidence.Looking ahead, Soham believes analytics teams will not disappear. They will evolve into designers and supervisors of intelligent systems that operate continuously across the business.For enterprise leaders rethinking the future of BI, this was a forward-looking and very practical discussion.#data #ai #gartnerda #wisdomai #theravitshow

#IBMPartner Governance, responsible AI implementation and delivering measurable value —these are top of mind for Jordan Byrd, AI/ML Ops Product Marketing Lead at IBM.AI adoption feels different this year—faster with more framework. Watch our conversation from Gartner D&A where we caught up to discuss what’s really changing inside enterprises and what that means for the next phase of AI.If you are building AI at scale inside an enterprise, this one will resonate.

Everyone says AI is the priority. Yet many projects are quietly failing. At Gartner D&A, I asked Christopher Moore, Global Sr. Director, AI & Platform at Alteryx, to be direct about why.His answer was not about models. It was about execution. Too many AI initiatives are disconnected from real business workflows. They look good in a lab. They struggle in operations. We then got into a bigger tension inside enterprises. Business teams understand the problem best. But they rarely build the AI solutions themselves.Why? Because the tooling has been too technical. Too fragmented. Too dependent on centralized teams. Christopher explained how Alteryx is trying to close that gap. Not by lowering standards, but by enabling governed, production-grade AI where business users already work. We also talked about what MCP server and Agentspace unlock for long time Alteryx users. In simple terms, it is about moving from isolated workflows to orchestrated AI systems. From analytics automation to agent-enabled automation.And then we addressed the elephant in the room. Alteryx was once labeled shadow IT. That perception has shifted. In a world where AI governance is critical, the focus is now on controlled enablement. Visibility. Auditability. Guardrails built in.The message was clear. Empowering business users does not mean losing governance. It means designing platforms that balance speed with control. If you are navigating the tension between innovation and oversight, this is a conversation you will want to watch.#data #ai #gartnerda #alteryx #theravitshow

Synthetic data is everywhere in AI conversations!!!! But what does it actually solve? I had an amazing conversation with Michael Eckhoff on The Ravit Show at Gartner he brought this down to reality. We spoke about when synthetic data makes more sense than masking or subsetting production data.It shines when:• Compliance makes moving production data into lower environments a bottleneck• Teams need data that simply does not exist• Rare edge cases are missing from real datasetsSynthetic data lets teams generate fit-for-purpose datasets on demand without copying real customer records across environments.We also tackled the big concern. Is synthetic realistic enough?Realistic does not mean copied. It means the relationships hold. The distributions look right. The system behaves the same way.And you prove it.You compare statistical properties.You validate patterns.You ensure no record is traceable to a real individual.Finally, where does synthetic fit in AI and GenAI?It removes the compliance friction.It helps balance datasets.It enables experimentation without exposing sensitive information.For AI teams trying to move fast and stay compliant, this is a serious lever.#data #ai #gartner #k2view #theravitshow

I had a blast at Gartner last week, here's my discussion with Hod Rotem from K2view on The Ravit Show, diving into one of the most important topics right now. What does AI-ready data architecture actually look like when it is running in production? We will break down:* How real-time, entity-level data gets assembled across dozens of systems* What it takes to support thousands of AI agents working in parallel* Why architecture, not just models, determines whether AI actually worksIf you are thinking about agentic AI beyond demos, this will be a practical and direct conversation.#data #ai #gartnerda #K2View #theravitshow

Mainframes. Synthetic data. AI-ready foundations. This was one of the most practical conversations we had at Gartner on The Ravit Show. I sat down with Ronen Schwartz, CEO at K2view and Michael Curry, President, Data Modernization, Rocket Software to talk about their partnership and why it matters right now.Here is the reality.A lot of enterprise data still lives in mainframes and core systems of record. At the same time, teams are racing to automate development, generate code with AI, and move faster than ever.That creates a real gap.We discussed:-- Why customers building data products, test data management, and synthetic data pushed K2View and Rocket Software to collaborate-- How modernization of legacy systems creates opportunities to generate and manage test data at scale-- Why synthetic data is critical when you cannot simply move production data into lower environments-- How teams can now generate code from a product story and also generate the data needed to test it-- Why governance is the layer leaders must get right before scaling AIOne point stood out.The technology leap toward AI is not the hardest part. Getting the data foundation, quality, and governance right is. If AI agents are going to act on enterprise data, that data must be trusted, protected, and consistent across systems of record.Their advice to leaders was simple.- Build AI-ready data environments.- Partner with vendors who are deep in what they do.- Carry your governance investments forward into your agentic AI strategy.If you are modernizing mainframes, thinking about synthetic data, or preparing your enterprise for AI in production, this one is worth watching.#data #ai #rocketsoftware #gartnerda #k2view #api #mainframes #enterprise #theravitshow

Five years advising CDAOs at Gartner D&A. Now in the field helping enterprises actually implement AI and governance. That shift gives Austin Kronz, Head of AI & Data Strategy, Atlan, a rare lens. And this conversation was honest. We talked about the gap between what we say on stage at big events and what really happens inside companies once everyone flies home.Here is what we unpacked:• What surprised him most moving from analyst to operator• The real signals coming out of this summit around AI governance, metadata, and context for AI agents• The controlled experiments around context layers at companies like Workday and Fox, and what actually drove up to 5x improvement in AI accuracy• Where Fortune 500 teams get stuck when they say “we need AI governance”• The patterns he sees in companies like Cargill and PPG that succeed with context at scaleOne theme kept coming up.The winners are not the ones talking the most about AI. They are the ones operationalizing context, ownership, and governance in very practical ways.Whether you attended Gartner D&A or not, this one is worth watching.#data #ai #gartnerda #atlan #theravitshow

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.