The Ravit Show

We are surrounded by the biggest Data and AI leaders at Gartner. Yet one question keeps coming up. Why are so many AI initiatives still failing? I asked Rich Hoyland, President, Global Field Operations, Anomalo to answer that directly on The Ravit Show at Gartner Orlando.His take was simple.AI does not fail because of ambition.It fails because of bad data.We spoke about:-- Why agentic AI raises the stakes for data quality-- Why speed without trust is a risk for executives-- What data quality failure actually looks like inside large enterprises-- Why traditional rule-based approaches are not enough anymore-- And where Anomalo sees the future of data management goingOne part of the conversation stood out. When AI moves from insight to action, data quality stops being a reporting issue. It becomes a business risk issue.Old approaches were built for dashboards. Now we are feeding data into agents that make decisions. That changes everything. Rich also shared how their long term vision goes beyond just catching bad data to providing enterprises with an agentic “self-driving data” system. It is about building continuous trust in data across the enterprise from ingestion to decision so AI agents can operate and scale safely.If you care about AI that actually works in production, this is one to watch.#data #ai #anomalo #dataquality #theravitshow

At Gartner D&A Day 1, I sat down with Antonio, CEO and Co-Founder of BEM, to talk about a problem many enterprises quietly struggle with.Messy data.While everyone is excited about agents, Antonio made one thing clear.Agents do not work well with unstructured, inconsistent inputs. That is where hallucinations and failures begin.BEM focuses on turning messy inputs, from PDFs and contracts to voice and video, into clean, structured outputs that enterprises can actually trust.We discussed:* Why so many AI pilots fail before reaching production* How BEM acts as the foundation layer before agents* Why regulated industries like healthcare and finance need production-grade accuracy* How some teams deploy in minutes by starting with one painful workflowThe message was simple.If you want agents to work, fix the data first.#data #ai #bem #gartnerda #theravitshow

“Your data is fine. Your AI isn’t good enough.” That is the bold statement behind TextQL, and it immediately caught my attention here at Gartner. I sat down with Ethan Ding, Co-Founder, CEO & Head of Product, TextQL, to unpack what he means by that and why they are challenging many assumptions around BI and analytics.Most enterprises have spent years building ETL pipelines, cleaning data, and preparing dashboards. The belief has been that AI will only work once data is perfectly structured.Ethan disagrees.He believes the real limitation has been the AI systems themselves.We talked about:-- What enterprises are misunderstanding today about AI and data quality-- Why traditional BI tools like Tableau or Power BI were built for a different era-- How TextQL enables AI analytics even when data is messy or not fully ETL’d-- Why they believe seat-based pricing for dashboards is broken-- How their approach focuses on trust and verification so enterprises can validate AI-generated answersOne idea stood out during the conversation.Executives do not just want answers.They want conviction that the answer is correct.That is where their “Query to Conviction” concept comes in. AI does not just generate an answer. It shows the reasoning, the data path, and the verification behind it.For CIOs walking the Gartner floor, Ethan had a simple suggestion. Do not ask vendors how good their AI looks. Ask them how their AI proves it is right.#data #ai #textql #gartnerda #theravitshow

Most AI analytics platforms assume one thing. Your data lives in one place. Kapil Chhabra, Co-Founder and CPO at WisdomAI on The Ravit Show challenged that assumption immediately. Enterprises are distributed. Their data is fragmented across clouds, warehouses, operational systems, and business units. Forcing everything into a single layer before AI can work is slow and expensive.That is why WisdomAI introduced Federated Agentic Intelligence.Instead of centralizing first and analyzing later, the system works across distributed sources. It assembles context at runtime. We spent time on what they call the Enterprise Context Layer. Without context, AI gives generic answers. With context, AI understands how metrics connect, what definitions mean, and how governance rules apply.Kapil was clear that federation is not a feature. It is a design principle for the modern enterprise.We also talked about what is next on their roadmap and what capabilities they are most excited about over the coming year. The focus is less on flashy features and more on depth, reliability, and scale.If your organization is struggling to move from AI experiments to AI that executives actually trust, this conversation goes deep into architecture decisions that matter.#data #ai #gartnerda #wisdomai #theravitshow

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