The Ravit Show

Small Data. Real outcomes. I covered MotherDuck’s Small Data SF in person and spoke to my long-time connection with Colin Zima, CEO of Omni, to cut through the AI noise in BI. We focused on what actually moves the needle for teams today.Here is what we got into:• Where AI is creating real BI valuePractical wins that ship now, not hype cycles• Flexibility vs governanceHow Omni gives analysts room to explore while keeping the shared truth intact• Why build a modeling layerWhat Omni’s own model unlocks for speed, trust, and how far AI can go• Embedded analytics after the Explo acquisitionWhen it makes sense to put live insights inside your product and what to avoid• Simple over cleverWays AI can remove clicks, clean up metrics, and make BI easier to use• Common mistake with AI in dashboardsTeams bolt on features before they fix definitions and owners• The agent futureIf agents run dashboards tomorrow, why export to Excel might still matterIf you care about getting answers faster with clear guardrails, you will like this one.#data #ai #motherduck #smalldatasf #theravitshow

Small Data is having a big moment!!!! I covered Small Data SF by MotherDuck in person and sat down with Brittany Bafandeh, CEO at Data Culture. We talked about the real blockers to impact and how teams can move faster with the data they already have.Here is what we got into:When it is not a data problem - Brittany walked through a case where dashboards, pipelines, and new tools were not the fix. The real issue was slow decisions and unclear ownership. Once they set decision rights and clear KPIs, outcomes changed without buying more tech.Do you have a data culture or just tools - As a consultant, she looks for simple signals. Are decisions tied to metrics. Do teams review outcomes every week. Are definitions shared. If the answer is no, that is an infrastructure shell without culture inside it.Consultant vs in house - Consultants can push for focus and bring patterns from many teams. In house leaders win by staying close to the business and building habits that last. The best results happen when both mindsets meet.One modeling habit that breaks things - Teams jump to complex models too soon. Brittany’s fix is to model around decisions first. Keep names, metrics, and grain simple. Let complexity come only when the use case proves it.Why this mattersMost teams do not need more tools to get value. They need faster decisions, shared language, and simple models that match the business. Small data, used well, beats big stacks used poorly!!!!I am publishing the full interview next. If you care about real outcomes with the stack you already have, you will like this one.#data #ai #motherduck #smalldatasf #theravitshow

Most companies say they are “doing AI.” Very few are actually ready for it. In my new episode of The Ravit Show, I sat down with Simon Miceli, Managing Director, Cisco, who leads Cloud and AI Infrastructure across Asia Pacific, Japan, and Greater China. He sits right where big AI ambitions meet the hard reality of networks, security, and technical debt.This conversation builds on my earlier episode with Jeetu Patel, President and CPO Cisco and goes deeper into what it really takes to get AI working in production in APJC.Here are a few themes we unpacked:-- Only a small group is truly AI ready- Cisco’s latest AI Readiness Index shows that just a small percentage of organizations globally are able to turn AI into real business value. Cisco calls them “Pacesetters.”- They are not just running pilots. They have use cases in production and are seeing returns.-- We are entering the agentic phase of AI- Simon talked about how we are moving from simple chatbots to AI agents that can take action.- That shift changes everything for infrastructure.- Instead of short bursts of activity, you now have systems that are always working in the background, automating processes and touching real operations.-- AI infrastructure debt is the new technical debt*- Many organizations are carrying years of compromises in their networks, data centers, and security.- Simon called this “AI infrastructure debt” and described how it quietly slows down innovation, increases costs, and makes it harder to scale AI safely.-- Network as a foundation, not an afterthought- One of his strongest points: leaders often think first about compute, but the companies that are ahead treat the network as the base layer for AI.- When workloads double, your network can become the bottleneck before your GPUs do. - The Pacesetters are already investing to make their networks “AI ready” and integrating AI deeply with both network and cloud.Three things leaders must fix in the next 2–3 yearsSimon shared a very clear checklist for CIOs and business leaders who are serious about agentic AI: 1. Solve for power before it becomes a constraint 2. Treat deployment as day one and keep optimizing models after they go live 3. Build security into the infrastructure from the start so it accelerates innovation instead of blocking itThis was a very honest, no-nonsense view of where APJC really stands on AI, and what the leading organizations are doing differently!!!!Thank you Simon for joining me and sharing how Cisco is thinking about AI infrastructure across the region.#data #ai #cisco #CiscoLiveAPJC #Sponsored #CiscoPartner #TheRavitShow

These are some of the most exciting times to be in AI. And some leaders are not just watching the shift. They are building it. Excited to share, I sat down with Jeetu Patel, President and Chief Product Officer at Cisco, for a conversation I have been wanting to do for a long time. Cisco is right in the middle of AI, networking, security, and data, and this interview felt like a front row seat to how the next decade is being shaped.In this episode of The Ravit Show, we spoke about:- The key AI trends Jeetu is seeing right now and how he explains Cisco’s AI vision- What being at the intersection of networking, security, and data allows Cisco to do with AI that most pure AI companies cannot- How AI adoption in Asia Pacific, Japan, Greater China, and India looks different from North America and Europe- Why India is so important to Cisco, both as a market and as a serious talent hub- The early career moments that still shape how he leads today- The one piece of career advice he wishes someone had given him at 25, for everyone starting out in India and across APJCFor me, this was part AI roadmap, part masterclass in leadership at global scale. You can feel how seriously he takes this moment and the responsibility that comes with it.If you care about AI, infrastructure, or building your career in this space, this is one you will want to watch.#data #ai #cisco #CiscoLiveAPJC #Sponsored #CiscoPartner #TheRavitShow

Gartner has named K2view a Visionary in the 2025 Magic Quadrant for Data Integration Tools, and they have moved up inside the Visionary quadrant. This is a big signal for anyone who cares about data and AI in the enterprise.I had to cover this news in person and what better place than my friend, Ronen Schwartz’s home in Palo Alto, talking to him about what this actually means. We did not just speak about a report. We spoke about whether data integration still matters in an AI world and why K2View’s approach is getting attention right now.Here is how I see it.- First, data integration is more relevant than ever. Your AI agents, copilots, and analytics are only as good as the data foundation behind them. K2View’s bet has been simple to understand. Give every business domain a clean, real time, governed view of its data, and make it available to any use case, including AI.- Second, the move up in the Visionary quadrant is about more than a label. It reflects how K2View is executing on this idea of “AI ready data,” not just talking about it. They are helping customers move away from scattered pipelines to a consistent way of delivering trustworthy data products into AI, analytics, and operations.- Third, when you compare their position with the large leaders, you see a different angle. The big platforms are broad. K2View is sharp and focused.They model data around real business entities, not just tablesThey support real time views without forcing you into one storage patternThey are designing with GenAI and agentic AI in mind from day oneFinally, the strategic outlook. Ronen is very clear that this is not about selling “yet another integration tool.” It is about being the data layer that lets enterprises move faster with AI while staying in control of privacy, compliance, and performance.For leaders who are serious about AI and tired of slideware, K2View’s move in the Magic Quadrant is one of those signals worth paying attention to.#data #ai #gartner #gartnermagicquadrant #agenticai #agents #k2view #theravitshow