Last week at Microsoft Ignite, I finally got to interview the man himself, Arun Ulag, President, Azure Data, Microsoft. I have known Arun for years. He is one of the best in the data and analytics space, and meeting him in person was so good. I have a new friend!!!

This was not a surface level chat. We went straight into the big announcements and what they unlock for customers:

• The real takeaways from his keynote and how they help enterprises move faster with data and AI

• What Fabric IQ actually is, how it works behind the scenes, and why it is a big deal

• How customers can start seeing value from Fabric IQ from day one, not after months of setup

• What makes Azure HorizonDB for PostgreSQL different and where it fits for modern workloads

• What is coming next for data services in the era of AI

• How leaders should think about the next 4 to 5 months with all these updates landing at once

If you are trying to understand where the Microsoft data and AI stack is really heading, this conversation will give you a very clear picture!!!!

#data #ai #ignite #microsoft Microsoft Azure Microsoft Azure Microsoft for Startups #microsoft2025 #copilot #agents #theravitshow

Agentic AI does not matter if it stays in slides. What matters is whether it can run safely in production. After AWS re:Invent, I sat down with Hemant Mohan from Amazon Web Services (AWS) for a follow up conversation focused on agentic AI and the AWS and IBM partnership. This discussion was less about announcements and more about what actually changes for enterprises now.Here is the core of what stood out.- AI is moving from response to actionWe are past the phase of just experimenting with models. The next phase is agentic AI systems that can reason, plan, and execute across tools and workflows. Business problems are never single step. They involve legacy systems, approvals, fragmented data, and humans in the loop. Agents are built to work across that reality.- Why the AWS and IBM partnership mattersMany enterprises struggle with the same question. How do we move agents from proof of concept to production without breaking security, compliance, or reliability? This partnership is about solving that gap. AWS brings the trusted, global infrastructure to run agents at scale. IBM brings orchestration, governance, and deep enterprise integration. Together, they give customers a clear path from experimentation to operational trust.- From promise to productionIBM becoming a launch partner for the AWS Marketplace AI and Tools category was an important signal. Customers want flexibility and choice, but they also want enterprise grade controls. This partnership is designed to give them both.- Watson Orchestrate and Amazon Bedrock Agent CoreAnnounced at re:Invent, this integration brings together AWS’s managed runtime for agents with IBM’s orchestration layer. The result is a full agentic stack where agents can run securely, maintain context, be audited, and coordinate across complex business workflows. This is what running agents responsibly actually looks like.- Project BobWe also spoke about IBM’s Project Bob, which tackles a problem many enterprises quietly struggle with. Most development budgets still go toward modernizing old code instead of building new features. Project Bob brings agentic AI into software modernization, helping teams refactor faster, reduce risk, and deal with long standing skill shortages.My takeaway.Agentic AI will not be adopted because it is powerful. It will be adopted because it is trustworthy. The AWS and IBM partnership is focused exactly on that transition.Learn more about the partnership here -- https://lnkd.in/gyRRuvdk#data #ai #awsreinvent #agents #agenticai #aws #enterprise #IBM #theravitshow

Healthcare runs on data. Most of it is still unstructured!!!! I had a great conversation on The Ravit Show with Lyle McMillin, AVP of Product Management at Hyland, where we went deep into how healthcare organizations are turning unstructured content into real decisions.

A few points that stood out to me:

- Most healthcare data is unstructured. In many organizations, it is close to 80 percent. This includes clinical and admin documents, faxes, emails, and massive imaging files like X-rays, CTs, and MRIs

- Hyland’s Content Innovation Cloud is changing how this data is used. Lyle shared how their Intelligent Med Record solution is helping teams move faster. A beta customer, Nebraska Medicine, saw a 99 percent improvement in time to value, with classification accuracy improving from 95 to 96 percent, driven by new LLM-based capabilities.

- AI is no longer just about automation. It is about decision support.

With Knowledge Discovery, teams can ask natural language questions and get answers with direct links to the exact place in the document. This can cut 60 to 80 percent of the time spent searching and stitching information together.

- Agentic AI was the most interesting part of the discussion.

From classifying documents to pulling missing records, bundling them, and sending claims back to payers, the system can handle most of the workflow. When humans step in, 80 percent of the work is already done.

Healthcare transformation is not only about new systems.

It is about finally making sense of the data we already have.

#data #ai #agentic #healthcare #unstructureddata #agents #knowledgehub #hyland #theravitshow

I had a chance to attend and cover SHIFT by Commvault in New York last week. I just spoke to Tim Zonca, VP of Product Marketing at Commvault, to talk ResOps, Keynote takeaways and more.

What we covered

• ResOps in one line and why it matters now

• How the Unity Platform turns resilience into daily practice across cloud, SaaS, and on-prem

• Synthetic Recovery and what it does to RTO and incident playbooks

• Identity resilience for AD with real-time detection and safe rollback

• Cloud-native protection with cost and energy views that drive smarter policies

Leaders want AI speed with clean recovery and tighter identity control. Tim breaks down how Unity brings these pieces together so teams can move fast and stay safe. Simple language. Clear outcomes. No hype.

My take

Synthetic Recovery plus Cleanroom is a real shift. The AD rollback story closes a gap I see in many incident reviews. The cost and energy view belongs in every daily dashboard!!!!

The interview is live now. I will share more clips and floor notes from SHIFT as sessions go up.

Check out all the announcement links in the comments!

#data #ai #cloud #security #cybersecurity #recovery #resilience #commvault #shift2025 #shift #theravitshow

Most companies think they have a backup problem. What they really have is a recovery problem. I spent the day at Commvault SHIFT, on the ground talking to customers, partners and the Commvault team. I also spoke with Darren Thomson, Field CTO at Commvault, for a focused chat on what they announced and what it means in the middle of real attacks and real pressure on IT teams.

In the interview, we got into:

-- The new Unity Platform and why Commvault is trying to bring data security, data access, identity patterns and cyber recovery into one view

-- How they are thinking about very fast, clean recovery after an attack, not just “we have backups somewhere”

-- Cloud Native Protection and what that actually changes for existing customers and large enterprises already deep into cloud

-- Identity Resilience and why identity is now part of the data security story, not a separate topic

-- Synthetic Recovery and how they use ML signals from anomalies, entropy, threat scans and third party tools to compose a clean state across multiple backup points

-- Real world use cases from regulated industries, where downtime and data loss are simply not an option

What I liked most is that this was not only about shiny new features, but about how security, backup and AI are starting to come together in one place. For teams who are tired of stitching ten tools to get back up after an attack, this direction will be interesting to watch.

If you care about data, security and recovery, this is worth your time.

Check out all the announcements links in the comments!

#data #ai #cloud #security #cybersecurity #recovery #resilience #commvault #shift2025 #shift #theravitshow

From SHIFT by Commvault New York, I sat with Christopher Mierzwa on culture, clarity, and execution!!!!

What you will get

• Real takeaways from his panel

• Why people, mindset, and culture decide security outcomes

• Practical advice for leaders, CISOs, and CIOs

Highlights

• Culture beats tools when pressure hits. If teams do not trust each other, runbooks stall.

• Mindset sets the tone. Treat incidents as system problems, not hero moments.

• Practice builds confidence. Short drills with clear ownership move every metric that matters.

Advice from Chris

• Start with people. Define roles, practice handoffs, review the tape after every drill.

• Build muscle memory. Run small, frequent exercises across IT, SecOps, and the business.

• Keep the board close. Explain risk in plain language and track progress like product work.

My take

Security is a team sport. The best programs invest in culture first, then controls.

#data #ai #cloud #security #cybersecurity #recovery #resilience #commvault #shift2025 #shift #theravitshow

Stop chasing tools. Start fixing decisions. I spoke to Stephen Sciortino, CEO and Founder of Database Tycoon LLC, at Small Data SF by MotherDuck. Clear takeaways for anyone running or advising a data team.What we covered• The real shift from his Brooklyn Data days to independent consulting• Early signals a team will win vs signs they are in trouble• How AI is changing expectations and what must stay the sameWatch the complete interview! Practical, direct, and worth your time.#Data #AI #SmallDataSF #DataEngineering #AI #Analytics #TheRavitShow

Real-time data is no longer a future problem. At Small Data SF by MotherDuck, I sat down with David Yaffe, Co-Founder & CEO at Estuary, to talk about what has changed in the world of data streaming!!!!

A few years ago, real-time data was something most teams put on their “later” list. Expensive. Hard to scale. Too complex for most use cases.

But as David shared, that story has shifted fast.

Here are some takeaways from our conversation:

- Streaming is now viable for everyone

With cheaper compute, mature tooling, and simpler developer experiences, real-time data isn’t a luxury anymore. The barriers that once made it a niche capability are gone

- Batch vs Real-time: Asking the right questions

Before jumping to streaming, David suggests asking what problems you’re solving — speed for the sake of speed rarely pays off. Sometimes batch is just fine. The goal is fit, not flash

- Architecture matters

Moving from batch to streaming means thinking end-to-end: from schema evolution and error handling to observability. Teams that skip this planning end up redoing pipelines

- CDC done right

Change Data Capture is powerful, but it’s easy to misuse. The most common mistake? Treating CDC as an ETL replacement rather than an event system. Understanding that difference prevents pain later

- The conversation was practical, focused, and refreshing.

Real-time isn’t about chasing trends, it’s about enabling faster insights and cleaner data movement with less friction.

If you’ve been wondering when “real-time” becomes realistic, this one will give you a clear answer.

#data #ai #motherduck #smalldatasf #theravitshow

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 value

Practical wins that ship now, not hype cycles

• Flexibility vs governance

How Omni gives analysts room to explore while keeping the shared truth intact

• Why build a modeling layer

What Omni’s own model unlocks for speed, trust, and how far AI can go

• Embedded analytics after the Explo acquisition

When it makes sense to put live insights inside your product and what to avoid

• Simple over clever

Ways AI can remove clicks, clean up metrics, and make BI easier to use

• Common mistake with AI in dashboards

Teams bolt on features before they fix definitions and owners

• The agent future

If agents run dashboards tomorrow, why export to Excel might still matter

If 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 matters

Most 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 APJC

For 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

Some conversations shift how you think about the future of AI. This one did. I just sat down with David Flynn, Founder and CEO of Hammerspace, to talk about something enterprises rarely discuss openly: the real engine behind AI is no longer compute. It is data.

We went deep into why NVIDIA’s AI Data Platform has become the blueprint for modern AI architecture and why Hammerspace is emerging as the layer that actually makes this blueprint real for enterprises.

David broke down how the industry is moving from building AI around compute to building AI around data.

He talked about what the AI Anywhere era looks like, and why the next generation of AI systems will need a global, unified view of data across cloud, edge, and physical environments.

We also talked about the partnership with NVIDIA, how it boosts the productivity of agentic AI, and why enterprises will need data that can move as fast as their models.

David shared how Hammerspace is preparing for what comes next in 2026 and beyond, from scale to power efficiency to open standards.

This is one of those conversations that gives you clarity on where the industry is going and why data architecture is about to become the biggest competitive advantage.

#data #ai #nvidia #hammerspace #gpu #enterprise #agenticai #theravitshow

AI doesn’t fail because of GPUs. It fails because of data.

I had a blast chatting with Jeff Echols, Vice President, AI and Strategic Partners at Hammerspace, from NVIDIA GTC in Washington. We talked about the part of AI nobody is fixing fast enough: getting data to GPUs at the speed the GPUs need it.

Jeff breaks down what makes the Hammerspace AI Data Platform different from traditional AI storage. This isn’t “more storage.” It’s orchestration. Move data globally. Feed it to the right workload. Keep GPUs busy instead of waiting.

We also got into MCP and why an intelligent data control layer is now core to any real AI strategy, plus how Hammerspace lines up with the NVIDIA AI Data Platform reference design so enterprises can actually run this in production, not just in a lab.

And we talked Tier 0. If you want GPU ROI, Tier 0 is about one thing: keep the GPUs fed at full speed.

If you’re trying to scale AI past a pilot, watch this.

#data #ai #nvidiagtc #nvidia #hammerspace #gpu #theravitshow

AI in the public sector isn’t a pilot anymore. It’s running in the real world. Check out my conversation from NVIDIA GTC in Washington with Robin Braun, VP AI Business Development, Hybrid Cloud at Hewlett Packard Enterprise, and Russell Forrest, Town Manager of Town of Vail. This one is important because it’s about AI for cities, not just AI for big tech. I had a blast interviewing both Robin and Russell.

We talked about how HPE is using AI to tackle real problems every city deals with: traffic, safety, and energy efficiency. Robin walked through how you build a smarter, more connected city by turning live data into decisions that actually help people on the ground.

Russell brought the city view from Vail. He explained what it takes to move from “we’re testing AI” to “we’re using this in operations.” We got into risk, cost, and how you deploy without adding complexity or slowing down public services.

We also discussed agentic AI. Not as a buzzword, but as something that can help a town react in real time while still keeping humans in control.

Better safety. Better visitor experience. Better use of resources. Same team.

This is AI as public service infrastructure.

Full interview is now live on LinkedIn and YouTube.

#data #ai #nvidiagtc #nvidia #hammerspace #gpu #storage #theravitshow

Public sector AI is moving fast. The big question is how to build it the right way.

I had a blast chatting with Andrew Wheeler, Hewlett Packard Enterprise Fellow and Vice President at Hewlett Packard Labs and HPC & AI Advanced Development at NVIDIA GTC in Washington, DC.

We talked about:

* How HPE helps agencies build and scale sovereign AI ecosystems

* Why the public sector is a core focus for HPE in AI

* Practical steps for data sovereignty, compliance, and security without slowing innovation

* Where sovereign AI shows up first: government, defense, citizen services, large-scale research

* How HPC and supercomputing power national-scale AI

* What quantum could unlock for government programs and where HPE fits

If you care about trusted AI for cities, states, and national labs, this one is worth a watch.

Full interview now live!!!!

#data #ai #hpe #nvidiagtc #gtc2025 #gpu #sovereign #nvidia #theravitshow

AI ROI is now the real test. I got a chance to chat Joe Corvaia, SVP Sales at DDN at NVIDIA GTC in Washington. This one is for CEOs and exec teams who are being pushed to “do AI” but still can’t show a return.

We started with a simple question. Why are some companies actually getting ROI from AI while others are still stuck in pilots. Joe was very direct on what separates the ones who are scaling from the ones who are still presenting slides.

We talked about infrastructure as a board-level strategy. Not just “buy more GPUs,” but “are you using the GPUs you already bought.” Joe walked through how data infrastructure and data flow have to be part of the conversation in the boardroom, not just in IT.

We got into AI factories and the new DDN Enterprise AI HyperPOD. Built with Supermicro and accelerated by NVIDIA, HyperPOD is designed to take teams from first deployment to serious scale. The idea is you should be able to stand up production AI without rebuilding the stack every time you grow the workload.

Joe also broke down why platforms like HyperPOD, and GPU-aware networking and acceleration like NVIDIA BlueField 4, are about more than performance. They are about efficiency. Max GPU utilization. No idle spend. Faster time to value. This matters not just for big tech, but for regulated industries and sovereign AI programs that need capability and control.

We closed on one topic that every CEO is thinking about right now. How do you future proof AI investments. Joe shared the one principle leaders should follow so they are not buying hardware for headlines, but building an AI foundation that still makes financial sense five years out.

If you own AI strategy, budget, or delivery, watch this.

#data #ai #nvidiagtc #nvidia #hammerspace #gpu #storage #theravitshow

I have seen a lot of AI demos this year. Very few make hard, messy work feel simple. Next week I am going live with Sarah McKenna, CEO of Sequentum, for an AI Magic Wand Launch Celebration on The Ravit Show.

What is happening -

We are going to walk through how Sequentum is using AI to change web data work. Not slides. Actual product.

Here is what we will get into during the show:

- AI Magic Wand (beta)

A new feature that turns high level intent into working web data flows. Think less trial and error, more “tell it what you want and refine.”

- Command Templates

How reusable templates help teams stop rebuilding the same patterns and start sharing what works across the company.

- New tools coming in the next weeks

Unblocker, Paginations and more. All focused on enhancing Sequentum’s data collection capabilities

- Latest in standards

The standards and good practices that matter if you want web data and AI that can stand up in an enterprise.

Why I am excited about this one

Most teams I meet are still stuck between scripts, manual fixes, and brittle tools when it comes to web data. Sequentum is trying to give them a cleaner path with AI on top. This session is about showing that work in public and talking through the real trade offs.

If you care about web data, automation, and using AI for real work, this will be a good one to watch.

Think Mumbai was electric. India’s AI build-out just moved into a higher gear.

I sat down with Sandip Patel, Managing Director, IBM India & South Asia at IBM’s Mumbai office. We unpack what Think Mumbai means for teams building with AI, hybrid cloud, and data at scale.

What stood out and why it matters:

IBM and airtel partnership

• Aim: give regulated industries a safe and fast path to run AI at scale

• How: combine Airtel’s cloud footprint and network with IBM’s hybrid cloud and watsonx stack

• Why it helps: data stays controlled and compliant while workloads flex across on-prem, cloud, and edge

• Impact areas: banking, insurance, public sector, large enterprises with strict governance

First cricket activation on watsonx

• What: AI-driven insights powering a live cricket experience

• Why it matters: shows real-time analytics, content, and decisioning are ready for prime time

• Enterprise takeaway: the same pattern applies to contact centers, fraud, supply chains, and field ops where seconds count

AI value for Indian enterprises today

• Start with governed data and clear ownership

• Use hybrid patterns so models run where the work and data live

• Blend predictive models with generative workflows inside watsonx for measurable lift

• Track outcomes in productivity, risk reduction, customer experience, and time to value

Skills as the force multiplier

• Priority skills: data governance, MLOps, orchestration, security on hybrid cloud

• Team model: small core teams operating a shared platform, federated use cases across business units

• Result: faster move from pilots to production with repeatable guardrails

My take

India is moving from talk to build. The blueprint is open, hybrid, and governed. Partnerships that keep control local while staying flexible will unlock scale. Sports gave us a sharp demo of real-time AI. The next wins will be in operations, customer journeys, and risk.

The interview is live now. Link to the complete interview in the comments!

#data #ai #agentic #ibm #ThinkMumbai #governance #cloud #watsonx #IBMPartner #theravitshow