Usman Khaderi

At Priority Health, we're bridging the gap between data and insights, empowering our team to get the answers they need instantly. Imagine being able to talk to your data securely and promptly, without the need for complex dashboards. In a recent demo, Snowflake's Director of Product Management, Jeff Hollan, showcased how Snowflake Intelligence allows anyone in your enterprise to interact with data – whether it's structured, unstructured, or external. This innovative approach helps you understand what happened and why and even enables you to take action. We're excited to explore Snowflake Intelligence as a proof-of-concept chatbot within our Priority Health modern data warehouse. This AI agent allows users to interact with our data using natural language, eliminating the need for expertise in writing SQL and analyzing results with traditional tools. By leveraging the power of LLMs within our secure Snowflake environment, we're enabling custom AI application development. Currently, our Intelligence chatbot is being tested by a small group of users, focusing on eligibility and claims as initial data domains. This agent can help discover trends and insights that may be cumbersome to identify using traditional dashboards. As with any new technology based on LLMs, we encourage users to verify results from trusted sources to confirm reasonableness of responses. https://lnkd.in/eMfgYCqd

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I’ve been thinking about one of the biggest pitfalls in analytics — analysis paralysis. That endless loop of “what if we cut it this way,” “can we get one more drill-down,” “what happens if we include this subgroup…” It’s not that curiosity is bad. It’s that without a clear framework, analytics can spiral into exploration without action. Over time, I’ve found it helps to think of analytics less as a mirror and more as a stimulant — something that provokes. For me, that stimulation happens across four universal phases: Strategy → Tactics → Intervention → Measurement. It’s a loop, not a one-and-done. Here’s what stimulants taught me about analytics: 👉 https://lnkd.in/gejYztpu What frameworks have you used to ensure analytics drives toward action and outcomes?

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Had a great conversation with Laszlo Mucsi at The Data Storytellers about data leadership, data strategy and building a data culture. It's easy as a data leader to just want to focus on data and technology, but that tends to be easier than culture building and change management. https://lnkd.in/gDjZxtju

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After attending the Snowflake Summit this week, AI is moving faster than many of us could have anticipated. The sheer amount of new capabilities, not just from Snowflake but also from their many partners is going to change the way that we manage data and analytics teams. The keynote with Sam Altman was a view into the future of where generative AI will be in the next 12 months. He thinks that we'll be at a point where gen AI start to develop novel solutions to problems, and possibly ones that no human could think about. I'll say that this time it feels different. In the last AI hype cycle, compute was a rate limiting factor along with company cultures. Gen AI is making these constraints largely irrelevant. The individual and companies that don't embrace it will be left behind. I could see the acceleration of the technology breaking the standard hype cycle. Buckle up.

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The most popular Business Intelligence tool is the Snipping Tool. We spend months building modern dashboards. We pay for PowerBI, Tableau, or Qlik. We build drill-downs, dynamic filters, and real-time feeds. But watch what happens before the Board Meeting. The manager opens the dashboard. They do not look for insights. They open the “Snipping Tool.” They take a screenshot of the chart. And they paste it into PowerPoint. In that second, the data dies. It is no longer interactive. It is no longer real-time. It is a static picture. Why do they do this? Because nobody wants to risk logging into a live system during a presentation, they want safety. They want a slide. We are building tools for explorers. But our executives are just looking for postcards.

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It’s been nearly a year since Tom Davenport's insightful article on the sobering gap between enthusiasm for generative AI and the real work of preparing enterprise data. Back then, the survey of 334 CDOs and data leaders showed widespread excitement—but also that many organizations hadn’t begun the foundational data strategy and curation work needed to leverage generative AI effectively. Fast forward to today: We can see how that underlying data readiness challenge still stands. Sure, we’ve made strides—from pilots to pockets of production use—but the toughest hurdle remains ensuring data is high-quality, well-integrated, and curated so AI can truly drive business outcomes. What’s changed since then? (1) Stronger board and C-suite involvement: We’re seeing more top-level commitment, accelerating data initiatives tied to AI priorities. (2) Focus: Many organizations are pinpointing the highest-value areas to integrate domain-specific data for genAI...perhaps after realizing both the challenge and opportunity! (3) Shift Toward Data Maturity: The best outcomes are coming from organizations that already invested in data and continue to evolve and innovate. One thing is clear: Regardless of the latest AI breakthroughs, companies can’t skip the heavy lifting on data strategy. It’s the fuel for generative AI—and real business value emerges only when we bring the right data to these models. I’ve seen firsthand how important it is to stay disciplined with data foundations, whether you’re rolling out a pilot or scaling up a major transformation initiative. If you’re still in the planning phase, know your data work will pay off exponentially. What’s your take—how has your organization’s data strategy evolved over the past year to capitalize on generative AI? #GenerativeAI #DataStrategy #AITransformation #DigitalInnovation https://lnkd.in/e3rJYqWE

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Man, there is so much I could write about this post. I am glad that Semantic Layers are really being surfaced these days as an integral part of the data stack. My biggest issue with all the Semantic Layers out there - DAX is superior to MDX. Like it's not even close. I can create magic with my DAX code. And when I have so called experts on MDX in the room, and I ask them to do what I consider foundational functions in MDX, something is always slightly off. DAX is tricky, but simple. All that being said, I do wish Microsoft would look at what AtScale is doing with model inheritance, preventing model sprawl, and aggregations. There is so much more work they could be putting into Tabular models that would make every other semantic layer tech comparison not even come close. But, we will get some more copilot instead. Ooof. https://lnkd.in/g8t75dHu

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I recently had the opportunity to speak with Becker’s about AI innovations within UnitedHealthcare’s commercial business. Check out the highlights below or read the full story to see some of the ways we’re leveraging AI. ✅ Over 1,000 AI use cases deployed across the enterprise ✅ Consumer-focused AI-powered tools like Smart Choice help simplify care navigation and save members money ✅ Early results show members who use Smart Choice save an average of $123 per provider visit ✅ We hear from employers and consumers regularly and use our large dataset to make the health care experience simpler and more affordable We have a lot of data, and a commitment to making the health care system work better for the people who rely on it every day. I’m excited to see how our data and innovative and responsible AI technologies can help combat rising costs and create simpler, more personalized experiences. Learn more: https://lnkd.in/gCF5bJzd

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Healthcare spending is growing fast and with it, the urgency to modernize how systems operate. This chart shows hospital expenditures projected to rise from about $2.98 trillion in 2016 to nearly $4.7 trillion by 2027, led by inpatient and outpatient care growth. The pressure points are clear: • Demand is increasing faster than workforce and infrastructure capacity • Manual workflows still dominate billing, triage, and administrative systems • Margins are shrinking while expectations for access and quality rise AI offers a way forward, if it’s implemented strategically. Leadership priorities now: • Use AI for operational intelligence, not just clinical automation • Integrate predictive models to reduce readmissions and optimize resource allocation • Link cost analytics directly to patient outcomes, not just financial KPIs Healthcare’s next productivity leap won’t come from more spending, but from smarter systems. Follow Rizwan Tufail for insight on how AI can align healthcare growth with sustainability and equity

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