Sean Chandler

Why Do So Many Data Analytics Projects Miss the Mark? From my first Data/Analytics conference (let’s not date it!) to today, one lesson keeps coming up: “Data Analytics must move the needle for the business.” It’s not rocket science. In fact, it’s a bit of a “D’uh—of course!” moment. If analytics isn’t making a difference, what’s the point? So why do so many analytics investments still fail? Simple: We don’t practice what we preach. It’s easier to chase shiny tech (AI, metaverse, data mesh—we just can’t get enough!) than tackle the hard stuff: changing human behavior, understanding processes, and solving the right problems. For analytics teams to succeed, we need to start by picking the right problems. In healthcare, it’s all about better patient care at a lower cost. That mission should trickle down to every function: - Clinical Care: Improve patient outcomes by reducing readmissions, managing chronic conditions, and addressing social determinants of health. - Rev Cycle: Reduce leakage, improve claim accuracy, and accelerate cash flow. - Managed Care: Optimize payer contracts, reduce denials, and improve risk adjustment accuracy. Once we nail the problem, the real work begins: 1️⃣ Map the end-to-end process—humans, systems, and all the messy interactions. 2️⃣ Go beyond surface-level requirements to uncover insights that drive decisions. 3️⃣ Build solutions that align with business goals, operational realities, and human constraints. Is it a lot of work? Yes. Impossible? No. But it requires a value mindset—and a commitment to becoming a business expert first, and a technologist second. At the end of the day, data analytics isn’t about the tech. It’s about solving the problems that truly matter. #DataAnalytics #Healthcare #ValueMindset #BusinessImpact #DigitalTransformation #ProcessImprovement

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A few weeks ago in Next-Level Tableau we built sparklines using level of details (LODs). Andy Kriebel and I had a little conversation in the comments of that post about doing this same exercise using table calcs. I enthusiastically agreed it would be fun. Today, we went through that exercise. I learned a lot about Table Calcs, specifically I need to brush up on them! We covered the logic behind them, how to check our work, and additional functions like "Total", "Window_Min" and "Window_Max". In my dashboard, I decided I wanted a running total... and wouldn't you know... there's a "Running_Total" function! So cool! It was a great experience and I look forward to growing my knowledge base around Table Calcs! Here's the link to my dashboard on Tableau Public: https://lnkd.in/e7QaHDU4

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Your data career path: What you expect: 1. Learn technical skills 2. Apply them to interesting problems 3. Provide insights 4. Influence decisions 5. Advance based on impact What actually happens: 1. Learn technical skills 2. Discover the data is a mess 3. Spend most of your time cleaning and explaining 4. Watch your insights compete with opinions 5. Realize success depends more on relationships than analytics 6. Gradually shift from technical expert to translator and diplomat 7. Advance based on your ability to navigate organizational complexity The most valuable skill isn't creating the perfect analysis. It's getting the imperfect analysis used effectively. That's the career path they don't tell you about, but should.

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When I first started out in healthcare data analytics, I was confused 87% of the time. I'd hop on client calls and be too terrified to speak - mostly because the jargon flew like confetti in a windstorm. "Which CPT codes do we want to match with the ICD groupings?" "Are we using the CMS-HCC model, or HHS-HCC?" "The APPs are performing better on this BCS HEDIS metric." Not to mention the constant discussion of Medicare, Medicaid, Commercial, and "Dual Eligible" patients & programs, something called "revenue cycle", and the constant discussion of FFS vs. VBC (which tbh I'm still wrapping my head around). It took many years of practice, reading & study to start to connect the dots and actually contribute something useful to a discussion. The learning curve of healthcare is steep. After hundreds of conversations with fellow engineers in healthcare, I realized I wasn't alone in this struggle. That's why we're running a pilot program called The Data Clinic - for engineers & technical folks who want to build in the healthcare space but don't have time to piece together how the data maps to meaning & impact from scattered blog posts and dense regulatory documents. If your interest is piqued by a hands-on, practical bootcamp to learn how to build real healthcare data systems that actually move the needle for patients and providers, sign up for the waitlist! We're also exploring a track that goes the other way - for healthcare providers & subject-matter experts who want to learn the technical side of building data products & analytics. Reach out if you're interested! Link in the comments.

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🚀 New Blog Series Alert I work across multiple analytics tools, and I’ve always found it valuable to compare them side by side. First up: 5 Things Tableau Has That Power BI Doesn’t 👀 Substack: https://lnkd.in/eCdccUKi Medium: https://lnkd.in/eUXP53gk Next, I’ll flip it around and highlight 5 Things Power BI Has That Tableau doesn’t. Then ill be closing the loop with they BOTH do well. Because at the end of the day, these tools aren’t just competitors, they’re both powerful platforms helping organizations turn data into insights. 💡 Question for you: If you use both tools, what’s ONE feature you wish the other had (AND BE NICE IN THE COMMENTS!!!) #Tableau #PowerBI #DataVisualization #Analytics

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