Kirby Mitchell, MBA, CPA

🔹 Data Governance: The Work That Moves Revenue 🔹 People often say data stewardship isn’t flashy. I couldn’t disagree more. Every taxonomy I define, every usage rule I enforce, and every governance process I refine creates downstream trust in data. That trust is the foundation of analytics, compliance, and innovation. Without it, business decisions are guesses—not strategy. 💡 When governance is done right, it’s not about control—it’s about enabling growth. 💡 When data is trusted, it directly fuels revenue, efficiency, and competitive edge. 💡 When stewardship is strategic, businesses thrive. I take pride in being the “revenue mover behind the scenes,” because I know the work I do makes the difference between data chaos and data clarity. 👉 How does your organization demonstrate the business value of governance work to stakeholders who don’t see the daily grind? #DataGovernance #DataStewardship #DataQuality #AnalyticsLeadership #DigitalTransformation #DAMA

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The comments in this article are quite insightful. It is perplexing why there is a profound lack of clarity around this critical function within the modern enterprise. My forthcoming article next week will endeavor to explore some of the root causes of this confusion. Fundamentally, Data, Business Intelligence (BI), Machine Learning (ML), and Artificial Intelligence (AI) should not be viewed as competitive or disparate practices; they are, in fact, interwoven components of a singular, cohesive ecosystem.

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🚢 𝗖𝗵𝗮𝗿𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 — 𝗖𝗵𝗶𝗰𝗮𝗴𝗼 𝗘𝗱𝗶𝘁𝗶𝗼𝗻 I recently attended the Analytics Leaders Network (ALN) event, “Charting the Future of Analytics in Chicago”, and it was truly inspiring. Takeaways I’m Reflecting On: Chicago’s Powerhouse Potential: With a $895B GDP, 400+ major HQs (33 Fortune 500), and 148 colleges within 25 miles, emerging global leader in quantum technology. Chicago has unmatched scale and diversity. Early Talent & Social Capital Matter: 300k+ CPS students, 83% graduation, and nearly 60% college enrollment show a strong pipeline. But skills alone aren’t enough, students and early professionals need networks, confidence, and mentorship. Data, Collaboration & Emerging Tech: By leveraging data across sectors, from transportation to policy making, Chicago can turn insights into meaningful action. Combined with cross-sector collaboration and breakthroughs like the Chicago Quantum Exchange (CQE), the city is poised to lead on a global stage. Chicago may have missed past opportunities (biotech, early AI leadership), but I left this event bullish on Chicago and especially about its next generation. My key takeaway: When we combine data with storytelling, make the benefits tangible, and build genuine partnerships, we unlock a better future for everyone. Big thanks to Tony Lopykinski for organizing and to Tom McCoy, Dominique McKoy, Nuwan Samaraweera, and Alice L. for a thoughtful, inspiring discussion. And on a personal note, it was wonderful to catch up with both old and new friends — and special thanks to Harmeet S. Grewal Savant Labs Mike Alaimo, SHRM-SCP Mavent Analytics , and Sigma for sponsoring this fantastic event. #ChartingTheFuture #ChicagoInnovation #AnalyticsLeaders Question for you 👉 : How can we make data a positive, trusted partner in decision-making whether in business, government, or education?

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Remember when demand forecasting required complex SQL and manual model training? Those days are ending. BigQuery is moving from a "Builder" approach to an "Agentic" approach using Conversational Analytics and the TimesFM (Time Series Foundation Model). Check out this article from Karl Weinmeister to learn more: https://lnkd.in/gCFWu78Y #conversationalanalytics #agenticAI #MCP #GoogleADK #VertexAI #timeseriesforecasting

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Knowing your tools is the fundamental key to a successful outcome. While the rizz around migration to CJA is strong, knowing the fundamentals around the tools is the basic stepping stone. This post outlines the key conceptual differences pretty well.

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"AI is exciting. But disciplined data and analytics leadership is what actually drives enterprise value." Throughout my career, I’ve focused on one thing: turning data, analytics, and AI into measurable business outcomes. After leading large-scale enterprise data, analytics, and AI transformations, I’m ready to bring that experience to my next leadership chapter. Over the past several years, I’ve: • Modernized legacy environments into scalable, cloud-based, governed platforms • Aligned data and analytics strategy directly to revenue growth, risk management, and operational performance • Built and scaled high-performing data engineering, governance, and analytics teams • Enabled responsible, business-aligned AI adoption in highly regulated industries My experience spans financial services, healthcare, and other regulated environments — where data leadership requires both innovation and discipline. I operate at the intersection of executive strategy and practical execution, partnering with CEOs, Boards, Product, Technology, Risk, and Operations leaders to deliver measurable enterprise outcomes. I’m exploring enterprise Director, VP, SVP, Managing Director, and Chief Data / AI Officer roles where transformation, analytics maturity, and AI enablement are strategic priorities. If you know of organizations looking for a pragmatic, business-first data leader, I’d welcome a conversation. Onward. #DataLeadership #ChiefDataOfficer #Analytics #AI

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Had some chats recently with some folks in the #DataFam that really stuck with me. There is a real discovery and trust gap with enterprise IT teams and data professionals when it comes to Tableau. A lot of the capabilities they are asking for already exist for the platform. For example metadata, data lineage, various APIs, data modeling flexibility, and governance layers are all current capabilities in the ecosystem of Tableau. The problem becomes they do not know the capabilities are there and/or they do not trust the platform enough to consider it "good enough" for production. Tableau still gets labeled as a business user tool or a no code layer, so it gets dismissed early. Once that perception is set, most teams never go deeper. They assume the real work has to happen somewhere else, even when the platform is already capable of supporting it. That gap is not about missing features. It is about perception, discovery, and trust. If people never look under the hood, they will never realize how much is actually there.

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🚨 NEW BLOG POST 🚨 The VizQL Data Service from Tableau lets us programmatically query the data we already know and trust. This 'headless BI' API opens up a new world of customization and integration options where your imagination is the limit! You know that I've been super excited about this for quite some time. So, now that VDS is in GA, it's the perfect chance to take a deeper, more technical look at the tool! 🤓 In this blog post (and accompanying video), I will walk you through everything you need to know to get started with VDS and make the most out of your queries. No prior experience with the API is needed! How will you use the VizQL Data Service, #datafam? I can't wait to see what you smarties come up with!! 🤘🏻 https://lnkd.in/eFgFZtn5

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