How to play detective with your data: 3 actionable insights

Everyone loves the performance. Nobody thinks about the setup. That’s data science vs data governance. → The models, dashboards, AI use cases they’re the actors in the spotlight. → The definitions, ownership, lineage, quality checks they’re the crew backstage. One gets the applause. The other gets ignored. But without the setup, the magic never happens. This is why so many companies fail with data. They rush to put on the show. They skip the prep. And then they wonder why: → KPIs don’t match → Dashboards tell different stories → AI models collapse outside the lab The magic only works if the setup does. Same for theatre. Same for data. Governance isn’t a side task. It’s the reason the lights turn on and the story makes sense. ➤ Hit Follow John for daily, no-fluff insights on data, AI, and what actually works. 🔔 Tap the bell on my profile to get notified when I post. ♻️ Repost if you found this valuable.

🔊 Louder for the folks in the back! ✨ Everyone loves the AI-powered dashboard. 📊 Nobody wants to talk about the naming conventions, the stale definitions, or the broken lineage underneath it. This hits so hard for anyone in GTM data ops, marketing ops, or RevOps: ->You don’t get performance without setup. ->You don’t get trust without governance. ->And you don’t get impact without alignment. If your KPIs don’t match across teams, your AI model dies in prod, or your dashboard just feels off...chances are the problem isn’t the tech. It’s the foundation. (Not the sci-fi show) So yes, the governance work is unglamorous... No one said it was sexy, but it’s also what turns noise into signal. 📣 Let’s stop treating it like a side hustle. #DataOps #GTM #RevOps #AI #MarketingOps #GovernanceMatters #FixTheFoundation #OpsLife #NoMagicWithoutSetup

Our data industry 'best practices' are increasingly ineffective, and AI isn't helping. How do I know? I know our best practices are ineffective because we've been advocating them for over two decades, where only a small minority of companies are realizing any transformational value from investments in data. We have datapoint after datapoint to show that the 'same old' approaches to data management and governance are only delivering the minimum needed to continue justifying our positions, and keep regulators happy. This is creating a massive divide in most companies between the potential value of data, and it's actual value - and it's only getting worse. Do you think AI will help steer data leaders to more innovative, or novel solutions to this problem? Think again. Using AI to provide insights on data and analytics best practices is like driving a boat off the wake. You'll go in a straight line - but your destination won't be any different than it was before. That's because the insights provided by AI for data 'best practices' are the same insights we know don't work that well. Ask any LLM for insights on data management best practices and they all largely behave the same way, regurgitating the same frameworks, with the same tactics, we've all heard for decades. LLMs are simply repeating same old and tired ideas - with complete confidence. Maturity assessments. Gap analyses. Literacy programs. Governance Frameworks. And on, and on, and on. So what's the answer? We need to start over. We need new ideas, and new perspectives. And most importantly, we need to start thinking differently about our roles, our data, and our customers. That's why I wrote the 'Data Hero Playbook'. It's not about one person saving the day. It's about all of us having the courage to think differently and challenge the status quo. AI is an amazing tool and I'm excited to see where it takes us, but when it comes to 'best practices', its not telling us anything we didnt already know. It's time for a change. What are you doing differently? How are you challenging the status quo? Check out my book! 👉 https://a.co/d/2s3G9K1

Data strategy is the list of rules that you must follow to achieve your data management goals. It is tempting to choose a data tool, a data lake platform, or follow the idea: "let's put all data into our data lake, our AI experts will figure out how to extract value from it". It is a short-term solution that will be inefficient over time. Before ingesting into long-term data or an AI project, you have to decide on the guiding rules that must be followed across the organization to avoid repetition and costly rework. #dataquality #datagovernance #dataengineering

🔑 Data Strategy: The Foundation for Every Successful Data & AI Journey I really liked this insightful post by Piotr on the importance of a well-crafted data strategy. Too often, organizations rush into tools, platforms, or AI initiatives without setting the guiding rules that ensure alignment, governance, and long-term value creation. The reality is: data quality, governance, architecture, and culture must come first—technology follows. A clear data strategy is not just technical—it’s about people, processes, and technology working together to unlock sustainable impact. 📌 Key takeaways: • Define guiding principles that align with business goals. • Prioritize data quality and governance as cornerstones. • Invest in the right infrastructure & talent, with continuous upskilling. • Ensure security, privacy, and measurable outcomes. 💡 The future of digital transformation belongs to organizations that treat data as a strategic asset. #DataStrategy #DataQuality #Governance #AI #DigitalTransformation

🚀 Embedding Data Governance into the Heart of Data Delivery One of the most powerful shifts we’ve made in our journey to build a modern data platform is baking in data governance directly into the delivery process—not as an afterthought, but as a foundational principle. This isn’t just about compliance or control. It’s about setting the standard for what good looks like in enterprise data practices. By engaging the business to own their own data, we’ve created a virtuous circle where data stewards feel empowered to identify opportunities for improvement, drive incremental change, and elevate data quality every single day. The result? A platform that doesn’t just deliver data—it delivers trustworthy, business-ready data that fuels the use cases that matter most, including AI. This is how we make data better, together. One improvement at a time. Every day. 💡 Shout out to our Data Governance Lead Nicole Cherian and our first Data Stewards Karthik Adla and Mylissa Garrick who have pioneered the way. #DataGovernance #ModernDataPlatform #AI #DataStewardship #BusinessValue #DataLeadership #IncrementalImprovement

If Your Data Could Talk… Every business has a living pulse—made up not just of transactions and workflows, but of tacit knowledge, overt relationships, and the proprietary nuance that makes your organization uniquely effective. These elements aren’t always written down. They’re embedded in your systems, your conversations, your decisions. And they live in your data. Now imagine if that data could talk. Not just regurgitate reports, but actually think—like your most specialized team member. One who’s been quietly watching everything unfold, absorbing context, learning patterns, and connecting dots across silos. That’s what a private AI model can unlock. It doesn’t just give your data a voice. It gives it agency. With a multi-model architecture like Proxzar’s, your data becomes a strategic partner—capable of surfacing insights, flagging inefficiencies, and even suggesting optimizations you hadn’t considered. It’s grounded in your reality, not some generic corpus. And because it’s private, it’s yours alone. How you use this awakened intelligence is up to you. But once it starts speaking, you may find it has some very helpful ideas. Want to expand this into a series—maybe “The Awakening of Enterprise Intelligence” or “Conversations with Your Data”? I’d love to riff on that with you. https://lnkd.in/gpvn2T9C

🚀 In today’s data-driven world, your metadata holds the key to smarter decisions and stronger outcomes. That’s where CatoInsights™ comes in—turning raw metadata into actionable intelligence with AI-powered analytics. 📊 Key Highlights: ✅ 360-Degree Data View – Complete visibility across your data ecosystem. ✅ Data Journey Mapping – Trace data flow from source to destination for clarity & efficiency. ✅ Risk Management – Detect potential issues before they escalate. ✅ AI-Powered Insights – Reveal hidden patterns that drive growth. ✅ Seamless Integration – Works smoothly with your existing tools. Unlock the true potential of your metadata with CatoInsights™. 🌟 Analytics that Empower. Insights that Transform. #RiskManagement #AI #DataAnalytics #Metadata #BusinessIntelligence #CatoInsights #DataDriven #DataStrategy #MetadataManagement #BusinessGrowth

Key takeaways from our opening Keynote Fireside Chat 'Beyond the hype - what it means to be AI-First at Data Management Summit New York': - How to make AI "Real" - MIT Sloan survey says only 5% make it to ROI -Fewer than 30% software projects succeed - lacking clear requirements and incomplete testing for AI projects - how do we frame the problem - what are we trying to achieve? - Product thinking - where in the workflow? Where are the guardrails? Where is the human in the loop? -It must be for "Real" users - Problem, Product, Platform - Challenged with shallow use cases -Avoid "Sprinkling" LLMS - e.g. "AI First will fix our strategy" - example case: Credit dispute - what is the definition, what is the right path to resolution? -example: Data governance Classification - how does integrate with the end-user process -Challenge is decomposing / capturing the human elements of process -Need to look top down and bottom up - User training for bottom up - top-down to bring business expertise into the AI discussion include "AI" as a "member of the team #DMSNYC #datamanagement #AI #humanintheloop #LLMs #workflows Stephanie Zhang Julia Bardmesser J.P. Morgan

🚨 Most GenAI projects don’t fail because of weak models. They fail because the data function is brittle, reactive, and misaligned. Data Strategy OS — a 6-layer system that makes your data platform adaptive, agent-ready, and self-healing. 📊 Built for CIOs, CDOs, and data leaders tired of firefighting. 💡Modeling agents, DQ Validators, and Feedback Loops; this is how we scale GenAI with confidence. 📖 Read the full article here: https://lnkd.in/eT8cKF9j #DataStrategy #GenAI #CIO #AgenticAI #LLM #DataOps #ModernDataStack