Rob Collie

The truth about MDM implementation styles: they really don't matter that much. In theory, your MDM architecture should reflect your data and analytics operating model. And yes, there are different data and analytics operating models - roughly aligning to centralized, decentralized, and hybrid. How you manage your master data should align to one of these three styles. Yet, many would have you believe there are 𝐟𝐨𝐮𝐫 distinct styles of MDM. Why are there only three different operating models, but four MDM implementation styles? 𝐓𝐡𝐞 𝐫𝐞𝐚𝐥𝐢𝐭𝐲 is there's really only three implementation styles that are in active use: Centralized, Federated, and Hybrid The fourth implementation style - a registry - exists almost exclusively as a theoretical construct. I've spoken to literally thousands of companies over the last five years, and I've never seen a MDM registry style in the wild. Not one. Seeing a registry style of MDM in production would be about as rare as a spotting a snow leopard in South Florida. Why? Because they're overly - and unnecessarily complex - and well beyond the maturity levels of 99% of all companies. Master data is persisted and managed from either the MDM hub, the source systems, or a combination of both. It's that simple. I do believe that, 𝘦𝘷𝘦𝘯𝘵𝘶𝘢𝘭𝘭𝘺, registry styles of MDM may rise from the ashes - at some point in the future where AI is doing almost all of the heavy lifting for data management. At that point I could see situations where MDM hubs are fully virtualized, and master data processes are managed in real time as data is created or consumed. Until then, if you're considering MDM, then I wouldn't spend any cycles worrying about MDM implementation styles. If you follow best practices and start small, you'll necessarily end up with a hybrid solution where some master data persists in your MDM hub, and some persists in the source systems. Beyond that, and you're creating unnecessary complexity. But that's just my opinion. What do you think? #mdm #masterdatamanagement #masterdata

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🚨 𝗥𝗲𝗹𝗼𝗮𝗱 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗤𝗹𝗶𝗸 𝗖𝗹𝗼𝘂𝗱 Did you notice that Qlik recently added two powerful options to the Reloads API? 🔹 𝗥𝗲𝗹𝗼𝗮𝗱 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀 Pass variable values with the reload request and use them right in your script. Great for parameterized reloads and flexible deployments. 🔹 𝗥𝗲𝗹𝗼𝗮𝗱 𝗪𝗲𝗶𝗴𝗵𝘁 Control when your reload runs in the queue by assigning it a weight (1–10). Higher values = higher priority. Both of these are already available in 𝗜𝗻𝗽𝗵𝗶𝗻𝗶𝘁𝘆 𝗙𝗼𝗿𝗺𝘀 inside your Qlik Cloud. Just choose reload type = Trigger task and start experimenting. 👉 Links to the Inphinity and Qlik technical docs will be in the comments.

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AI conversations are full of buzzwords—agents, tools, workflows, RPA, often used as if they mean the same thing. They don’t. This blog breaks down what each term actually represents, how they differ in real systems, and why confusing them leads to fragile AI deployments, especially in voice, WhatsApp, call centers, and universities. If you’re building or evaluating AI systems, this clarity can save months of trial and error. https://lnkd.in/dDY2SU6R

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The PBIX file is out. Code-first and AI-driven BI are the new normal. Power BI Developers are often slowed down by manual development and lack of version control in Power BI. The challenge: accelerating development with modern tools and automation. Darren Gosbell will showcase new scripting capabilities like TMDL and AI-driven modelling, giving you a glimpse into the future of intelligent BI development. Speaker: Darren Gosbell, Power BI CAT Team Don’t miss this: https://lnkd.in/gVNJjd7W #DataSaturday #PowerBI #TMDL #AIDriven #BusinessIntelligence #Melbourne

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Claude, ChatGPT, Snowflake Intelligence, Cortex Analyst, Databricks Genie, Zenlytic, TextQL. Think about any AI agent you connect to your data. They all get questions from business users like: “What was the average number of daily active users in the last quarter?” These AI agent connects to Euno’s MCP server to retrieve metadata. That’s when Euno gives these agents the missing context they need to understand your data. ** Just a reminder that Euno is a metadata platform so we only access your metadata ** Euno runs a semantic search and looks for dashboards that use DAU (which is semantically equivalent to Daily Active Users). It filters for certified dashboards using the trust signals you’ve already set in Euno. In this example Euno finds two candidates: 1. DAU used in the User Engagement dashboards 2. DAU_NEW used in the Product Statistics dashboard Both dashboards are certified. But Euno also shows how often each has been used in the past 30 days, along with the owner or creator. With that information, the business user can confidently choose the right one and the AI agent can deliver the correct answer. The same applies to tools like Cursor. Let’s say you’re an engineer and ask: “Can you find me the Tableau dashboard for Customer Success?” Cursor will reach out to Euno’s AI assistant to get that BI context because Cursor is focused on dbt development and doesn’t have access to that layer. You can even go further and ask which dbt models power that dashboard and how those models are calculated. Euno feeds Cursor with that real-time context as you build. It’s not just for business users. It’s for developers too.

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Most Qlik environments I see today weren’t built for cloud, scale, or AI. They just grew organically over time. Business logic duplicated across apps. Performance tuned app by app. On-prem assumptions baked in. Limited governance and reuse. Now those same deployments are expected to support modern analytics. Modernizing Qlik isn’t about building new dashboards. It’s about fixing and rebuilding the analytics foundation underneath. At Gogalex, most of our impact today comes from: ✔ refactoring data models and business logic ✔ improving performance and scalability ✔ designing cloud-ready Qlik architectures ✔ modernizing embedded analytics ✔ preparing analytics platforms for AI use cases That’s where real value gets unlocked in existing Qlik environments. Curious how others are approaching Qlik modernization in 2026. #Qlik #AnalyticsModernization #AIReadyData #EmbeddedAnalytics

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