Modern Data 101

💰 The cost of data pipeline failures : • 40+ hours per week spent on data quality management • $150K is burnt annually for custom pipeline maintenance per use case • 23% of ML failures are happening due to upstream data changes • 6-8 months has become the average project delivery time • 60% of a data scientist's time is being spent on data cleaning Traditional data pipelines create strong barriers to AI's success that cannot be solved through incremental improvements. The challenges of : semantic ambiguity, quality degradation, temporal misalignment, and format inconsistency require architectural transformation. What a great article by Sagar Paul ! https://lnkd.in/drkiVBnJ

🎬 Trailer Drop! 💡🔥 | Presenting Jan Meskens for 𝐓𝐡𝐞 𝐌𝐨𝐝𝐞𝐫𝐧 𝐃𝐚𝐭𝐚 𝐌𝐚𝐬𝐭𝐞𝐫𝐜𝐥𝐚𝐬𝐬! 🎓   📽️ 𝐂𝐫𝐚𝐟𝐭𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐛𝐲 𝐁𝐚𝐥𝐚𝐧𝐜𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐔𝐬𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐋𝐢𝐭𝐞𝐫𝐚𝐜𝐲 Most data strategies fail not because of technology, but because people can’t use the data. In a world where every company is racing to be “data-driven,” the real differentiator lies in how well your teams can read, communicate, and act on data.   Hosted by Jan Meskens, 𝐃𝐚𝐭𝐚 & 𝐀𝐈 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐚𝐧𝐭, this masterclass is a complete guide to designing and executing a data strategy that puts 𝐡𝐮𝐦𝐚𝐧𝐬 𝐚𝐭 𝐭𝐡𝐞 𝐜𝐞𝐧𝐭𝐫𝐞, balancing usability, literacy, and strategic intent.   🎞️ 𝐘𝐨𝐮’𝐥𝐥 𝐞𝐱𝐩𝐥𝐨𝐫𝐞: 🎬 The hidden adoption gap: why teams still struggle to use data despite advanced tools 🎬 The Data-Driven Organisation Framework: connecting people, processes, and platforms 🎬 How to evolve from descriptive dashboards to prescriptive decision systems 🎬 The literacy-usability balance: tailoring access, improving design, empowering users 🎬 The four components of a winning data strategy: Vision, Roadmap, Governance & Communication   Because data doesn’t drive change, people do. And the best data strategy is the one your people can actually use.   🎥 𝐖𝐚𝐭𝐜𝐡 𝐭𝐡𝐞 𝐭𝐫𝐚𝐢𝐥𝐞𝐫 𝐚𝐧𝐝 𝐞𝐧𝐫𝐨𝐥𝐥 𝐟𝐨𝐫 𝐟𝐫𝐞𝐞: 🔗 https://lnkd.in/dTeG-JRG     #ModernDataMasterclass #DataStrategy #AITransformation

📚 𝐌𝐨𝐝𝐞𝐫𝐧 𝐃𝐚𝐭𝐚 𝐏𝐢𝐜𝐤𝐬: 𝐓𝐡𝐢𝐬 𝐰𝐞𝐞𝐤'𝐬 𝐌𝐮𝐬𝐭-𝐑𝐞𝐚𝐝𝐬 Dive into the week's best resources to be at the forefront of the data, AI, and tech strategy! 💠 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐟𝐫𝐨𝐦 𝐅𝐢𝐫𝐬𝐭 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬: 𝐑𝐞𝐟𝐥𝐞𝐜𝐭𝐢𝐨𝐧 Mariya Mansurova dissects Reflection, a fundamental agentic AI design pattern that creates feedback loops to improve LLM accuracy. Similar to how humans review their work, reflection involves asking an LLM to critique and refine its own output or course-correct based on external feedback. Read more: https://lnkd.in/gnYWxUeU 💠 𝐓𝐡𝐞 𝐫𝐢𝐬𝐞 𝐨𝐟 𝐚𝐜𝐜𝐨𝐮𝐧𝐭𝐚𝐛𝐥𝐞 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬: 𝐇𝐨𝐰 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐠𝐫𝐚𝐩𝐡𝐬 𝐬𝐨𝐥𝐯𝐞 𝐭𝐡𝐞 𝐚𝐮𝐭𝐨𝐧𝐨𝐦𝐲 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 Jans Aasman argues th at the term 'AI agent' suffers from three competing definitions: the Executive's Agent, the Developer's Agent, and the Researcher's Agent. He warns that autonomy without accountability is high risk, potentially causing cascading errors. Read more: https://lnkd.in/dvxQ8R6E 💠 𝐖𝐡𝐲 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐃𝐢𝐬𝐚𝐩𝐩𝐨𝐢𝐧𝐭 Maria Sukhareva argues that the current hype around AI agents far exceeds their actual capabilities, echoing Andrej Karpathy's claim that they are "cognitively lacking." The fundamental problem is that agents are built as next-token predictors trained to select from a limited, predefined tool set. Read more: https://lnkd.in/g7tzRukw 💠 𝐖𝐢𝐥𝐥 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐌𝐚𝐤𝐞 𝐓𝐡𝐞 𝐏𝐞𝐫𝐟𝐞𝐜𝐭 𝐂𝐨𝐧𝐭𝐫𝐚𝐜𝐭? Will Rinehart explores the legal and economic implications of Sam Altman's vision of autonomous AI agents negotiating contracts. The discussion centers on the economics concept of incomplete contracts, which often persist due to a balance between front-end costs and back-end costs. Read more: https://lnkd.in/dDiim4ah 💠 𝐅𝐫𝐨𝐦 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭𝐬 𝐭𝐨 𝐀𝐠𝐞𝐧𝐭𝐬: 𝐀𝐈 𝐒𝐭𝐚𝐫𝐭𝐬 𝐓𝐚𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐋𝐞𝐚𝐝 AI Fyndings Newsletter explores the shift where AI moves beyond assistance to take on proactive roles in business, commerce, and design. In marketing, commerce, and design this trend demonstrates AI's growing ability to take the lead on execution and function as an active seller or creator. Read more: https://lnkd.in/d2supyiu Never miss a top resource: subscribe for weekly updates: https://lnkd.in/dWRBjVCb 🔔 Follow Modern Data 101 for the best insights from the data world, delivered straight to you!

📚 𝐌𝐨𝐝𝐞𝐫𝐧 𝐃𝐚𝐭𝐚 𝐏𝐢𝐜𝐤𝐬: 𝐓𝐡𝐢𝐬 𝐰𝐞𝐞𝐤'𝐬 𝐌𝐮𝐬𝐭-𝐑𝐞𝐚𝐝𝐬 Dive into the week's best resources to be at the forefront of the data, AI, and tech strategy! 💠 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐟𝐫𝐨𝐦 𝐅𝐢𝐫𝐬𝐭 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬: 𝐑𝐞𝐟𝐥𝐞𝐜𝐭𝐢𝐨𝐧 Mariya Mansurova dissects Reflection, a fundamental agentic AI design pattern that creates feedback loops to improve LLM accuracy. Similar to how humans review their work, reflection involves asking an LLM to critique and refine its own output or course-correct based on external feedback. Read more: https://lnkd.in/gnYWxUeU 💠 𝐓𝐡𝐞 𝐫𝐢𝐬𝐞 𝐨𝐟 𝐚𝐜𝐜𝐨𝐮𝐧𝐭𝐚𝐛𝐥𝐞 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬: 𝐇𝐨𝐰 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐠𝐫𝐚𝐩𝐡𝐬 𝐬𝐨𝐥𝐯𝐞 𝐭𝐡𝐞 𝐚𝐮𝐭𝐨𝐧𝐨𝐦𝐲 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 Jans Aasman argues th at the term 'AI agent' suffers from three competing definitions: the Executive's Agent, the Developer's Agent, and the Researcher's Agent. He warns that autonomy without accountability is high risk, potentially causing cascading errors. Read more: https://lnkd.in/dvxQ8R6E 💠 𝐖𝐡𝐲 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐃𝐢𝐬𝐚𝐩𝐩𝐨𝐢𝐧𝐭 Maria Sukhareva argues that the current hype around AI agents far exceeds their actual capabilities, echoing Andrej Karpathy's claim that they are "cognitively lacking." The fundamental problem is that agents are built as next-token predictors trained to select from a limited, predefined tool set. Read more: https://lnkd.in/g7tzRukw 💠 𝐖𝐢𝐥𝐥 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐌𝐚𝐤𝐞 𝐓𝐡𝐞 𝐏𝐞𝐫𝐟𝐞𝐜𝐭 𝐂𝐨𝐧𝐭𝐫𝐚𝐜𝐭? Will Rinehart explores the legal and economic implications of Sam Altman's vision of autonomous AI agents negotiating contracts. The discussion centers on the economics concept of incomplete contracts, which often persist due to a balance between front-end costs and back-end costs. Read more: https://lnkd.in/dDiim4ah 💠 𝐅𝐫𝐨𝐦 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭𝐬 𝐭𝐨 𝐀𝐠𝐞𝐧𝐭𝐬: 𝐀𝐈 𝐒𝐭𝐚𝐫𝐭𝐬 𝐓𝐚𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐋𝐞𝐚𝐝 AI Fyndings Newsletter explores the shift where AI moves beyond assistance to take on proactive roles in business, commerce, and design. In marketing, commerce, and design this trend demonstrates AI's growing ability to take the lead on execution and function as an active seller or creator. Read more: https://lnkd.in/d2supyiu Never miss a top resource: subscribe for weekly updates: https://lnkd.in/dWRBjVCb 🔔 Follow Modern Data 101 for the best insights from the data world, delivered straight to you!

“𝐼𝑓 𝑦𝑜𝑢𝑟 𝐿𝐿𝑀-𝑝𝑜𝑤𝑒𝑟𝑒𝑑 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝑠 𝑎𝑟𝑒 𝑑𝑟𝑎𝑖𝑛𝑖𝑛𝑔 𝑦𝑜𝑢𝑟 𝑏𝑢𝑑𝑔𝑒𝑡 𝑓𝑎𝑠𝑡 𝑜𝑟 𝑡𝑎𝑘𝑖𝑛𝑔 𝑡ℎ𝑒𝑖𝑟 𝑠𝑤𝑒𝑒𝑡 𝑡𝑖𝑚𝑒 𝑡𝑜 𝑟𝑒𝑠𝑝𝑜𝑛𝑑, 𝑡ℎ𝑒 𝑠𝑒𝑐𝑟𝑒𝑡 𝑙𝑖𝑒𝑠 𝑛𝑜𝑡 𝑗𝑢𝑠𝑡 𝑖𝑛 𝑡ℎ𝑒 𝑚𝑜𝑑𝑒𝑙𝑠 𝑡ℎ𝑒𝑚𝑠𝑒𝑙𝑣𝑒𝑠, 𝑏𝑢𝑡 𝑖𝑛 ℎ𝑜𝑤 𝑦𝑜𝑢 𝑓𝑒𝑒𝑑 𝑡ℎ𝑒𝑚.” This piece highlights a crucial reality: LLMs are like high-performing talent. They can be expensive and slow if mismanaged. The problem is feeding them raw, unfiltered data and paying top dollar for the LLM to do basic prep work. The magic is in the ingredient: 𝐡𝐢𝐠𝐡-𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐝𝐚𝐭𝐚 𝐚𝐬 𝐋𝐋𝐌 𝐟𝐮𝐞𝐥. If you're dealing with budget bloat or high inference latency, you know the frustration. This isn't an anomaly; it’s a strategic failure driven by ignoring how data is delivered. 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐜𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐲. In this essential read, author Swami Achari makes a powerful case for a data-first paradigm shift. 𝐓𝐡𝐞 𝐒𝐡𝐢𝐟𝐭 The key to LLM optimisation is treating your data as first-class 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬 and leveraging their architecture to cut costs and boost speed. The article details 𝟏𝟎 𝐰𝐚𝐲𝐬 to achieve only that. From using 𝐯𝐞𝐫𝐬𝐢𝐨𝐧𝐞𝐝 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐲 𝐚𝐧𝐝 𝐬𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐜𝐚𝐜𝐡𝐢𝐧𝐠 to implementing 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 that tracks usage and enforces policy. Learn how to unlock your LLMs' true potential, ensure 𝐭𝐨𝐤𝐞𝐧 𝐬𝐚𝐯𝐢𝐧𝐠𝐬 𝐚𝐭 𝐬𝐜𝐚𝐥𝐞, and accelerate time-to-value: https://lnkd.in/dx2Ekf8v For more expert content, follow Modern Data 101!

The thing about AI or any trending technology is that 𝐦𝐨𝐬𝐭 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐬 𝐚𝐫𝐞𝐧’𝐭 𝐧𝐚𝐭𝐢𝐯𝐞 𝐭𝐨 𝐢𝐭, but now aim to become AI-enabled. And that’s where the cracks often start to show. Goal setting isn’t a problem since most enterprises strategically need to adapt to these technologies to scale their business gains. But the issue is that unless they are new AI natives who are 𝐛𝐮𝐢𝐥𝐭 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐠𝐫𝐨𝐮𝐧𝐝 𝐮𝐩 𝐰𝐢𝐭𝐡 𝐀𝐈 𝐚𝐬 𝐭𝐡𝐞𝐢𝐫 𝐜𝐨𝐫𝐞 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧, 𝐭𝐡𝐞 𝐀𝐈-𝐞𝐧𝐚𝐛𝐥𝐞𝐝 𝐫𝐞𝐪𝐮𝐢𝐫𝐞 an ecosystem built around. 2025 has quietly become the year of reckoning for this. Everyone’s chasing models, copilots, and use cases, but few are fixing the foundational layer that makes any of this sustainable: its 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞. Dion Harris, had zeroed down on this, mentioning that despite new AI models being introduced every week, the time frame for 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐨𝐮𝐭 𝐭𝐡𝐞 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐭𝐡𝐚𝐭 𝐜𝐚𝐧 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 𝐀𝐈 𝐢𝐬 𝐜𝐮𝐫𝐫𝐞𝐧𝐭𝐥𝐲 𝐦𝐞𝐚𝐬𝐮𝐫𝐞𝐝 𝐢𝐧 𝐲𝐞𝐚𝐫𝐬. In the same event, Yee Jiun (YJ) Song said how AI is exposing how little we actually understand about infrastructure. AI success depends on 𝒊𝒏𝒇𝒓𝒂𝒔𝒕𝒓𝒖𝒄𝒕𝒖𝒓𝒆 𝒎𝒂𝒕𝒖𝒓𝒊𝒕𝒚, but what happens in multiple scenarios is that most orgs over-invest in models and under-invest in the self-service plumbing that feeds them. The symptoms: 💭 Fragmented data stacks. 💭 Siloed ownership between data engineers, scientists, and AI teams. 💭 Manual integration of context and data quality signals. This infrastructure gap implies that AI readiness is not only about models or data quality, but about the 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦, 𝐩𝐥𝐮𝐦𝐛𝐢𝐧𝐠, 𝐚𝐧𝐝 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 behind production-scale AI. How ready is the infrastructure for AI, do you think? Follow us for more: https://lnkd.in/dDT7FB3y

𝐑𝐢𝐬𝐞 𝐨𝐟 𝐓𝐡𝐞 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: 𝐖𝐡𝐞𝐫𝐞 𝐌𝐞𝐭𝐚 𝐢𝐬 𝐌𝐨𝐫𝐞 𝐕𝐢𝐭𝐚𝐥 𝐓𝐡𝐚𝐧 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 Context is hyperfuel for AI uprising. It's the crucial meta layer, or data on data, that lifts information from raw characters into meaning. Yet, current data platforms are built to store, not to understand. This creates a massive gap for AI adoption. The solution isn't another tool, but a Context Architecture that reconstructs meaning through deduction. In this strategic piece, Animesh Kumar argues that metadata is no longer a by-product; it's THE product. He outlines a structural shift from managing data to understanding it by proposing the Trinity of Deduction, Productisation, and Activation as the core intelligence loop for enterprise AI. 𝐖𝐡𝐚𝐭’𝐬 𝐈𝐧𝐬𝐢𝐝𝐞 𝐭𝐡𝐢𝐬 𝐚𝐫𝐭𝐢𝐜𝐥𝐞? ✅ 𝐓𝐡𝐞 𝐂𝐨𝐥𝐥𝐚𝐩𝐬𝐞 𝐨𝐟 𝐂𝐨𝐧𝐭𝐞𝐱𝐭: Why traditional Catalogs became passive search indexes for unknowns, describing what exists but failing to deduce why it matters. ✅ 𝐓𝐡𝐞 𝐓𝐡𝐫𝐞𝐞-𝐋𝐚𝐲𝐞𝐫 𝐃𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥: How a reasoning system moves beyond simple search to infer conceptual closeness across Meta, Profile, & Usage. ✅ 𝐒𝐡𝐢𝐟𝐭 𝐋𝐞𝐟𝐭 𝐰𝐢𝐭𝐡 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐬𝐚𝐭𝐢𝐨𝐧: Flipping the traditional linear workflow, where the Deduction Stack feeds contextual intelligence to the Productise Stack to generate contracts, schemas, & quality checks automatically. ✅ 𝐀𝐜𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧 𝐒𝐭𝐚𝐜𝐤 𝐚𝐬 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐯𝐞 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞: The final layer where a critical mass of data products communicates through MCP interfaces, enabling agentic frameworks to reason & dynamically assemble knowledge across the system. ✅ 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐚𝐭 𝐭𝐡𝐞 𝐂𝐞𝐧𝐭𝐫𝐞: Why Catalogs won't disappear but will shift to the periphery, evolving from static inventories into dynamic indexes of deduced knowledge. 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 𝐟𝐨𝐫 𝐘𝐨𝐮𝐫 𝐎𝐫𝐠𝐚𝐧𝐢𝐬𝐚𝐭𝐢𝐨𝐧: For AI to truly scale, your data platform must become a reasoning engine. This Context Architecture closes the intelligence loop: it allows you to see what you have, build what you need, & activate what you build, enabling your platform to evolve into a collective intelligence system where AI agents can operate with agency & purpose. 👉 Ready to transform your metadata into your most valuable product? Dive into this piece and learn how to implement the Trinity of Deduction, Productisation, & Activation. ➡️ 𝐑𝐞𝐚𝐝 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞 𝐡𝐞𝐫𝐞: https://lnkd.in/dzYdZaC2 🗣️ 𝐂𝐚𝐥𝐥𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐄𝐱𝐩𝐞𝐫𝐭𝐬! 𝐀𝐭 𝐌𝐨𝐝𝐞𝐫𝐧 𝐃𝐚𝐭𝐚 𝟏𝟎𝟏, we collaborate with industry leaders to bring top-tier insights to a thriving data community. Have a unique perspective to share? We’re all ears! (All submissions are vetted for quality & relevance.) 🔔 Follow 𝐌𝐨𝐝𝐞𝐫𝐧 𝐃𝐚𝐭𝐚 𝟏𝟎𝟏 and stay updated with our weekly highlights from the modern data space.

Another great article from Modern Data 101 and Animesh Kumar I really like when he says: "A model is an informative representation of an object, person, or system." And this is the reality when we talk about information/communication. It's (almost) all a matter of representation, etymology (semantics), epistemology (ontology), and a good sense of reasoning. The secret is to train our levels of abstraction. 😁 https://lnkd.in/dFpUpax5

If this is going to be 𝐚 𝐜𝐚𝐬𝐞 𝐚𝐠𝐚𝐢𝐧𝐬𝐭 𝐭𝐡𝐞 𝐌𝐞𝐝𝐚𝐥𝐥𝐢𝐨𝐧 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞, we need to lawyer up on both ends and make a case for Medallion first: its significance, reasons behind its emergence, impact, and why it worked or didn’t work. And that's what we did in this spiel around the 𝐜𝐨𝐦𝐩𝐚𝐫𝐚𝐭𝐢𝐯𝐞 𝐥𝐨𝐨𝐤 𝐚𝐭 𝐭𝐰𝐨 𝐭𝐡𝐫𝐞𝐞-𝐭𝐢𝐞𝐫𝐞𝐝 𝐬𝐲𝐬𝐭𝐞𝐦𝐬: The Medallion Architecture and the Data Product Ecosystem. 🗂️ 𝐀𝐜𝐜𝐞𝐬𝐬 𝐭𝐡𝐞 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐜𝐨𝐦𝐩𝐚𝐫𝐚𝐭𝐢𝐯𝐞 𝐜𝐚𝐬𝐞 𝐡𝐞𝐫𝐞: https://lnkd.in/dBnRdthH 🧱 𝐀 𝐂𝐚𝐬𝐞 𝐟𝐨𝐫 𝐌𝐞𝐝𝐚𝐥𝐥𝐢𝐨𝐧 𝐛𝐞𝐟𝐨𝐫𝐞 𝐚 𝐂𝐚𝐬𝐞 𝐀𝐠𝐚𝐢𝐧𝐬𝐭 From what is the Medallion Architecture to why it emerged and what it solved. What are the real differences from the big picture (of the emergence story) that we are used to seeing and hearing? We keep the case on Medallion short, for there are so many good cases on it already. And we acknowledge the impact of Medallion across the data industry. The most impactful one being how it established the benefits of a tiered system. But it also 𝑡𝑖𝑟𝑒𝑑 the system. 💡 𝐓𝐡𝐞 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 We don't waste much time before jumping into a case for the Data Product Ecosystem. The 𝐥𝐞𝐯𝐞𝐫 𝐰𝐞 𝐡𝐚𝐯𝐞 𝐮𝐬𝐞𝐝 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐭𝐡𝐞 𝐜𝐚𝐬𝐞 𝐢𝐬 𝐚 𝐏𝐮𝐬𝐡 𝐯𝐬. 𝐏𝐮𝐥𝐥 𝐥𝐞𝐧𝐬. This lever enabled us to paint a very clear picture of the entire story through one end-to-end argument. While we've addressed some critical case points in the second half of this piece, each argument with dedicated reasoning; the 𝐏𝐮𝐬𝐡 𝐯𝐬. 𝐏𝐮𝐥𝐥 𝐩𝐞𝐫𝐬𝐩𝐞𝐜𝐭𝐢𝐯𝐞 𝐞𝐧𝐜𝐨𝐦𝐩𝐚𝐬𝐬𝐞𝐬 𝐚𝐥𝐥 𝐨𝐟 𝐭𝐡𝐞𝐦 𝐢𝐧𝐭𝐨 𝐨𝐧𝐞 𝐛𝐢𝐠 𝐩𝐢𝐜𝐭𝐮𝐫𝐞. 🎬 𝐀𝐥𝐥 𝐚𝐫𝐠𝐮𝐦𝐞𝐧𝐭𝐬 𝐡𝐞𝐫𝐞: https://lnkd.in/dBnRdthH If you have (and most of you do) implemented or experienced working in layers of the Medallion tiers, what have been the wins and losses, and how did you build on them? #DataArchitecture #DataProducts #MedallionArchitecture