How to avoid the hidden costs of messy data with Databricks

Your Data Doesn’t Need a Lake. It Needs a Map. In my conversations with data leaders, I often hear: “We need to build a data lake first.” Sound familiar? Here’s the truth: data works best where it belongs — in source systems, with clear ownership, connected through modern APIs. Many AI projects fail not because of missing warehouses, but because teams don’t know: - Where data lives - What it actually means - Who owns it - How to access it safely We call this the federated approach. Map your landscape, document definitions, connect responsibly, and establish ownership. Result? Often, 3 months vs 3 years, same outcome, fraction of the cost. The best data strategy isn’t centralization. It’s accessibility and trust. How is your organization handling AI data readiness today?

⚡ The fastest data teams don’t skip governance. They’ve automated it. 🚦 Data Governance 2.0 – From Bureaucracy to Business Enabler Most teams still think governance = slowdowns. But modern data platforms prove the opposite. Here’s the shift happening now 👇 ⚙️ Old Governance: Meetings, approvals, and “ask-for-permission” workflows. 🚀 Governance 2.0: Runs automatically in the background — with data contracts, policy-as-code, and federated ownership. 💡 What changes: • Guardrails replace gatekeepers • Rules become runnable • Definitions stay consistent across BI, AI, and ML • Control planes, not committees, keep it all aligned The result? Faster delivery. Safer data. Fewer debates. And a foundation that powers analytics, AI frontends, and data science — all at once. 👉 Read the full story on my blog: “Data Governance 2.0 – From Bureaucracy to Business Enabler” [🔗 Link: https://lnkd.in/d2byy2_T] #DataGovernance #InformationArchitecture #AI #DataScience #Analytics #DigitalTransformation

What if I told you that "data platform" is a trap? And it is hiding at least four beasts inside! ~~~ I've seen this term lure leaders into ambiguity, bloating budgets and stalling innovation. It's not one monolith; it's a hydra with multiple heads, each demanding different skills, tools, and strategies. 1. Reporting and Dashboards 2. Data Hub for Real-Time System Integration 3. ML and Advanced Analytics Factory 4. LLMs and Agentic AI Arena Vendors sell silver bullets, execs buy in without challenging assumptions. When creating your data platform, have you tamed your beast?

𝗧𝗵𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝘄𝗶𝗻𝗻𝗶𝗻𝗴 𝗶𝗻 𝟮𝟬𝟮𝟱 𝗮𝗿𝗲𝗻’𝘁 𝘁𝗵𝗲 𝗼𝗻𝗲𝘀 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝗔𝗜... 𝗯𝘂𝘁 𝘁𝗵𝗲 𝗼𝗻𝗲𝘀 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗰𝗹𝗲𝗮𝗻𝗲𝘀𝘁 𝗱𝗮𝘁𝗮 🔥📊 And almost nobody is talking about this shift. ⚠️ 𝗕𝗲𝗰𝗮𝘂𝘀𝗲 “𝗬𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝘪𝘴 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆.”   Not a tagline. A market signal. ⏱️ Look at what’s happening 👇 - 72% of enterprises say 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗶𝘀 𝘁𝗵𝗲 #𝟭 𝗯𝗹𝗼𝗰𝗸𝗲𝗿 to deploying AI at scale 📉   - 55% of engineering time is wasted on 𝗱𝗮𝘁𝗮 𝗰𝗹𝗲𝗮𝗻𝘂𝗽 + 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗳𝗶𝗿𝗲𝗳𝗶𝗴𝗵𝘁𝗶𝗻𝗴 🧯   - Teams with strong governance ship AI features 𝟯.𝟰X 𝗳𝗮𝘀𝘁𝗲𝗿 ⏱️⚡ That means the real competitive edge in 2025 isn’t GPUs…   It’s 𝗴𝗼𝘃𝗲𝗿𝗻𝗲𝗱, 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲, 𝘁𝗿𝘂𝘀𝘁𝘄𝗼𝗿𝘁𝗵𝘆 𝗱𝗮𝘁𝗮. Here’s the 𝘮𝘪𝘤𝘳𝘰-𝘴𝘵𝘰𝘳𝘺 everyone’s living through:   AI pilots look magical in demos ✨   …but collapse when they hit messy, siloed, undocumented real-world data.   Every. Single. Time. 💥 So here’s the 𝟱-𝘀𝘁𝗲𝗽 𝗺𝗶𝗻𝗶-𝗹𝗲𝘀𝘀𝗼𝗻 for leaders + engineers ⚙️🧠: 1️⃣ 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 → Standardize metrics, enforce freshness SLAs, kill zombie tables.   2️⃣ 𝗟𝗶𝗻𝗲𝗮𝗴𝗲 → Make every dataset traceable across the stack. No orphan pipelines.   3️⃣ 𝗔𝗰𝗰𝗲𝘀𝘀 → Role-based, frictionless, 𝘢𝘶𝘥𝘪𝘵𝘢𝘣𝘭𝘦 access for builders 🔑   4️⃣ 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 → Clear owners, automated checks, continuous monitoring 📏   5️⃣ 𝗖𝘂𝗹𝘁𝘂𝗿𝗲 → Treat data like infrastructure, not an afterthought. It compounds. 💯 This is the real AI infrastructure layer — not the model.   𝗠𝗼𝗱𝗲𝗹𝘀 𝗮𝗿𝗲 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝗮𝗯𝗹𝗲. 𝗖𝗹𝗲𝗮𝗻 𝗱𝗮𝘁𝗮 𝗶𝘀𝗻’𝘁. ⚔️🤖 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁:   By 2026, the companies with the strongest data foundations will unlock 𝟱–𝟭𝟬𝗫 𝗳𝗮𝘀𝘁𝗲𝗿 𝗔𝗜 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝘃𝗲𝗹𝗼𝗰𝗶𝘁𝘆 and crush competitors who still “fix data later.” 🌏📈 💭 𝗕𝗲 𝗵𝗼𝗻𝗲𝘀𝘁: 𝗜𝘀 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴’𝘀 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗔𝗜 𝗯𝗹𝗼𝗰𝗸𝗲𝗿 𝘵𝘦𝘤𝘩𝘯𝘪𝘤𝘢𝘭... 𝗼𝗿 𝗶𝘀 𝗶𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘁𝗵𝗲 𝘀𝘁𝗮𝘁𝗲 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮? #DataStrategy #DataEngineering #ArtificialIntelligence #AIatScale #CleanDataWins #FutureOfAI

Naked calls itself a data company. So does WeBuyCars. Notice a pattern? The countries most innovative companies aren't just using data. They've made data their foundation. Their competitive advantage. Their entire business model. Because data isn't just the new currency - it's the new infrastructure. But here's the gap: most businesses are sitting on gold mines of data and treating it like... well, like data. Scattered across systems. Inconsistent definitions. No single source of truth. At Tyto Insights, we help businesses make the same shift these companies did. We don't just organise your data, we help you become a data company. How? - Data Engineering - We build your data infrastructure. One database/warehouse in your ecosystem. One source of truth. Every report, every decision, every insight pulls from there. - Data Governance - We give your data a language centered around YOUR business, YOUR customers, YOUR operations. Not generic templates. Your reality. - Analytics - We turn that foundation into competitive advantage. Insights that drive decisions. Intelligence that scales. Why does this matter now? Because AI is here. And every AI model is only as powerful as the data foundation it's built on. Companies with clean, governed, engineering-ready data will build AI that transforms their business. Companies without it will build expensive experiments. The companies winning right now aren't the ones with the most data. They're the ones who've made data their foundation. Is your business ready to become a data company?

The world of unstructured data is exploding. Every AI note-taker, Slack thread, and call summary adds to it...insights, tone, nuance…the kind of stuff you can sip slowly and learn from. Think of it like small-batch data...handcrafted, rich, and best enjoyed in small doses. But to see trends, make decisions, and forecast...you still need structured data. That’s your large-scale production run; consistent, measurable, repeatable. The problem? Most teams are swimming in small-batch data and starving for structure. Their AI note-takers capture everything, but none of it becomes reportable. Imagine if every “pain point,” “competitor,” or “next step” mentioned in calls automatically rolled up into Salesforce fields that can help leaders spot macro trends, like a picklist value. Suddenly, unstructured turns into understood. Because insights don’t become impact until they’re structured. Tools like Cloudingo help bridge that gap...turning all that rich, unstructured context into usable, reportable data.

🧠 𝗧𝗵𝗲 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗦𝘁𝗮𝗰𝗸: 𝗧𝗵𝗲 𝗡𝗲𝘄 𝗟𝗮𝘆𝗲𝗿 𝗔𝗯𝗼𝘃𝗲 𝗗𝗮𝘁𝗮 Every company built a 𝗱𝗮𝘁𝗮 𝘀𝘁𝗮𝗰𝗸 — pipelines, models, dashboards. Then came the 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗹𝗮𝘆𝗲𝗿 — making data consistent and meaningful. Now it’s time for the 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗹𝗮𝘆𝗲𝗿 — making data actually useful. Here’s the pattern: • Storage made data available. • Semantics made data understandable. • Context made data connected. • 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗺𝗮𝗸𝗲 𝗱𝗮𝘁𝗮 𝗮𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲. Each layer didn’t replace the last — 𝗶𝘁 𝗰𝗼𝗺𝗽𝗼𝘂𝗻𝗱𝗲𝗱 𝗶𝘁. A better semantic model makes every dashboard smarter. A decision layer makes every semantic model operational. The Decision Stack doesn’t kill BI or AI. It makes both finally work together. Because the future isn’t just analyzing what happened — 𝗜𝘁’𝘀 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗮𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝗵𝗮𝗽𝗽𝗲𝗻 𝗻𝗲𝘅𝘁. ⸻ 💡 The pattern repeats every decade: Each new layer turns information into leverage. 𝗧𝗵𝗲 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗦𝘁𝗮𝗰𝗸 is that next multiplier. #TheDecisionStack #DecisionIntelligence #DataIntelligence #SemanticLayer 

You wouldn't cook a meal with rotten ingredients, right? Yet, businesses pump messy data into AI models daily— ..and wonder why their insights taste off. Without quality, even the most advanced systems churn unreliable insights. Let’s talk simple — how do we make sure our “ingredients” stay fresh? Start Smart → Know what matters: Identify your critical data (customer IDs, revenue, transactions) → Pick your battles: Monitor high-impact tables first, not everything at once Build the Guardrails: → Set clear rules: Is data arriving on time? Is anything missing? Are formats consistent? → Automate checks: Embed validations in your pipelines (Airflow, Prefect) to catch issues before they spread → Test in slices: Check daily or weekly chunks first—spot problems early, fix them fast Stay Alert (But Not Overwhelmed): → Tune your alarms: Too many false alerts = team burnout. Adjust thresholds to match real patterns → Build dashboards: Visual KPIs help everyone see what's healthy and what's breaking Fix It Right: → Dig into logs when things break—schema changes? Missing files? → Refresh everything downstream: Fix the source, then update dependent dashboards and reports → Validate your fix: Rerun checks, confirm KPIs improve before moving on Now, in the era of AI, data quality deserves even sharper focus. Models amplify what data feeds them — they can’t fix your bad ingredients. → Garbage in = hallucinations out. LLMs amplify bad data exponentially → Bias detection starts with clean, representative datasets → Automate quality checks using AI itself—anomaly detection, schema drift monitoring → Version your data like code: Track lineage, changes, and rollback when needed Here's the amazing step-by-step guide curated by DQOps - Piotr Czarnas to deep dive in the fundamentals of Data Quality. Clean data isn’t a process — it’s a discipline. 💬 What's your biggest data quality challenge right now?

I saw this post thanks to Andreas Horn’s comment "Garbage in = Garbage out”. And don’t get me wrong, I agree and often use GIGO myself. GenAI can’t scale if employees don't trust its output. But...we all have to start somewhere. Whatever your data looks like right now, humans are already making decisions based on it and agents can do the same. I think of a support rep answering a customer question from an outdated article. One might give the wrong response, another who’s been around longer knows better and replies correctly. But the article stays wrong. The company doesn’t learn and the same issue repeats. In my ideal setup, data improves through daily use, not only through heavy-lift cleanup projects. When humans and AI work together, with every correction feeding back into the system, that’s when a company truly gets smarter. (And yes, a solution like Agentforce helps make that loop real 😉)

📊 From Raw to Reliable — Data Foundation Setup In today’s data-driven world, building a strong foundation isn’t just the first step — it’s the most critical one. A reliable data foundation enables organizations to scale seamlessly, ensure data integrity, and unlock real-time insights with confidence. At CloudFrame Technologies, we believe that the strength of every advanced data initiative lies in how well the foundation is structured. Our approach emphasizes standardization, governance, and scalability, ensuring that data remains consistent, trusted, and ready for innovation. 🌐 When the foundation is solid, everything built on top thrives — from analytics to AI. #DataFoundation #DataEngineering #BigData #CloudFrameTechnology #DataStrategy #Innovation #DigitalTransformation #DataIntegrity #CloudSolutions #EnterpriseData