BeyondBlue Consulting’s Post

AI may grab headlines, but without a strong data culture, even the best models fall short. Leaders from DHL, IBM, Dun & Bradstreet, and the UN World Food Program agree: success comes from treating data as a product, building trust through observability, embedding governance, and raising data literacy across the enterprise. These actions turn data into a strategic asset, and make AI adoption sustainable. For CIOs, the real question isn’t “Do we have enough data?” but “Is our culture ready for AI?” #CIO #AI #Data #Leadership

Great CIO.com article on building an AI-ready data culture: treat data as a product, bake in governance, make observability and traceability core, push data literacy, and unify structured with unstructured sources. From my lens, success depends not just on tech, but on creating ownership, trust, and everyday habits across the organization. How is your org preparing its data culture for AI? Let's discuss! #DataStrategy #AI #DigitalTransformation #DataCulture #Leadership https://lnkd.in/gcjuay7G

𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗝𝗼𝘂𝗿𝗻𝗲𝘆: 𝟮𝟬 𝗬𝗲𝗮𝗿𝘀 𝘁𝗼 𝗔𝗜: Two decades ago, data was mostly about storage and access. Warehouses were rigid, ETL pipelines were brittle, and reports often arrived weeks after decisions were already made. Fast forward to today, and AI models are being trained on petabytes of data in near real-time. The journey has been fascinating 🤔 ✅ 𝗪𝗵𝗮𝘁 𝗵𝗮𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗱: 👉 Shift from batch ETL to streaming and event-driven architectures 👉 Cloud-native data lakes and lakehouses unlocking scale and agility 👉 Democratization of analytics with self-serve BI and visualization tools 👉 ML/AI moving from research to production, embedded in everyday workflows 👉 Rise of governance, lineage, and observability as first-class citizens ❌ 𝗪𝗵𝗮𝘁 𝗵𝗮𝘀 𝗳𝗮𝗶𝗹𝗲𝗱 𝗼𝗿 𝗻𝗲𝗲𝗱𝘀 𝗰𝗼𝗿𝗿𝗲𝗰𝘁𝗶𝗼𝗻: 👎 Poor data quality continues to derail AI initiatives 👎 High compute and storage costs create inequality in access to AI 👎 Ethical frameworks and bias detection lag behind adoption speed 👎 Data silos still exist... too much fragmentation across tools and teams 👎 Many organizations still treat data as an IT problem, and not a business enabler 𝗧𝗵𝗲 𝗻𝗲𝘅𝘁 𝟮𝟬 𝘆𝗲𝗮𝗿𝘀? The winners will be those who treat data not just as fuel for AI, but as a product i.e. 🔹 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝗮𝗯𝗹𝗲: Data should be easy to find, catalogued, and accessible. 🔹 𝗔𝗰𝗰𝘂𝗿𝗮𝘁𝗲: High-quality, reliable, and free from errors or inconsistencies. 🔹 𝗧𝗿𝘂𝘀𝘁𝗲𝗱: Governed, secure, and compliant enabling confidence in its use. 🔹 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲: Continuously improved, scalable, and future-ready. AI doesn’t forgive bad data, and the cracks in today’s foundations will only widen unless addressed. What do you think... are we building the right data foundations for the AI-first era? #Data #AI #Analytics #MachineLearning #Leadership

It is almost impossible to implement good AI-enable processes without the data layer to support it. It was always important for me to make sure the our data was: Complete, Available, and Trustworthy. This article expands on that hierarchy of maturity to add: Insightful, Decisive, and Autonomous (Self Actualizing). That last aspect was the AI-oriented layer of data. It will feel urgent to get to the top level of maturity immediately, as we are all apparently trying to use, to implement, or to perfect the AI within the workplace. But the data journey is not clean and easy, nor is it skippable. It takes work on the definitions, the governance, the architectures, and the usability in order to scale this data maturity. In order to be AI-ready, we need to tackle the data foundations AI will be built upon. Check out the original article: https://lnkd.in/ggaKiUA9

This CIO Online article by Pat Brans concisely summarizes the key ingredients to unlock AI's full potential (all are also key to CBRE's AI strategic pillars). It starts with a focus on data. 1. Treat data as a product 2. Build trust through observability  3. Bake in governance  4. Make data literacy universal  5. Unify structured + unstructured data to deliver richer insights 🔗 cio.com/article/4049233 #AI #DataCulture #DigitalTransformation

As AI adoption accelerates, one truth becomes clear: your AI is only as good as your data foundation. Data silos - once seen as just an operational headache, are now a strategic blocker. Why? - AI models demand unified, high-quality, contextualized data - Data engineering must evolve from ETL to AI-native pipelines - Analytics needs to shift from dashboards to decision intelligence In my career across data engineering, analytics, and business operations, I’ve seen how silos don’t just fragment data - they fragment decision-making. As mentioned in earlier posts as well - The future is moving toward semantic layers, shared data models, and AI-augmented architectures that connect engineering and analytics seamlessly. - AI will amplify both the strengths and the weaknesses of your data stack. Breaking silos isn’t just an IT goal, - it’s a business differentiator. How is your organization addressing data silos as AI becomes central to strategy?

🚀 Data Leaders Digest – Issue #7 is here! From AI-first platforms to Uber’s mind-blowing 150M reads/second cache, this edition dives into the strategies, frameworks, and innovations shaping the future of data. Inside this issue: 🔹 Building an AI-first platform strategy 🔹 Generative AI for data analytics 🔹 Data life cycle: stages, importance & best practices 🔹 Data & analytics governance – backbone of AI adoption 🔹 Data governance strategy 2025: modern frameworks 🔹 How Uber scales with stronger cache consistency guarantees Whether you’re building platforms, governing data, or scaling distributed systems, this digest brings you practical insights + real-world stories to stay ahead. 👉 Dive in & explore: https://lnkd.in/gcM_edAe #DataAnalytics #AI #DataGovernance #GenerativeAI #PlatformEngineering

Data Governance in the AI Era - Why It’s the Ultimate Game-Changer Ever feel like data is running wild and AI is just pouring gasoline on the fire? Well, here’s the amazing truth: strong governance tames that chaos and makes data and AI truly trustworthy and powerful! In 2025, data governance isn't just about compliance, it’s about building real-time, ethical, AI-aware pipelines. Think smart data catalogs, automated lineage tracking, policy enforcement as code, and data contracts that ensure access is controlled AND meaningful. Think data that’s clean, clear, and ready for AI. That’s next-level! I had a crisp “aha!” moment when I realized: building flashy AI dashboards is pointless if they’re fed by messy, unreliable data. That’s why investing in governance tools like real-time quality alerts or policy-embedded pipelines becomes the real secret sauce. Takeaways to Use Now: - Treat governance as part of your analytics strategy, not as an afterthought. - Think in terms of real-time enforcement, not static policies. - Use data contracts, lineage, and catalogs to transform trust, transparency, and speed. If you could automate just one part of data governance in your stack—what would it be? Data quality alerts? Smart lineage? Metadata access? Let's spark ideas! Want to explore more about how top companies are evolving governance? Check these out: - Gartner 2025 on AI & Data Strategy: rgues that without governance at the foundation, AI delivers little business value. https://lnkd.in/eResQ8ii - Top Governance Trends of 2025: spotlights how AI is automating everything from cataloging to compliance workflows. https://lnkd.in/eEVxBTfi - DVTA Predictions: calls for real-time governance as foundational for trustworthy AI systems. https://lnkd.in/eKBsQ7NZ #DataGovernance #AITrust #ResponsibleAI #DataStrategy #Metadata #AnalyticsOps

The Last Mile of Data Trust For years, the data world has obsessed over speed and scale.  - We built faster pipelines.  - We scaled warehouses to petabytes.  - We automated ingestion at the click of a button. And yet when it comes to the moment of decision, trust often falls apart. This is the last mile problem in data: The journey from a number on a screen to a decision in a human brain.Why does trust break here? -> Because definitions live in docs/sharepoint/company wiki pages, not in dashboards. -> Because lineage graphs exist, but its messy -> Because freshness checks are built, but aren’t surfaced when it matters. -> Because AI assistants “hallucinate” when they don’t know context. We’ve treated data like a technical asset to deliver not a human experience to design. The shift we need: -> From Delivery to Understanding        - Not just shipping tables, but embedding meaning into every metric. -> From Portals to In-Context Trust       - Surface lineage, ownership, and freshness in the moment of decision (inside dashboards, AI responses, slack thread, meet user where they are). -> From Static Docs to Interactive Dialogue        - A number should never be a dead end. It should be explorable, explainable, and trustworthy (How these tables are transformed, which SQLs were used). The future isn’t just “big data.” It’s believable data. Our job isn’t only to deliver data pipelines. It’s to deliver confidence in decisions. #Data #AI #Stratergy #Semantics

AI is only as strong as the data foundation beneath it. Without context, models may produce results that are difficult to explain or trust. That’s where federated knowledge graphs come in — they connect distributed data sources into a unified layer that supports explainability, compliance, and smarter automation.