How to turn data into a strategic asset for AI

You can’t scale AI unless you scale trust in the data behind it. As CIO highlights, developing a strong AI-ready data culture isn’t just about technology—it’s about mindset, ownership, and rigor. Here are five key practices organisations can adopt: Treat data as a product, not a byproduct — giving datasets clear ownership, defined lifecycles, and usability across teams.  Embed observability and traceability — know where data comes from, how it’s transformed, and how it’s used.  Bake governance into the foundation — policies around access, retention, classification, quality, etc., enforced not as an afterthought.  Make data literacy everyone’s job — across roles and levels, ensure people understand quality expectations, analytics basics, and how data impacts outcomes.  Integrate structured and unstructured data — bring together traditional systems data and the messy, rich sources like documents, images or emails so AI gets a fuller picture. #AIReady #DataCulture #DataGovernance #TrustInData #EnterpriseAI #DigitalTransformation https://lnkd.in/d6-kJn3G

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

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

AI can't deliver accurate results without the right foundation ➡️ a strong data culture. CIO Online highlights 5 actions you can take to create the trust and accessibility #AI needs to thrive—from treating #data as a true product to making governance and literacy part of everyday work. https://ow.ly/ltuX50WXOxc

Why do so many AI initiatives stall or fail to deliver value? Often, the problem isn't the model, but the data culture supporting it. A recent article from the publication CIO (Link in comments below) shares insights from four leaders on how to build a solid foundation. Here are the five key actions they recommend: 1. Treat data as a product, not a byproduct. This means assigning business owners, standardizing formats, and ensuring version control, just like any other product. 2. Make observability and traceability core to trust. You can't trust what you can't trace. It's essential to have complete visibility into data's origins, transformations, and uses. 3. Bake governance into the foundation. By getting top-down endorsement and automating policies from the beginning, governance becomes an enabler of innovation, not a bureaucratic hurdle. 4. Make data literacy everyone’s job. A truly data-driven culture requires everyone to have a baseline fluency in data concepts. As the article notes, it’s about more than just "plumbing." 5. Integrate structured and unstructured data. To get a complete picture and enhance AI precision, it's vital to combine data from systems of record with unstructured data like documents and emails. The article covers five key actions for an AI-ready data culture. Is there a sixth you would add to the list? Let me know your thoughts in the comments. #AIStrategy #DataCulture #DataGovernance #GenerativeAI #CIO

With 61% of organizations rethinking their data and analytics strategy due to artificial intelligence (AI), CDOs are asking, “Should data governance be two separate teams for data and AI, or should it be one team that evolves?” The answer was unanimous – everyone said it should be one team. But what does that look like in reality? In this white paper, you’ll learn about a strategic framework that will evolve your data governance team to support AI. https://lnkd.in/gZuMM_2v

For enterprises seeking to unlock the value of gen AI, it’s not enough to have data or even a model. What matters is how data is created, managed, shared, and trusted. (Story by Pat Brans) With thanks to Mike K., Dinesh Nirmal, Andrew Crisp, and Magan Naidoo for sharing your insights. https://lnkd.in/eQqK-y3Q

🚀 Data Leaders Digest | Issue #9 is Live ! This edition dives into some of the most critical shifts shaping the future of Data + AI as we head toward 2026: 🔟 10 Data + AI Trends to Watch in Fall 2025 — from conversational BI to the rise of context engineering. 🤖 How AI is reshaping developer teams — practical insights for engineering leaders. 🏢 Building Sustainable Enterprise AI Adoption — cultural strategies that drove 95% developer engagement. 🌐 When semantically connected data matters most — why standardized knowledge graphs are key. ⚙️ The Model Context Protocol (MCP) — a beginner’s guide to plug-and-play AI agents. 📊 Enterprise Data Governance Strategy — the essential elements in the GenAI era. One theme stands out: 👉 Winning with AI = Reliable Data + Scalable Systems + Strong Culture. 🔗 Dive into the full digest here: https://lnkd.in/gY8KNHu3 #DataLeadersDigest #AI #DataGovernance #EnterpriseAI #FutureOfWork

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

The #AI Bottleneck No One Really Talks About >> Real-Time #Data Agility >> https://buff.ly/cSn2WHH #tech #business #leadership #digital #innovation #disruption #digitaltransformation #strategy #AIstrategy #digitalstrategy #AgenticAI #genAI #LLM #CIO #CTO #CDO #CEO