CIOs face AI challenge: preparing enterprise data for AI success

AI is only as trustworthy as the data it learns from. We’re all racing to unlock value from AI—whether that’s through automation, faster insights, or mission acceleration. But here’s the reality: if you can’t trust your data, you can’t trust your AI. The new White House AI Action Plan makes it clear: to build responsible, effective, and trusted AI, we must invest in data quality, transparency, and governance. And that starts long before a model is trained. We’ve seen this mirrored in the DOD Data Strategy and its VAULTIS goals—prioritizing data that is Visible, Accessible, Understandable, Linked, Trusted, Interoperable, and Secure as a foundation for enabling secure and scalable AI across the mission. The latest release of erwin Data Intelligence 15 is built for this moment. With features like certified data models, automated discovery, and deep lineage visualization, it empowers organizations to: 🔍 Understand what data exists—and where it came from ✅ Validate the quality and ownership of critical datasets 🔒 Align with Zero Trust, CMMC, and Responsible AI principles 🤖 Enable AI that is explainable, repeatable, and grounded in trusted inputs Whether you're supporting mission planning, supply chain visibility, or digital health—trusted AI begins with trusted data. If your data isn't trustworthy, your AI won't be either. You can read more here from the erwin team: 🔗 https://lnkd.in/ePUeWKA2 #AI #ResponsibleAI #DataStrategy #TrustedData #erwin #DataIntelligence #VAULTIS #DoDDataStrategy #WhiteHouseAI #AIActionPlan #CMMC #ZeroTrust #DataGovernance #DataQuality #AIGovernance

❄️ Snowflake Cortex: Conversational AI for the Enterprise Snowflake’s new Cortex Agent moves beyond traditional BI and into the era of agentic AI—where natural language becomes the interface for enterprise data. 🔹 How it works → Plans & executes multi-step workflows (structured + unstructured data) Uses Cortex Analyst (SQL/BI) + Cortex Search (unstructured/documents) Generates charts, summaries, and SQL directly from user prompts Secure by design: role-based access, masking, auditability 🔹 For leaders → Accelerates decision-making with governed, explainable AI. 🔹 For developers → REST API with streaming responses (text, SQL, visualizations) makes it simple to embed into apps or chat UIs. This isn’t just incremental BI—it’s data intelligence operationalized. 📖 Full article here: https://lnkd.in/gkiRFx3K #Snowflake #Cortex #EnterpriseAI #DataStrategy #Analytics #AI

Is your data governance framework ready for GenAI? We’re moving from manual oversight to an era where GenAI will governance with: - Metadata that writes itself - Lineage that explains itself - Glossaries that stay alive - Smarter sensitive data controls I break down how GenAI could transform these core pillars of data governance in my new blog: https://lnkd.in/gCkNjtsC What’s are the governance pain points you are looking to solve with AI? #DataGovernance #GenAI #DataManagement #ArtificialIntelligence

Really great piece by Jonathan Reichental, Ph.D. on how relatively simple AI techniques can unlock real value from data governance before complexity and cost overwhelm the effort. Here's a few takeaways from what HEMOdata see happening in this space: - The increased emphasis on quick wins. Many companies postpone tasks like automating metadata creation, classification, lineage and so on because they feel too tedious but the truth is they provide IMMEDIATE value. - How building frameworks now (even if they're not perfect) pays dividend by creating visibility, reducing risk and more steadily enabling AI & analytics. - The idea that better governance isn’t just about compliance or risk-mitigation but about enabling innovation. When your data is organized, you move faster with more confidence. Where HEMOdata make a difference: - We help organizations leverage smart metadata management so data assets become discoverable with richer context & without manual overhead. - Our focus on data lineage & classification alongside leveraging our partner solutions make it easier to show where data came from, how it’s used and who owns or is accountable for it. Immediate visibility here often gives leadership the confidence to invest further. - Once the basics are in place the scalability of governance is realized and adding newer AI models, data sources or regulatory pressures becomes a lot less painful. Some common blockers we see: - IT / data teams may see governance differently than business units. It’s often necessary to make a clear business case (not just risk) to get buy-in from stakeholders. - Over-engineering can stall momentum so you want frameworks that evolve by keeping governance light but effective. - Ensuring tools & processes support continuous monitoring. Because governance isn’t a “one and done” thing. Trends, regulations & data volumes keep shifting. In short, if your organization is trying to unlock value from data, start with the simple AI-enabled governance moves. They offer low risk, fast benefits and lay the foundation for more advanced analytics and innovation. At HEMOdata, we’re here to help companies move from “messy, manual data” toward “trusted, usable data.” Excited to see how this space continues to evolve. https://lnkd.in/eZ4eKBvf #HEMOdata #datagovernance #AI #data

𝐀𝐈-𝐑𝐞𝐚𝐝𝐲 𝐃𝐚𝐭𝐚: 𝐀 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐀𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭. 𝐓𝐡𝐞 𝐅𝐮𝐞𝐥 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐅𝐫𝐢𝐜𝐭𝐢𝐨𝐧. Spending millions on AI but seeing project failure rates over 60%? The problem: AI? Naah, it is the foundation, the data. This piece attempts to expose the hidden architecture of constraints in traditional data pipelines that's quietly blocking AI from ever truly scaling. Community Author, Sagar Paul talks about why data infrastructure designed for humans is fundamentally misaligned with the needs of production AI. It’s a call to action for organisations to transition to AI-native data product architectures that provide the semantic clarity, quality, and reliability necessary for real AI ROI. 𝐖𝐡𝐚𝐭’𝐬 𝐈𝐧𝐬𝐢𝐝𝐞 𝐭𝐡𝐢𝐬 𝐚𝐫𝐭𝐢𝐜𝐥𝐞? ✅ 𝐓𝐡𝐞 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: Understand the high costs and wasted time (60% of a data scientist's time) spent on data issues. ✅ 𝐅𝐨𝐮𝐫 𝐂𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐁𝐚𝐫𝐫𝐢𝐞𝐫𝐬: Learn why semantic ambiguity, quality degradation, temporal misalignment, and format inconsistency are silently killing AI initiatives. ✅ 𝐈𝐧𝐥𝐢𝐧𝐞 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐢𝐬 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥: Discover how to embed data quality and business rules directly into your data flow, transforming data from a passive asset into a living product. ✅ 𝐅𝐫𝐨𝐦 𝐀𝐈-𝐑𝐞𝐚𝐝𝐲 𝐭𝐨 𝐀𝐜𝐭𝐢𝐨𝐧-𝐑𝐞𝐚𝐝𝐲: The importance of a distributed semantic layer that makes data not just compatible with AI, but capable of enabling autonomous decision-making. 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 𝐟𝐨𝐫 𝐘𝐨𝐮𝐫 𝐎𝐫𝐠𝐚𝐧𝐢𝐬𝐚𝐭𝐢𝐨𝐧: With traditional approaches, AI project delivery averages 6-8 months. This piece provides a clear blueprint to cut that down to weeks. It's a strategic guide for building an AI-native data foundation that provides sustained competitive advantages and ensures your investment in AI actually pays off. 👉 𝐑𝐞𝐚𝐝𝐲 𝐭𝐨 𝐭𝐚𝐤𝐞 𝐚 𝐩𝐚𝐮𝐬𝐞 𝐟𝐫𝐨𝐦 𝐀𝐈 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐬𝐭𝐚𝐫𝐭 𝐩𝐥𝐚𝐲𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐫𝐞𝐚𝐥 𝐫𝐞𝐬𝐮𝐥𝐭𝐬? 𝐃𝐢𝐯𝐞 𝐢𝐧𝐭𝐨 𝐭𝐡𝐢𝐬 𝐩𝐢𝐞𝐜𝐞 𝐚𝐧𝐝 𝐥𝐞𝐚𝐫𝐧 𝐡𝐨𝐰 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐝𝐚𝐭𝐚 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐟𝐮𝐭𝐮𝐫𝐞! ➡️ 𝐑𝐞𝐚𝐝 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞 𝐡𝐞𝐫𝐞: https://lnkd.in/dTc45bjC 🗣️ 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.

Storage Teams Must Work Closely with Data Teams - or Risk AI Chaos #AI changes what “access” means. It's not enough for storage teams to just serve up files. Data teams need context, classification, and governance to deliver the right data to AI. The gap between data storage and data engineering is where AI success (or failure) happens. Komprise helps bridge that gap so enterprises can turn unstructured data into AI-ready data services. In this Blocks and Files interview, Chris Mellor and Krishna Subramanianon discuss why storage and data teams need to talk. https://lnkd.in/gwSwRQ5g #datamanagement #unstructureddata

The future of AI success depends on more than just storing data... it’s about context, governance, and collaboration. Storage teams and data teams can no longer operate in silos. This interview highlights why bridging that gap is critical for turning unstructured data into AI-ready data services. A must-read for anyone driving #AI and #DataManagement strategies. #UnstructuredData #AI #DataTeams #Storage

💡 AI and open-source are transforming enterprise data platforms in 2025 and businesses that adapt early are gaining a real competitive edge. At PaperTrail, we see this trend reflected every day: companies need flexible, intelligent, and unified data solutions to unlock the full value of their information. Here are three key takeaways from the recent Forbes article, “AI And Open Source Redefine Enterprise Data Platforms In 2025”: ✅ Scalable and Adaptive Platforms: Modern enterprise data platforms leverage AI and open-source technology to scale quickly and adapt to evolving business needs, reducing reliance on rigid legacy systems. ✅ Cost Efficiency and Operational Agility: By integrating AI-driven automation and open-source tools, businesses can process and structure data more efficiently 📈, cutting operational costs 💰 while improving decision-making speed. ✅ Data as a Strategic Asset: Structured, searchable and enriched data becomes a cornerstone for innovation, powering analytics, AI agents, and better collaboration across departments 🤝. PaperTrail is at the forefront of this shift, transforming unstructured documents into a unified, actionable knowledge base. #papertrailgr #fromdatatoknowledge #AI #datamanagement https://lnkd.in/dCUBFR6V

This is how 7-Eleven turned AI against itself to solve a massive data problem. Imagine this. You have thousands of data tables, hundreds of columns each, and your AI tools are underperforming because they can't understand what your data actually means. Sound familiar? AI needs that important context. That's exactly where 7-Eleven found themselves. Their data documentation lived in Confluence, but their Databricks AI/BI tools (like Genie) needed metadata directly in the platform to work effectively. The result? Missed insights, confused AI responses, and untapped potential. The challenge was staggering: → Thousands of tables to migrate → Manual work would take months → High risk of human error → Opportunity cost of pulling data teams from higher-value work Their solution? Fight AI with AI. 7-Eleven built a sophisticated AI workflow using Mosaic and Llama 4 that: ✅ Automatically parsed Confluence documentation ✅ Intelligently matched it to Databricks tables and columns ✅ Migrated metadata with contextual understanding ✅ Reduced months of work to just days The results speak volumes: - 90% of tables now have proper documentation - AI/BI Genie transformed from "lightly used" to "everyday tool" - Natural language queries now work like magic - Dashboards provide more meaningful insights This isn't just about documentation—it's about unlocking the true potential of your AI investments. When your AI understands your data context, everything changes. 💭 Read the full case study: https://lnkd.in/egWudcjx

#AgenticAI Is Killing Data Lakes🤯 For the past decade, enterprises have been told that data lakes were the foundation of modern analytics. These massive repositories promised to store raw, unstructured, and structured data at scale, enabling future innovation. But in reality, many organizations discovered that data lakes became **data swamps**—unwieldy, siloed, and underutilized. Now, agentic AI is accelerating the decline of this model. ##Why Data Lakes Struggled? Data lakes were designed to be the "single source of truth," but they often failed because: - They required heavy governance and engineering to remain usable. - Most organizations lacked the talent to manage and extract value. - Business users couldn’t easily access or understand the data without specialized tools. - By the time insights were extracted, the information was often already stale. Simply put, data lakes became storage-first, insights-later systems—too slow for the pace of business. ##Enter Agentic AI Unlike traditional analytics and BI tools, **agentic AI systems** don’t just query pre-modeled datasets. They: - Integrate directly with distributed data sources, in real-time. - Use AI agents to orchestrate data retrieval, cleaning, summarization, and analysis automatically. - Deliver contextual insights at the point of decision, rather than requiring central storage. - Eliminate the need for centralized schemas by interpreting and reasoning across heterogeneous data on the fly. In this model, data doesn’t need to be dumped into a central lake—it stays where it is, and AI agents act as intelligent intermediaries. The allure of the data lake—a central pool of all enterprise data—becomes obsolete when AI can dynamically traverse the entire digital estate. From Lakes to Streams The metaphor is shifting. In a world of agentic AI, data behaves less like a stagnant lake and more like a **flowing stream**—constantly moving, contextual, and actionable. Instead of batch collection and storage, AI-driven ecosystems prioritize **continuous sensemaking**. The analytics value lies in interpretation and decision augmentation, not just storage capacity. The New Data Stack The emerging architecture looks something like this: - Operational systems remain the source of origin. - Event-driven pipelines stream updates. - AI agents handle semantic understanding, data prep, and analysis dynamically. - Natural language interfaces replace dashboards as the main consumption layer. Here, the central question is no longer "Where should we store it?" but "How quickly can AI help us act on it?" **actionable intelligence is primary.**