How Chief Data Officers can lead data provisioning

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

The AI regulatory landscape is fundamentally transforming how organizations think about data management, and it's creating seismic shifts in the data governance and catalog market. 𝐓𝐡𝐞 𝐍𝐞𝐰 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐕𝐞𝐧𝐝𝐨𝐫𝐬: 𝐅𝐫𝐨𝐦 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐭𝐨 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧: Data lineage and provenance tracking have evolved from "nice-to-have" catalog features to regulatory necessities. Vendors are rapidly repositioning their solutions around compliance-first narratives. 𝐀𝐈-𝐑𝐞𝐚𝐝𝐲 𝐃𝐚𝐭𝐚 𝐚𝐬 𝐚 𝐌𝐚𝐫𝐤𝐞𝐭 𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐲: The traditional data catalog is expanding into comprehensive AI governance platforms. Vendors are integrating bias detection, model transparency tools, and automated compliance reporting directly into their core offerings. 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐑𝐞𝐢𝐦𝐚𝐠𝐢𝐧𝐞𝐝: Data management vendors are shifting focus from simple discovery to end-to-end governance workflows that support explainable AI. The ability to trace data from source to AI outcome is becoming table stakes. 𝐌𝐚𝐫𝐤𝐞𝐭 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐧 𝐃𝐚𝐭𝐚 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞: 𝐂𝐨𝐧𝐬𝐨𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 𝐏𝐫𝐞𝐬𝐬𝐮𝐫𝐞: Organizations want fewer vendors managing their compliance story. This is driving M&A activity as companies seek integrated platforms rather than point solutions. 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Vendors are developing industry-specific compliance modules for financial services, healthcare, and other heavily regulated sectors where AI decisions carry high stakes. 𝐌𝐞𝐭𝐚𝐝𝐚𝐭𝐚 𝐄𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧: The definition of "good metadata" now includes AI model training data, algorithmic decision points, and bias testing results, expanding the traditional data catalog scope dramatically. 𝐓𝐡𝐞 𝐕𝐞𝐧𝐝𝐨𝐫 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐞 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲: Leading data management companies aren't just adding AI features, they're rebuilding their platforms around the concept of "trustworthy data supply chains" that can withstand regulatory scrutiny while enabling innovation. 𝐊𝐞𝐲 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧: As AI regulations tighten globally, which data management approach will emerge as the standard: comprehensive platforms or best-of-breed specialized tools? The companies that crack this code won't just survive the regulatory wave; they'll define the next generation of enterprise data architecture. Stijn (Stan) Christiaens Stephen Zisk Prabhat Mishra #datagovernance #datacatalog #AIgovernance #datamanagement #enterprisedata #regulatorycompliance #datalineage #dataquality

Unstructured data is shifting from a hidden liability to a growth driver. Industries from healthcare to finance are learning that unmanaged file shares waste budgets and block AI adoption. With up to 40% duplicate data and 80% rarely accessed, companies need visibility, governance, and lifecycle management to transform data sprawl into business advantage. https://lnkd.in/gByXPk9a

🚀 Reinventing Enterprise Data Quality — Not Just Nice, It’s Mission-Critical 💡 They say what gets measured gets managed. In the AI era, what gets trusted gets adopted. That’s why Anomalo’s unveiling the 6 Pillars of Data Quality feels less like a roadmap and more like a survival kit. Here’s the reality check: Blind spots in your data stack aren’t just nuisances — they’re kryptonite for AI. Traditional data “observability” is often just scratching the surface. You need depth. And in a world of rapidly evolving models, manual rules? They can’t catch what you don’t expect. Anomalo’s 6 Pillars: The foundation to trust your data everywhere it lives - Enterprise-Grade Security - Depth of Data Understanding - Comprehensive Data Coverage - Automated Anomaly Detection - Ease of Use - Customization & Control If your enterprise is still picking between “coverage vs. control” or “automation vs. ease of use,” you might already be behind. 🔍 Let’s design our data infrastructure not just to serve dashboards, but to fuel decisions, AI, and real impact — without compromise. https://lnkd.in/g2KKrScf

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

Why Every Business Needs a Strong Data Engineering Services Foundation: According to one industry report, most enterprise AI projects fail or stall—often because of poor data quality, lack of trust, or weak system ...

𝐈𝐬 𝐩𝐨𝐨𝐫 𝐝𝐚𝐭𝐚 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐤𝐢𝐥𝐥𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐩𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐬𝐚𝐭𝐢𝐨𝐧 𝐞𝐟𝐟𝐨𝐫𝐭𝐬? Every brand wants more targeted, personalised customer experiences. But here's what most are discovering the hard way: all that personalisation tech won't work without trustworthy data. The reality? Data governance isn't just an IT problem anymore. It's become a team sport that demands real collaboration between CMOs and CTOs. I call this the Customer Data chasm - the gap between Business teams' growing hunger for customer data and IT's need to ensure that data is clean and reliable. 𝐐𝐮𝐢𝐜𝐤 𝐠𝐮𝐭 𝐜𝐡𝐞𝐜𝐤 - 𝐖𝐡𝐞𝐫𝐞 𝐝𝐨𝐞𝐬 𝐲𝐨𝐮𝐫 𝐨𝐫𝐠𝐚𝐧𝐢𝐬𝐚𝐭𝐢𝐨𝐧 𝐬𝐭𝐚𝐧𝐝? Map yourself on these two dimensions: ▪️ Data complexity (volume, sources, teams involved) ▪️ Governance maturity (business stakeholder involvement) The 4 scenarios: ✅ Low complexity + Low governance = Safe Zone (light-touch works fine) ✅ High complexity + High governance = Safe Zone (heavy but controlled) ⚡ Low complexity + High governance = Optimization Zone (maybe over-engineered, but stable) 🚨 High complexity + Low governance = Danger Zone (conflicting definitions, untrusted data, delays) Not sure where you stand? Ask yourself: → Do multiple teams own different parts of the same customer data? → Is there a clear process when teams disagree on the "right" value for key attributes? → Can business users easily see what data is available and request more? If these questions are hard to answer, that's your signal. If you're serious about data-driven personalisation, governance isn't what slows you down - it's what helps you move faster with confidence. Sometimes you need to take two steps back to take a quantum leap forward. What's your organisation's biggest challenge with customer data governance? Credit: David Chan #DataGovernance #Personalisation #MarTech #CustomerExperience #DataStrategy

As we look ahead to 2026, data management is poised for a transformative shift. Fueled by AI innovation, rising regulatory demands, and the increasing need for real-time data, organizations must evolve their data strategies to stay competitive.

🚀 New Blog Alert on Medium 🚀 “Data Governance Simplified: A Beginner’s Guide for 2025” In today’s data-driven world, organizations struggle with data silos, poor quality, and compliance challenges. Without a strong data governance framework, scaling AI and analytics becomes nearly impossible. At Segmetriq Analytics LLP, we break down the essentials of Data Governance in simple terms: ✅ Metadata Management ✅ Data Quality Rules ✅ Data Lineage ✅ Business Glossary ✅ AI & Agentic AI Integration 👉 Read the full beginner-friendly guide here: 🔗 - https://lnkd.in/dRYq4quB 📊 Because in 2025, businesses that govern data well will lead the digital race. #DataGovernance #DataStrategy #AIinBusiness #AgenticAI #DigitalTransformation #DataQuality #MetadataManagement #SegmetriqAnalytics

💡 Data Governance is no longer optional — it’s the backbone of data-driven decisions. I recently wrote a blog on “Data Governance Simplified”, where I break down: ✔ Metadata Management ✔ Data Quality Rules ✔ Lineage & Glossary ✔ AI-driven Governance 👉 Here’s the link: https://lnkd.in/dARSY4ic Curious to know — in your organizations, which area do you find most challenging right now? #DataGovernance #DataQuality #CDO