Data Management Summit: AI, Data Governance, and the Future of Data

Is Your Enterprise Data Strategy Ready for the Age of Intelligence? by Jerry Fisher, PhD, MBA In a recent Harvard Business Review article, Manish Sood (CEO of Reltio) and Venkat Venkatraman (Boston University) argue that companies must radically rethink their data strategies as AI and autonomous systems transform business operations. Their key message: data can no longer be treated as “storage.” To thrive in the Age of Intelligence, organizations must: ✅ Reframe data as a competitive advantage (not a filing cabinet). ✅ Invest in unified platforms and data literacy. ✅ Ensure seamless human + AI collaboration across the value chain. As Sood and Venkatraman note, “the most successful organizations will design their data strategies for speed, trust, and effective human-AI collaboration.” But here’s the challenge: even the best data platforms fail without the right human teams to implement them. That’s where the Innovation Strengths Preference Indicator® comes in.  The Innovation Strengths Preference Indicator® is the world’s only scientifically validated and patented (U.S. Patent No. D885,418) AI team assembly tool, developed by innovation pioneer Robert Rosenfeld and validated by the Center for Creative Leadership. Developed over the last 15 years and used by such organizations as Exxon, Raytheon, McDonnell Douglas, NASA, the Department of Defense, and so on. Using the Innovation Strengths Preference Indicator®, leaders can assemble AI and GenAI teams with the right balance of: • Visionaries  who drive breakthrough thinking and bold risk-taking. • Pragmatists  who refine processes, ensure governance, and manage risk. • Integrators  who bridge divides, connect silos, and accelerate adoption. Instead of relying on chance, the Innovation Strengths Preference Indicator® provides a data-driven method for building teams that minimize friction, shorten decision cycles, and unlock real-time velocity—the very shift from “data volume” to “data velocity” that HBR highlights. The lesson is clear: Data strategies demand intelligent data ecosystems, but they also demand intelligent human ecosystems. If data is the fuel of transformation, the Innovation Strengths Preference Indicator® is the blueprint for building engines that can run on it. CONTACT: for more info and to Take the Innovation Strengths Preference Indicator®.  Jerry Fisher, MBA, PhD Co-Founder & COO, Innovating Edge, Inc. Email: jfisher@innovatingedge.com Website: www.innovatingedge.com LinkedIn: Jerry Fisher https://lnkd.in/gc3__p9Y Link to article: https://lnkd.in/dB88Yckn. #AI #GenAI #DataStrategy #Leadership #Innovation #ISPIEXE #DigitalTransformation

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

Scott Buckles Keynote takeaways at Data Management Summit New York: Using US Open as an analytics & data case study -150 data points for every point scored in the match -Pales in comparison when applied to business - more unstructured than structured - only 1% of data is being leveraged by GenAI -Data Governance - ~ 20 years ago sampling was the model -today ALL Data needs to be captured & managed -GenAI & Agentic AI will transform data governance - e.g. Agentic AI Data Quality -consolidate tool sprawl - firms say they have three or more data mgt tools = more integrated -The Agentic AI Data Engineer -Meet data engineers with the tools they are familiar with -Adapt a hybrid data architecture DATA INTELLIGENCE -Quality, governance, risk assessment, accessibility in a single solution -bad data = bad AI -impacts of bad data are felt more immediately -Regulators don't want to hear excuses - don't blame the tool -Database + Data intelligence + Data integration + Data Security = Data Fabric - via Unified meadata & governance -Use fit-for-purpose tools How to ensure high quality and trusted data for AI: -Garbage in - garbage out on steroids #DMSNYC #datamanagement #dataquality #AI #agenticAI #hybriddataarchitecture #dataintelligence #datagovernance IBM

As a Chief Data Officer, I have seen many data teams try to endlessly aim for 100% data accuracy before releasing data products, while users could be making high quality decisions today, even with less than perfect data. The reality? Perfect data is a myth. What actually works: ✅ Release early with clear quality indicators ✅ Make users your quality partners ✅ Define quality in business terms, not technical ones Read the full playbook 👉 https://lnkd.in/gZ3GQW_T #DigitalTransformation, #Leadership, #DataDriven, #WorkforceTransformation #chiefdataofficer, #CDO, #datastrategy, #digitaltransformation, #CTO, #CIO, #AI, #data, #analytics, #DataEngineering

The role of the data steward is being redefined in the age of Agentic AI. Back in my Reltio days, I had the opportunity to walk in the shoes of data stewards through a day-in-the-life program, seeing firsthand the workflows involved in onboarding, reviewing, and cleaning data to ensure it was fit for use. In regulated industries, especially, these tasks are critical but also manual, error-prone, and labor-intensive. What was once a human-heavy responsibility, ensuring data quality, managing metadata, overseeing master data, and enforcing retention, can now be amplified through AI agents that act automatically, continuously, and at scale. This CDO Magazine piece highlights how embedding agentic automation into stewardship shifts us from reactive governance to proactive, real-time assurance: 👉 Data quality checks become autonomous 👉 Metadata is enriched and validated as data lands 👉 Retention and lifecycle policies are applied consistently, without waiting on human intervention For me, it signals a broader shift: trust in data can no longer be after-the-fact. It must be built-in, automated, and always-on because that’s what modern AI and business workflows demand. 📖 Worth a read: Digital Data Steward: Leveraging Agentic AI for Data Quality, Metadata, Master Data Management, and Data Retention by Maria C Villar Mike Alvarez and others. https://lnkd.in/g3ckVBth #agenticworkflows #dataobservability #dataquality #datamanagement

Stop Talking About "Data Assets." Start Delivering "Data Products”!!! We're drowning in data but starving for insight. The problem? We treat data like a raw material, not a finished product. This recent article from The Modern Data Company nails the solution through a data-as-a-product strategy. It means we stop delivering complex datasets and start delivering clean, trusted, and ready to use data products designed for a specific business outcome. The result is a true win-win. —> Business gets faster, more confident decisions. —> Tech shifts from reactive fire-drills to building high-impact, reusable assets. As leaders, this is our mandate. It’s how we transform data from a cost center into a core driver of business value and agility. #DataProducts #DataStrategy #AI #Analytics #Leadership https://lnkd.in/edk2vib5

The role of the Chief Data Officer has never been more critical — or more complex. For years, CDOs were measured on governance fundamentals: catalog coverage, data quality, and compliance reporting. These were the foundations of trust. But the rise of GenAI has shifted the terrain. Practically overnight, every employee has become a data consumer. The scale of demand is unlike anything we’ve seen before. And this is where old models break down. Ticket-based approvals that take weeks? They don’t work when thousands of employees — and AI agents — are requesting access at scale. Governance teams drown, business slows, and innovation stalls. That’s why data provisioning is emerging as the CDO’s new mandate. Provisioning is where governance meets business velocity. It’s about enabling the right people to get the right data, securely and quickly, without friction. And it’s measurable. 𝐍𝐞𝐰 𝐊𝐏𝐈𝐬 𝐚𝐫𝐞 𝐬𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐭𝐨 𝐝𝐞𝐟𝐢𝐧𝐞 𝐥𝐞𝐚𝐝𝐢𝐧𝐠 𝐂𝐃𝐎 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬: ⏱️ Average time from request to access (weeks → hours) 📈 Percentage of requests handled automatically through policy or AI 🔒 Share of sensitive requests requiring exception handling 👥 Proportion of the workforce actively provisioned with governed data These aren’t just operational stats — they’re indicators of how effectively an enterprise is using data to make decisions (I am spending time writing a data provisioning guide for Data Governance Book of Knowledge - coming soon! -- and I plan to get it integrated into conversational AI). At Immuta, we’ve reimagined how to make this possible. By meeting data consumers in the tools they already use, routing approvals intelligently, and leveraging AI to cut through bottlenecks, we help CDOs transform provisioning from a pain point into a competitive advantage. Because here’s the truth: the future of data doesn’t belong to those who store it, secure it, or even catalog it. 𝐓𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐝𝐚𝐭𝐚 𝐛𝐞𝐥𝐨𝐧𝐠𝐬 𝐭𝐨 𝐭𝐡𝐨𝐬𝐞 𝐰𝐡𝐨 𝐜𝐚𝐧 𝐩𝐫𝐨𝐯𝐢𝐬𝐢𝐨𝐧 𝐢𝐭. And it’s the CDO who sets that future in motion. Read more here --> https://lnkd.in/eSy2C6d9

Thoughts from our panel discussion on how to ensure high quality and trusted data for AI, at Data Management Summit New York: -Moving from reactive /rules based to pro-active -Apply AI across the DQ process - not necessarily replacing existing rues but augmenting - AI helps with proactive Managing Unstructured Data: -Growth & opportunity but the risks is higher -Historical tools are focused on structured data -Unstructured Quality KPIs - lineage is key -Ensure vendors meet formatting standards -we're still early in the Unstructured data quality journey -Raw document to JSON - enables applying traditional DQ rules Automating unstructured remediation at scale? -Agentic AI can flag - automate the fix - then test that the fix is valid - what's missing? First step - Structure the data - by transforming into JSON - then apply analytics -e.g. Take 50 records for manual assessment - apply AI and compare how it performed - Human in the Loop is always necessary Key org challenge for establishing the Ground truth: -Used to be 100% correct before data hit production - we're now probability based meaning 90% of the data is correct going into prod - creating greater oversight challenges so demands Probabilistic tools are great for probabilistic answers - not for 100% accuracy Using AI to streamline data management: -dealing with scale -identifying missing / incomplete data -detecting issues earlier in the pipeline A governance framework can fail if everyone interprets and executes differently - essentially creating multiple frameworks If governance slows you down, it's a cost center, if it's keeping you safe, it's an asset - Does a speed limit slow you down or keep you safe? Where AI should NOT be used: -Where bias in data -Don't use AI where you need a believable answer -Critical decision making Think of it as an excellent assistant Implementing new DQ monitoring and observability tools tops Priority actions Key takeaways: -Culture -Trust framework -Governance model - we have new tools but the good data management skillset is probably more important than ever - we can do it at scale and faster #DMSNYC #datamanagement #data #AI #GenAI #LLMs #dataquality #governance #unstructureddata BNY SMBC Group Datactics Informatica Context Analytics, Inc. (CA) Brian Greenberg Manal Alimari Eugene Coakley, OLY Amy Horowitz Joe Gits, CFA

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 ...

🚀 The Data Universe: More than just storage & dashboards When we talk about data, it’s not one thing—it’s an interconnected ecosystem that fuels business transformation. Here are the core pillars every organization needs to think about 👇 🔐 Data Governance – Policies, security, compliance & stewardship ensuring trust and quality. 🌊 Data Lake – The foundation to store raw, unstructured, and structured data at scale. 🏛 Data Warehouse – Structured, curated data designed for analytics and reporting. 📊 Data Analytics – Turning data into insights with BI, dashboards, and visualization. 🧪 Data Science – Advanced modeling, AI/ML, and experimentation to unlock hidden patterns. ⚙️ Data Engineering – Pipelines, ETL/ELT, orchestration, and observability to make data flow. 📈 Data Strategy – Aligning data initiatives with business outcomes. 🤝 Data Culture – Empowering teams to make decisions with data at every level. The real magic? ✨ When these layers are designed together as a system, they create a robust, future-proof data platform—capable of not just storing and reporting, but predicting, optimizing, and innovating. 💡 In today’s world, data is not the byproduct of operations—it is the product itself. 👉 Curious: Which part of this data ecosystem is your organization investing in most right now—Governance, Analytics, or AI? #Data #DataAnalytics #DataScience #BigData #DataEngineering #DataStrategy #DataWarehouse #DataLake #DataGovernance #AI #MachineLearning #BusinessIntelligence #DigitalTransformation