Embedding Data Governance into Data Delivery Process

Takeaways from Opening Keynote Fireside chat at Data Management Summit New York: How do you make it into the 5% Club: -Understanding the process in depth -Overcoming siloed data - integrating quality data and metadata - curation, labelling -Frequently small projects don't scale - lineage and end-user impact Data access doesn't = data available for analytics AI creates a "Kitchen Window" effect -All data issues become much more visible -Metadata management is the biggest piece of the challenge - semantic context - what is the source? Do we have permission to use it for this purpose?" -What is the goal? Agentic-AI - Context becomes key: -What is the Enterprise Memory? Governance: -Needs to be embedded in the process - then it becomes and enabler - contextual and dynamic #DMSNYC #AI #datamanagement #governance #metadata ##dataquality Stephanie Zhang Julia Bardmesser J.P. Morgan

🚀 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

Why does data feel so siloed? In most organizations, data starts in isolated pockets. A department collects information to improve its products or operations, but this data stays locked away. While valuable, this siloed approach misses the exponentially greater value that comes from aggregating large data sets across the entire enterprise. This is the power of Big Data. Every enterprise uses data, but the most successful ones leverage Big Data for a competitive advantage: * Media and Consumer Products use it to build predictive models for new products and services. * Manufacturing deploys it for predictive maintenance, anticipating failures before they happen. * Retail businesses use it to improve customer experiences and manage complex supply chains. * Financial organizations analyze it to find patterns that indicate potential fraud. But Big Data's most critical role today is supporting AI initiatives. Artificial intelligence and machine learning require vast sets of rich data to train and validate models. A lack of sufficient data is one of the most common reasons AI initiatives fail. To truly achieve AI goals, organizations must develop an enterprise-wide approach to collecting, managing, and delivering integrated data. Of course, collecting all this data isn't easy. Big Data is often characterized by the 3 Vs: * Volume: The sheer scale of data is massive. Big Data solutions must collect, aggregate, and deliver massive volumes of data that can reach hundreds of petabytes. * Velocity: Data-driven decisions require the latest information. Velocity is the speed at which data is received and refreshed, demanding solutions that can handle continuous streams of new data. * Variety: Data comes in many forms—from structured databases to unstructured sources like video, images, and sensor data—each presenting its own unique challenges. More recently, the data community has added more "Vs" like Veracity, Value, and Visibility to highlight the ongoing challenges of storing, managing, and serving this crucial resource. How are you moving beyond data silos to unlock the full potential of Big Data and AI? #SAFe6 #Bigdata #AI4All learn more here: https://lnkd.in/eJHawGSg

🚨 Most GenAI projects don’t fail because of weak models. They fail because the data function is brittle, reactive, and misaligned. Data Strategy OS — a 6-layer system that makes your data platform adaptive, agent-ready, and self-healing. 📊 Built for CIOs, CDOs, and data leaders tired of firefighting. 💡Modeling agents, DQ Validators, and Feedback Loops; this is how we scale GenAI with confidence. 📖 Read the full article here: https://lnkd.in/eT8cKF9j #DataStrategy #GenAI #CIO #AgenticAI #LLM #DataOps #ModernDataStack

Nearly 90% of data professionals say scaling and complexity are slowing down AI adoption, yet many executives still believe their organizations are ahead of the curve. The Actian State of Data Governance Maturity 2025 Report shows just how wide the gap is between how executives and data pros perceive their organization's data maturity, and why governance is the key to closing it.

INearly 90% of data professionals say scaling and complexity are slowing down AI adoption — yet many executives still believe their organizations are ahead of the curve. The CIO Dive article on Actian’s latest report highlights why stronger data governance can’t wait.

Joe Gits, CEO, Context Analytics, Inc. (CA) will give a keynote at Data Management Summit New York to discuss 'From risk to opportunity – minimizing hallucinations in financial data'. Find out: ❓How can data managers redefine data quality for the AI era to prevent hallucinations? ❓How can firms architect for factual integrity by building a Retrieval-Augmented Generation (RAG) architecture that grounds AI outputs in verified structured and unstructured data, ensuring every answer is traceable? ❓What is the playbook for prompt engineering, and how can data managers use it to craft precise queries that guide models toward factual answers and away from fabrication? Register: https://lnkd.in/etzPVkKv #DMSNYC #datamanagement #hallucinations #financialdata #AI #RAG #retrievalaugmentedarchitecture #unstructreddata #feedbackloop

Ever played detective with your own data? 🕵️♂️ Organizations often juggle inconsistent data sources, poor quality, and no clear governance. That messy mix hinders analytics, stalls AI projects, and trips up compliance. Why it matters: Without reliable data and a governance game plan, projects stall, risk regulatory breaches, and waste resources. 3 Actionable Insights: 1. Audit your data ecosystem: map sources, ownership, and flow. 2. Set up a governance framework: define roles, standards, and processes. 3. Leverage data quality tools: automate validation and cleansing. What's your biggest data governance challenge? Let's swap war stories! #DataGovernance #DataQuality #Analytics #AI #BusinessInsights

Nearly 90% of data professionals say scaling and complexity are slowing down AI adoption — yet many executives still believe their organizations are ahead of the curve. The CIO Dive article on Actian’s latest report highlights why stronger data governance can’t wait.

Nearly 90% of data professionals say scaling and complexity are slowing down AI adoption — yet many executives still believe their organizations are ahead of the curve. The CIO Dive article on Actian’s latest report highlights why stronger data governance can’t wait.