Eckerson Group

Governance Modernization: A Journey, Not a Quick Fix Many organizations remain stuck in traditional data governance—rigid, process-heavy approaches that clash with today’s agile analytics and self-service demands. Yet, governance has never been more critical, especially in the age of AI, where trust and accountability are paramount. Modern data governance shifts the focus from controlling data to guiding how people work with data. It prioritizes collaboration, cultural alignment, and simplicity, ensuring governance becomes a service, not a barrier. Dave Wells’ framework outlines how to modernize governance practices for today’s challenges, offering a roadmap to bridge the gaps and create value. Read the full article to explore the framework: https://lnkd.in/gphcWGh #DataGovernance #Modernization #SelfService #GenAI

Is unstructured data really unstructured? Dave Wells says data that seems “unstructured” actually holds inherent structure—it just doesn’t always fit neatly into rows and columns. As formats like JSON, XML, and multimedia become standard, data modeling has adapted to keep pace, offering a fresh, holistic approach for today’s data landscape. Discover how modern data modeling meets today’s unique data challenges: https://lnkd.in/gHU67jvq #backtothebasics #datamodelling #UnstructuredIsStructured #datamanagement

Non-profits can't afford to skip a data strategy. Limited budgets and lean teams often make data an afterthought, yet without a clear strategy, organizations risk inefficiencies that undermine their mission. At Eckerson Group, we've seen how a well-planned data strategy can help non-profits make data-driven decisions, manage resources wisely, and ultimately fulfill their goals with greater precision. From fostering consensus to efficiently managing resources, a data strategy is essential to every non-profit’s success. Explore our latest article to learn why a data strategy is crucial for non-profits. https://lnkd.in/gYv-CXSe #DataStrategy #NonProfits #MissionDriven

Data Fabric and Data Mesh have sparked plenty of discussion in data management. Are they frameworks, products, or something else? And is it about choosing one or the other? In this insightful blog series from our partner Promethium, CEO Kaycee Lai offers a practical view that aligns with our perspective: rather than choosing, consider how these approaches can complement each other. By blending Data Fabric’s centralized access with Data Mesh’s decentralized ownership, organizations can build a flexible, future-ready data strategy that adapts to evolving demands. Read the full series here: https://lnkd.in/g8CsbvaV #DataFabric #DataMesh #UnifiedData #DataArchitecture #EckersonGroup #Promethium

Tired of the AI Hype? Discover What Really Works. At DATA Festival #online, you’ll move past buzzwords and dive into real, impactful applications of AI, data science, engineering, and governance. Hosted by BARC, this event gathers hundreds of professionals eager to explore strategies that drive measurable results. Join your peers for a day of practical insights, hands-on examples, and actionable discussions designed to help you leverage AI and data effectively in your own organization. ?https://lnkd.in/ezfVWkVb #AI #DataScience #MachineLearning #DataEngineering #DataGovernance #DATAfestival

The good news is that leaders don’t need to choose between Data Mesh and Data Fabric. By combining both approaches, companies can create a balanced strategy that blends decentralized data management with centralized governance—ensuring compliance and scalability. Read our latest topical newsletter by Jay Piscioneri to explore how these frameworks have evolved together to offer synergistic solutions for modern data challenges. #datastrategy #datafabric #datamesh #moderndatamanagement #dataarchitecture

Data mesh offers a flexible approach to tackling today’s data complexities by decentralizing ownership and leveraging automation. Here are the four pillars that encompass the organizational and technical changes it espouses: ?Domain Ownership: Domain teams own and manage their data products, ensuring relevance and accountability. ?Data as a Product: Data is treated as a product, complete with metadata, quality standards, and APIs for seamless access. ?Self-Serve Data Platform: Teams need autonomous tools for managing and accessing data products without creating new bottlenecks. ?Federated Computational Governance: A shared governance framework enforces standards across domains, leveraging automation to ensure compliance and quality. Dive deeper with our white paper: https://lnkd.in/evd6F7cj Want to learn more live? Join experts Jay Piscioneri and Wayne Eckerson on October 31, where they’ll discuss how these frameworks support modern data strategies: https://lnkd.in/e_KG8Sc4 #DataMesh #DataFabric #DataStrategy #DataArchitecture #DataManagement

Data Mesh vs. Data Fabric: Do You Really Need to Choose? Are data mesh and data fabric two sides of the same coin? Or can they be combined for ultimate flexibility? ?? In his latest article, Jay Piscioneri breaks down how these two frameworks can complement each other to help your organization strike the perfect balance between autonomy and control. Discover how blending decentralized data ownership with centralized governance could be the key to unlocking an adaptive, scalable data strategy: https://lnkd.in/eGx76Aby #DataMesh #DataFabric #DataStrategy #DataArchitecture #DataManagement

Think Only Race Cars Need High-Octane Fuel? AI Engines Do Too! AI and ML models also thrive on high-octane fuel—refined data. As Kevin Petrie explains, structured data remains crucial, with 61% of organizations relying on it to drive their AI/ML innovation. And it’s not just about the type; it’s about the timing as well. Over half of the respondents prefer real-time data delivery, with 27% favoring streaming to keep their AI/ML engines running smoothly. But raw data alone isn’t enough. Through data integration, these inputs are filtered, labeled, and engineered into refined data that’s truly fit for purpose, fueling AI/ML across its lifecycle. Learn more about how data integration transforms data into high-octane AI power.?https://lnkd.in/eJpww96J (Data sourced from a report by Shawn Rogers of BARC and Merv Adrian of IT Market Strategy) #AI #DataQuality #ML #DataScience #DataManagement

As organizations look to scale AI, they need more than just isolated tools. They require data platforms capable of integrating diverse sources, enabling real-time analytics, and supporting long-term storage. Whether it's a data lakehouse or another unified architecture, these platforms blend various storage solutions with advanced analytics to provide a cohesive foundation for AI. When enhanced with comprehensive analytics and business intelligence tools, these platforms form what we can call an intelligent data platform—one that enables real-time visualization, in-depth insights, and the scalability needed for enterprise-level AI. On October 24th, Wayne Eckerson will moderate a roundtable with industry experts to explore how these platforms can drive scalable AI. Join us to learn practical strategies for unifying storage and analytics in support of enterprise AI: https://lnkd.in/enDu5gi5 #AI #DataPlatforms #ML #DataLakehouse #DataArchitecture