How to Modernize Your Data with Snowflake and EY

EY and Snowflake have collaborated to produce this insightful report that explores how organizations can modernize their data landscapes, enabling scalability, resilience, and AI-powered insights for the future. Read more and download the full report today. #ShapeTheFutureWithConfidence

EY and Snowflake have collaborated to produce this insightful report that explores how organizations can modernize their data landscapes, enabling scalability, resilience, and AI-powered insights for the future. Read more and download the full report today. #ShapeTheFutureWithConfidence

Data catalogs are the foundation for discovery — the place where employees go to understand what data exists. But discovery alone isn’t enough. Without a way to quickly and securely provision access, users still end up waiting weeks, and governance teams drown in manual approvals. That’s why leading organizations are pairing catalogs with intelligent data provisioning — uniting discovery with automated, governed access. The result: catalogs evolve into true hubs for data products, where finding and using data is seamless, safe, and scalable. Read more → https://ow.ly/9hNf50WUzv8 #Immuta #DataProvisioning #DataAccess #Governance

<p>Check out our latest blog by Robert Gauthier on best practices for building a robust data ingestion pipeline for Observability Data! Learn how to architect scalable, resilient pipelines that ensure your observability data delivers real value-whether for real-time incident response or long-term analytics. This guide is packed with actionable tips, covering everything from data quality to distributed processing and security.</p> <p><br></p><p>Perfect for anyone using DX Operational Observability (DX O2) or exploring next-gen AIOps strategies!</p> <p><br></p><p>Dive into key insights like:</p> <ul><li>Ensuring high-fidelity data capture for accurate insights.</li><li>Best practices for handling massive data volumes efficiently.</li><li>Strategies to integrate, process, and secure observability data at scale.</li></ul> <p>Read the full blog for expert advice and step confidently into the future of IT operations.</p> <p><br></p><p>#DXO2 #Observability #AIOps #DataIngestion #DXOperationalObservability #Broadcom</p> https://dy.si/Fr5b5

Dave McCrory first introduced the concept of Data Gravity in 2010. He used the very intuitive and convincing analogy: data is similar to a planet, gaining mass when it grows and drawing applications, services, and additional data into its orbit. Over the years, this metaphor has become much more than a technical observation. It has turned into a strategic principle that shapes how organizations think about technology. As data accumulates, it begins to determine where applications are built, how systems interact, and which ecosystems companies inevitably become tied to. The implications for IT strategy are significant. The physical and regulatory location of data influences decisions about cloud adoption, on-premise investments, and hybrid or multi-cloud approaches. The cost and complexity of moving large datasets often lead to platform and vendor lock-in. Latency requirements drive applications to reside closer to the data they consume. And increasingly, laws around privacy and data residency add another dimension to this gravitational pull. In practice, data is no longer just a resource to be managed, it acts as the anchor point around which the rest of the technology landscape must orbit. Forward-looking organizations recognize this and design architectures that balance agility with the realities of immovable data, whether through distributed models, edge strategies, or careful governance frameworks. Understanding and planning for data gravity not only helps avoid technical bottlenecks, but also positions companies to turn data into a competitive advantage.

𝗖𝗮𝗻 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝗮 𝗱𝗮𝘁𝗮 𝗺𝗲𝘀𝗵 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗗𝗮𝘁𝗮+ 𝗔𝗜 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆? Some assume that once you decentralize ownership and give domains responsibility, a data mesh will simply work. The reality: without data observability, it’s nearly impossible to scale. Here’s why: ✅ 𝗧𝗿𝘂𝘀𝘁: If data products aren’t reliable, domains will quickly lose confidence in each other’s outputs. ✅ 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Observability provides the visibility needed for teams to take true ownership. ✅ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: A mesh multiplies complexity; observability keeps it manageable. ✅ 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Without automated monitoring, domains spend more time firefighting than innovating. With Data + AI observability in place, you can even assign each data product a Data Reliability Score, built from KPIs like freshness, completeness, accuracy, and pipeline health. This makes trust measurable, comparable, and actionable across the mesh. A data mesh is not just about architecture or org design. It’s about ensuring every data product can be trusted and that requires observability at its core. 💬 What’s your take: is data observability optional or essential for a successful data mesh? #DataObservability #AIObservability #DataMesh #DataReliability #DataEngineering #DataOps

EY-Parthenon uses Databricks to reduce insight delivery from weeks to hours, boosting efficiency by 30–50% and powering smarter M&A decisions at scale. 

Financial firms are tackling an explosion of complex data - from structured trading records to unstructured research reports. Scaling data lakes that 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗔𝗜 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 is now the critical next step. Is your organization facing these challenges? ⏳ Legacy systems slowing down under data growth 🐢 Lengthy analytics delaying insights 🔒 Managing compliance risks across complex data environments 🤯 Difficulty delivering trusted, unified data access at scale Discover how financial institutions can overcome these barriers with 𝗺𝗼𝗱𝘂𝗹𝗮𝗿, 𝗴𝗼𝘃𝗲𝗿𝗻𝗲𝗱, 𝗮𝗻𝗱 𝗺𝘂𝗹𝘁𝗶-𝗰𝗹𝗼𝘂𝗱 𝗱𝗮𝘁𝗮 𝗹𝗮𝗸𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀. Our latest guide, “𝗧𝗵𝗲 𝗕𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 𝗳𝗼𝗿 𝗔𝗜-𝗥𝗲𝗮𝗱𝘆 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲𝘀 𝗶𝗻 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗙𝗶𝗿𝗺𝘀,” offers: ✅ Real-world lessons and case examples ✅ A clear framework covering ingestion, storage, analytics, governance, and consumption ✅ Key trade-offs and how to navigate them ✅ A practical, step-by-step roadmap to build and mature your data lake ✅ A maturity model to benchmark your AI readiness progress 📥 Download the attached PDF to unlock actionable insights and accelerate your AI transformation journey. 💬 What’s the biggest challenge your data teams face at scale? Or which breakthrough moved your analytics forward? Let’s discuss in the comments! #DataLakes #AIinFinance #Fintech #CloudData #DataGovernance #MachineLearning #FinancialServices #BigData

It’s eye-opening to see how differently executives and data pros perceive their organization’s data maturity. The Actian State of Data Governance Maturity 2025 Report shows just how wide the gap can be — and why governance is the key to closing it.

It’s eye-opening to see how differently executives and data pros perceive their organization’s data maturity. The Actian State of Data Governance Maturity 2025 Report shows just how wide the gap can be — and why governance is the key to closing it.