How to turn data catalogs into data hubs with intelligent provisioning

I recently read a fantastic data modernization guide from Snowflake's Federico Zoufaly, Matt Kvancz, and Josh Klahr and EY's Patrick Siconolfi and Margherita Braga designed to help improve data migration analysis and implementation. Key Takeaways: 1. Don’t treat migration as a lift-and-shift. Embed automation, quality, and business alignment. 2. Prioritize platforms that combine scalability with governance like Snowflake’s AI-Data Cloud. 3. Approach modernization as an incremental, strategic process that is aligned to value, not just infrastructure. 4. The right data foundation unlocks your path to AI and advanced analytics. Click the below to read the EY–Snowflake report: 

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

<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

↔️ Shift-Left vs Shift-Right in Data Governance: Who Owns Trust—and Who Builds It? ➡️ When Alation started the data catalog, it was all about engagement and adoption, shifting the work of data management to the right.  ⬅️ Then, with the modern data stack, data engineering teams pulled governance towards the left, moving quality controls, contracts, metadata, and validation upstream, closer to the source, with the premise that engineers could bake in trust from Day 1. ➡️ Today, LLMs and AI empower less-technical stewards & analysts to scale rapidly. Raluca Alexandru called it a Shift‑Right moment. ❓If you had to choose one, which one would you choose? 👈 Shift‑Left: Governance as code, embedded in pipelines, ensuring data quality before downstream risk. 👉Shift‑Right: Governance embedded in applications, revalidations at consumption, trust on demand—especially where AI-generated outputs are concerned. ❗ Why it matters: - Shift-Left gives you proactive guardrails, fewer data surprises, and more efficiency. - Shift-Right gives users embedded assurance when and where they need it—especially essential for LLM-driven workflows. Sanjeev Mohan and Guido De Simoni what are your thoughts on this one? #datagovernance #datatrust

🚦 𝘚𝘩𝘪𝘧𝘵-𝘓𝘦𝘧𝘵. 𝘚𝘩𝘪𝘧𝘵-𝘙𝘪𝘨𝘩𝘵. But… 𝘄𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗽𝗲𝗼𝗽𝗹𝗲? This is not the first article I have seen on the shift-left vs. shift-right debate in data governance (and it certainly won’t be the last). The framing is valuable: should governance live in pipelines (shift-left) or in applications (shift-right)? Both matter: proactive guardrails upstream and embedded assurance downstream. But let’s be honest: neither will succeed without the people side of governance. 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻, 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗰𝗵𝗮𝗻𝗴𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗮𝗿𝗲 𝘄𝗵𝗮𝘁 𝗺𝗮𝗸𝗲 𝘁𝗿𝘂𝘀𝘁 𝗿𝗲𝗮𝗹. Governance “as code” or “in the app” is powerful, yes, but without business stewards, analysts, and decision-makers seeing their role and being supported in it, it risks becoming just another technical layer. 👉 The real shift is not left or right. It is 𝘁𝗼𝘄𝗮𝗿𝗱𝘀 𝗽𝗲𝗼𝗽𝗹𝗲 𝗳𝗶𝗿𝘀𝘁. Because that is where trust is built, and where governance truly holds. So here is the question: 𝘪𝘧 𝘱𝘦𝘰𝘱𝘭𝘦 𝘢𝘳𝘦𝘯’𝘵 𝘪𝘯 𝘵𝘩𝘦 𝘱𝘪𝘤𝘵𝘶𝘳𝘦, 𝘪𝘴 𝘪𝘵 𝘳𝘦𝘢𝘭𝘭𝘺 𝘨𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 𝘢𝘵 𝘢𝘭𝘭?🤔 #DataGovernance #InformationGovernance #DataManagement #DataTrust #PeopleFirst #DataLeadership

Precisely has partnered with Opendatasoft to launch a new integrated data marketplace, making it easier for teams to access trusted, AI-ready data! The offering combines Opendatasoft’s data sharing platform with the Precisely Data Integrity Suite to improve data discovery, sharing, and use across the organization. 👉 Read the full announcement here: https://okt.to/2AJRlZ #DataIntegrity #AIReadyData #DataMarketplace

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.

Data living in lakes, warehouses, streams, apps need a governance layer.   Without the layer, it’s scattered, risky, and harder to trust.   That’s where 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐔𝐧𝐢𝐭𝐲 𝐂𝐚𝐭𝐚𝐥𝐨𝐠 comes in. It creates a single pane of control across your modern data platform—so teams can manage access, secure sensitive records, and keep compliance in check without slowing innovation.   In our latest blog, we explore: 🔹 Why healthcare and enterprise CIOs are turning to Unity Catalog 🔹 How a unified layer supports HIPAA and other compliance needs 🔹 What this shift means for data reliability and business agility   📖 Read the full article here: https://lnkd.in/dMuXgm_H Gayatri Akhani | Yash Thakkar | Srinivas Mothey | Jalindar Karande | Richa Gupta | Nakul Daf

New Carlsquare Sector Report – The Modern Data Software Landscape Our latest report explores: 🔹 The core components of modern data infrastructure 🔹 The impact of data growth & cloud adoption 🔹 The trends shaping tomorrow’s data-driven organizations In a nutshell: 🔹 90% of the world’s data was created in the last 2 years – most of it unstructured 🔹 60% of all corporate data is now cloud-based (up from 25% in 2015) 🔹 Cloud + AI adoption, alongside regulatory & security demands, are reshaping how organizations manage, process & protect data This report breaks down the essential building blocks of the modern data stack – covering data access, compliance, orchestration, observability, integration, storage, and analytics. We highlight how these layers interconnect to support secure, scalable, and intelligent data-driven organizations. Dive in to see why high-quality, well-governed data the foundation for effective use of AI. Download the full report via the link in the comments. Carlsquare has advised on landmark transactions across data integration, automation, and governance. For questions, feel free to reach out to: Susan Blanco John A. Cooper Jon Tingling Travis Mahoney, CFA David Lamb Rushi Amin Metin S. Akshay Krishnan #Data #Software #cloud #manda