Precisely and Opendatasoft launch integrated data marketplace

Precisely partners with Opendatasoft to launch a self-service data marketplace, giving enterprises fast, compliant access to AI-ready data. Read the Latest Full News - https://lnkd.in/dHHhdeH2 #DataMarketplace #EnterpriseAI #DataIntegrity #AIReadyData #SelfServiceData #DataManagement #CloudData #DataGovernance #Analytics #Automation #TechEdgeAI #TechEdge

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

🚀 Next up in our journey to make data more accessible for our customers: We’re excited to announce the release of the Cap IQ Company ID on our open data platform, dunl.org. Two years ago, we launched dunl within Commodity Insights.  Part of our broader Enterprise Data Organization (EDO) strategy is to share the best of each part of S&P across the company—and this reuse is a great example of that vision in action. Our approach is simple but powerful: 📘 Publish master and reference data under an industry-standard permissive license (Creative Commons) 🔗 Make it easier for customers to link their data with ours ⚡ Accelerate time to value through machine-readable metadata and improved interoperability Like the metadata release we made in July, this is all about enhancing machine readability and enabling faster, smarter data integration. Think of dunl as a small window into our enterprise knowledge graph—a graph that connects disparate datasets across S&P to unlock deeper insights. #OpenData #EnterpriseData #KnowledgeGraph #DataStrategy #MachineReadable #CapIQ #EDO #S&PGlobal #dunl Shout out to many who made this possible, including: Saugata Saha, Warren Breakstone, David Coluccio, Justine S Iverson, Greg Lawson, Laura Miller, Erica Robeen, Dharmendra Rana, Tamanna Madan, PRAMOD MOHAN BHAT, Szymon Klarman (Partners: you'll notice that the CC license we're using doesn't include commercial use, please get in touch if you have use cases for integration of this dataset) https://lnkd.in/gkUJtSxZ

Your company doesn't just need data. It needs a data moat. What's the difference? A data moat isn't about volume - it's a strategic collection of proprietary data that competitors CANNOT replicate. The magic happens in the flywheel: • Better data → better products • Better products → more users • More users → even more valuable data While tech giants have massive data lakes, smaller companies can build powerful moats by: - Focusing on niche, high-quality data collection - Creating proprietary acquisition channels - Building specialized ML models that extract more value from limited data - Prioritizing data quality over quantity The strongest moats combine multiple reinforcing elements rather than relying on a single data advantage. What data advantages have you engineered in your business? #DataStrategy #CompetitiveAdvantage #AIStrategy #BusinessMoats

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

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

The modern data stack is built on key layers—data access, compliance, orchestration, observability, integration, storage, and analytics. In this report, we unpack how these components fit together to enable secure, scalable, and intelligent data-driven organizations. Explore why high-quality, well-governed data is the foundation for unlocking the full potential of AI.

Is your data team service-driven or product-driven? Most companies start with 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗮𝘁𝗮 𝗧𝗲𝗮𝗺𝘀: reactive, ad-hoc, underfunded. Requests pile up. Tickets overflow. The business sees them as a cost center. Familiar? We've seen many teams evolving to the top left: 𝗛𝗶𝗴𝗵-𝗜𝗺𝗽𝗮𝗰𝘁 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗧𝗲𝗮𝗺𝘀 — still request-driven, but faster, more efficient, higher quality. Suddenly, the business sees real value. 💡 But the real leap? 𝗙𝗿𝗼𝗺 𝘀𝗲𝗿𝘃𝗶𝗰𝗲-𝗱𝗿𝗶𝘃𝗲𝗻 𝘁𝗼 𝗽𝗿𝗼𝗱𝘂𝗰𝘁-𝗱𝗿𝗶𝘃𝗲𝗻. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁-𝗗𝗿𝗶𝘃𝗲𝗻 𝗗𝗮𝘁𝗮 𝗧𝗲𝗮𝗺𝘀 (top right) don’t just answer questions — they build scalable, reusable data products. Products that compound value over time, fueling decision-making, innovation, and growth. ⚠️ Beware the danger zone: 𝗢𝘃𝗲𝗿-𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗲𝗱 𝗗𝗮𝘁𝗮 𝗧𝗲𝗮𝗺𝘀 — sophisticated systems, zero adoption. Resources burned, no impact. 👉 The recommended route? Traditional → High-Impact Service → Product-Driven. Because at the end of the day, data teams aren’t here to serve “wants”, they’re here to serve business “needs.” So, which quadrant best describes your team right now? #DataStrategy #Astrafy #DataTeams #DataProducts #AIStack