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What would it mean for your organization if 𝘦𝘷𝘦𝘳𝘺𝘰𝘯𝘦 could be their own data analyst? 🤔 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 𝗖𝗼𝗿𝘁𝗲𝘅 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 and other AI-powered BI assistants are now being used to generate SQL, summarize dashboards, and answer data questions. But here's the catch – they need a 𝘄𝗲𝗹𝗹-𝗯𝘂𝗶𝗹𝘁 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗺𝗼𝗱𝗲𝗹 to work effectively. 🧠 Why is a semantic model crucial? • It bridges the gap between business terms and technical data structures • Ensures accurate SQL generation from natural language inputs • Enables non-technical users to retrieve meaningful insights At Select Star, we're excited about the potential of Cortex Analyst to enable true self-service analytics. That's why we've put together a comprehensive guide to help you understand and implement this powerful tool. ❓ What is Snowflake Cortex Analyst and how does it work ⚙️ The critical role of semantic models in Cortex Analyst 🛠️ How to build effective semantic models for Cortex Analyst 🔗 Read more: https://lnkd.in/g-C_kRWY #SnowflakeCortex #CortexAnalyst #SelfServiceAnalytics #DataAnalytics

Join Snehal Karanjkar, Product Analytics Lead, as he shares his journey in implementing a data catalog. He’ll cover: 🏔️ Key challenges they were facing and why they needed a data catalog 📋 How they evaluated and selected a data catalog 📊 Impact and outcomes of the data catalog Whether you're implementing a data catalog, enhancing data governance, or building trust in your data, don't miss this opportunity to learn from real-world experiences. #DataCatalog #DataGovernance

Congratulations to our customer SADA, An Insight company on winning Google Cloud's 2025 Global Partner of the Year award for Google Workspace! 🏆 As SADA leads in cloud innovation, robust data governance is key. We're proud to partner with them with our automated data catalog, lineage tracking, and business glossary - ensuring they can confidently manage data while maintaining compliance. Kudos to the SADA team! Here's to empowering more businesses with cloud solutions backed by solid data governance. 🚀 #GoogleCloudNext #DataGovernance #CloudInnovation

How are Snowflake object tags enabling your data governance strategy? 🏷️ We see data teams use tags to enable key areas of governance: 🔍 Discoverability – Tags make it easier to find and understand data assets. 🛡️ Security – Dynamic masking and access policies based on tags protect sensitive data without impeding necessary access. 📊 Classification – Automated tagging streamlines data categorization for compliance, like GDPR or CCPA. 💰 Cost Management – Resource tagging enables better tracking and allocation of Snowflake usage to optimize expenses. Read on to learn how you can implement a robust tagging strategy as part of your metadata-driven governance: https://lnkd.in/eB7Gv-BW #Snowflake #DataGovernance #ObjectTagging #DataStrategy #Metadata

🤔 Why are data stewards crucial for effective data governance? Jeff Rosen, CEO of Qbiz Inc., recently shared insights on data stewards and why organizations need them. Data stewards help you: ✅ Bridge technical teams and business users ✅ Enhance data quality and accessibility ✅ Navigate complex regulatory frameworks like GDPR and CCPA ✅ Build trust with users and stakeholders Discover how these data ambassadors drive business value and learn best practices for implementing data stewardship in your organization. 🔗 Read more: https://lnkd.in/gquDjqKp #DataStewardship #DataGovernance #DataManagement

How are you tracking your data’s journey in Snowflake? ❄️ 🧭 Our latest guide on Snowflake Data Lineage breaks down the three key ways to access lineage information: 1️⃣ Direct querying in Snowflake 2️⃣ Using Snowflake’s Lineage API 3️⃣ Exploring through Snowsight Lineage (Snowflake’s lineage UI) Why is lineage so important? It's the map that helps you understand where data is coming from and how it is being used, ensuring transparency, compliance, and trust in your data. Read on for how to implement and automate data lineage for Snowflake: https://lnkd.in/gs6fPcy8 #DataLineage #Snowflake #DataGovernance #DataManagement

What is data stewardship and how can it drive impactful data governance outcomes? Jeff Rosen, CEO at Qbiz Inc., explains this and more in our latest Inner Join show. 🎧 Tune in for the full episode on how to accelerate data stewardship and governance in the age of AI: https://lnkd.in/gY4DCVJr

Data stewardship is a critical but often overlooked component of successful data governance and data catalog implementations. Without strong stewardship, even the best data catalogs risk becoming stale and underutilized. Jeff Rosen will share Qbiz Inc. experience helping leading organizations accelerate their data stewardship efforts and answer questions like: • How do you avoid turning your data catalog into a “metadata graveyard”? • What’s the right way to define the data steward role—and prevent overloading SMEs? • How can you align business and technical teams to curate metadata and build trust in data? • What are the common pitfalls in operationalizing stewardship, and how can you overcome them? If you’re rolling out a data catalog, building a data governance program, or simply looking to improve trust in your data—this session is for you.

🎉 FeaturedCustomers recognizes Select Star as a Rising ⭐ in their 2025 Data Governance Software Customer Success Report! Our modern data governance platform helps you manage and understand your data at scale – enabling AI, analytics, and self-service across the business. ✅ Catalog datasets ✅ Trace end-to-end lineage ✅ Build a shared business glossary and semantic layer Curious how Select Star can transform your data governance strategy? Discover how we're enabling teams to work confidently with trusted data: https://lnkd.in/gwVwmc63 #DataGovernance #AIReadyData #DataCatalog #DataLineage #BusinessGlossary #DataManagement

🧩 Output ≠ Outcome. Just because you've produced documentation, implemented tools, or hosted governance meetings (outputs) doesn't mean you've achieved better data quality, trust, or adoption (outcomes). In data stewardship and governance, outcomes rely more on alignment, trust, and collaboration than just ticking off deliverables. - Output: You’ve defined data owners, documented data definitions, or built dashboards. - Outcome: People actually use those definitions to make consistent decisions and trust the data. 👉 Outputs are necessary, but not sufficient for meaningful outcomes. So how do you drive data governance outcomes? 1. SME Alignment. - Subject Matter Experts must agree on definitions, metrics, and ownership. - Alignment across teams creates the foundation for trustworthy data. 2. Cultural Adoption > Tooling. - Even the best governance tools fail if people don’t believe in the process. - Build buy-in through collaboration, not mandates. 3. Define Shared Goals. - Governance should be aligned with business goals (e.g., “reduce revenue leakage due to misreported metrics”). - When governance feels relevant, people care more. 4. Communication Loops. - Regular check-ins between SMEs, data stewards, and consumers help reinforce governance. - It’s an ongoing conversation, not a one-time project. 5. Governance as Enablement, Not Control. - Frame governance as a way to accelerate trusted access and a support system, not a gatekeeping function. - The best way for end users to truly understand data is to use it in real scenarios—to ask questions, spot inconsistencies, and explore patterns. 6. Measure the Right Things. - Don’t just measure “number of governed datasets.” - Measure “reduction in data-related escalations,” “increase in self-service usage,” or “time saved in resolving data issues.” Anything I missed? This Friday, I'm having Jeff Rosen over on Inner Join to talk about this specific topic - accelerating data stewardship & data governance in the age of AI. Please join us for our deep dive conversation 🤠! #datagovernance #datastewardship #dataai #metadata