Manouj Tahiliani

Starburst has always believed in providing customers with the freedom of choice. Nowhere is this more true than the emerging developments around interoperable compute among data platforms. Recently, I sat down with Snowflake’s Ryan C. Green to talk about the Open Semantic Interchange (OSI), an open, vendor-neutral standard for sharing semantic models across AI, BI, and analytics tools. OSI gives customers a common, open way to define and share business metrics. This helps them stay consistent across dashboards, notebooks, and machine learning models, no matter which tools they use. For Starburst, this furthers one of our core goals. Choice. Customers should be able to keep one trusted set of business definitions inside their Snowflake environment while still having the freedom to choose the tools that work best for them. OSI helps make that possible. It marks an important step in bringing Starburst’s engine to Snowflake-interoperable compute. The video from my chat with Ryan is coming soon 👀 In the meantime, you can get more details from our recent press release: https://lnkd.in/ev2XGB4u #OpenSemanticInterchange #OSI #Interoperability #AI #Data #Snowflake #Starburst

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Enterprise software sales isn’t about speed. It’s about alignment. When frontline users, IT, finance, and the C‑suite are aligned, deals move, commits are made. When they aren’t, no amount of pressure fixes it.

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We’re proud to announce that Starburst has joined the Open Semantic Interchange (OSI), an open-source initiative led by Snowflake and key industry partners to define a universal, vendor-neutral standard for semantic data exchange. The Open Semantic Interchange creates a common specification that helps organizations unify fragmented data definitions and maintain consistent metrics across dashboards, notebooks, and machine-learning models. By fostering interoperability, transparency, and customer choice, OSI is helping enterprises simplify data operations and accelerate AI innovation. “OSI represents the future of data exchange for AI, one built on standards, interoperability, and customer choice,” said our CEO & Co-Founder, Justin Borgman. “By establishing common standards, we’re enabling organizations to seamlessly maintain business context across platforms and tools.” Together with Snowflake and ecosystem partners, Starburst is shaping a more open, flexible future for the data and AI community. Learn more about the initiative: https://okt.to/6sIO1b #DataInteroperability #AI #OpenSource #Snowflake #DataAnalytics #SemanticModel #Trino #Iceberg

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Excited to share that MuleSoft Agent2Agent (A2A) Support is now available. As AI agents become more capable, the next big challenge is helping them work together. A2A Support helps solve that — enabling secure, scalable collaboration between agents across complex enterprise systems. This release is a big step toward building coordinated, multi-agent ecosystems with governance, visibility, and control at the core. More on how it works and what’s next:

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Informatica launched new customer-success offerings that leverage AI to accelerate business outcomes — the release notes cite “50% faster response times” for certain workflows + the company earned a fifth consecutive J.D. Power certification and the Technology Services Industry Association (TSIA) Star Award for CS excellence. TSIA Sendmarc appointed @DanLevinson as Customer Success Director for North America, signalling an investment in regional CS leadership and in strengthening post-sale support and compliance programs in their email-security business. DevOps.com A market study shows the global CS Platforms market is projected to reach around USD 6.2 billion by 2031, with a CAGR of ~12.3% from 2025. This speaks to growing investment in dedicated CS tooling across industries. openPR.de In research terrain: a new academic paper introduces “Agent-in-the-Loop” (AITL) frameworks to continuously improve LLM-based customer support systems (improving retrieval accuracy + generation quality + agent adoption). This suggests CS/Support orgs are shifting towards real-time, human-in-the-loop AI models rather than just rule-based bots. (arXiv) Also in research: deep-learning-based framework “ReviewSense” was published (Oct 18, 2025) that transforms customer reviews into actionable business recommendations — moving beyond sentiment analysis to prescriptive insights for CS teams. (arXiv) On the benchmark/report front: The TSIA “State of Customer Success 2025” report emphasises key trends such as: CS teams being under pressure to prove business value, tighter alignment with revenue, growing use of data to do so, and AI/automation becoming table-stakes. (tsia.com) On tooling & platforms: According to research, the global market for CS Platforms was valued at USD 1.52 billion in 2023 and is projected to reach ~USD 5.89 billion by 2030 (CAGR ~21.8%). This underscores how CS tech is scaling rapidly. (GrandView Medical Research, Inc.) 🔍 Key Takeaways for You The CS discipline is increasingly revenue-centric vs just “reduce churn” — vendors and orgs are positioning CS as a growth engine. AI/LLM and human-in-the-loop models are becoming more central to support/CS operations — not just chatbots, but systems that continuously refine themselves using real user/agent feedback. The tooling market is booming: whether you’re evaluating CS platforms or updating your stack, the investment region is large and growing. Talent and structure matter: as seen with Sendmarc’s hire, companies are investing in senior CS leadership and region-specific capabilities. Customer feedback is being elevated: frameworks like ReviewSense show CS teams want actionable insights from unstructured feedback, not just surface metrics.

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AI conversations have been dominated by AI models and GPUs, but they miss a critical component. To act accurately in enterprise environments, agents need operational data in real time.  Over half of the zettabyte scale data in enterprises is machine data. The challenge is finding the needles in this massive haystack so your agents can operate successfully.   Without unified, contextual, and trustworthy data, AI agents are blind, leading to costly errors and stalled workflows. Winning organizations will invest in the right operational data foundation to empower autonomous, reliable AI.  Is your organization ready to build that competitive moat?  Read my full take on VentureBeat ➡️ https://lnkd.in/gbdXCHz9 

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