The data landscape is evolving rapidly. What was once a race to centralize data in cloud warehouses has now become a competition for intelligence, automation, and real-time decision-making. At two recent Snowflake events—the SPN Connect Day (Berlin, Sept 30) and the Snowflake World Tour (Berlin, Oct 1)—our colleagues Dennis Stolp and Fabian Stadler heard especially one message: Snowflake is no longer just a data warehouse; it’s an integrated AI and analytics platform.
For businesses in healthcare, life sciences, media, and supply chain logistics, this shift isn’t just exciting, it’s transformative, but requires careful evaluation of maturity and governance. With new capabilities like Snowflake Intelligence, AISQL, and Cortex, the question is less about hype and more about measurable value: how can organizations leverage these innovations responsibly today?
In this recap, we’ll break down the key announcements, real-world use cases, and strategic insights from both events—with a sharp focus on what matters for enterprises, data teams, and decision-makers.
Snowflake’s AI-First Strategy: From Data Storage to Intelligent Automation
The Big Shift: Snowflake as AI Data Cloud, Not Just a Data Warehouse
Originally positioned as a “Data Sharehouse”, Snowflake built its reputation as a scalable, cloud-based warehouse with powerful data-sharing capabilities.
In 2025, however, the roadmap positions it as a unified AI and data platform, integrating:
- Natural language querying (Snowflake Intelligence)
- AI-augmented SQL (AISQL operators like AI_FILTER, smart JOINs)
- Autonomous data agents (for automation, governance, and workflows)
- End-to-end ML lifecycle management (Cortex, LLM fine-tuning, Data Science Agent)
At the SPN Connect Day, Snowflake’s Germany GTM strategy emphasized three pillars:
- AI-driven data democratization (enabling non-technical users)
- Industry-specific solutions (healthcare, life sciences, media)
- Partner-first ecosystem growth (with support for dbt, Dataiku, Coalesce or Microsoft gaining traction)
Snowflake Intelligence: A Closer Look for Business User
A live demo of Snowflake Intelligence (currently in public preview) was the highlight at SPN Connect. Snowflake advertised it as looking like ChatGPT with the ability to work directly on your data, an impressive concept whose real value will depend on data quality, governance, and enterprise integration.
But there is more to it than just a fancy chatbot:
| Feature |
What It Does |
Real-World Impact |
| Natural Language Querying |
Users ask questions in plain English (e.g., "Show me Q3 sales trends for Product X") without SQL. |
Data democratization—business users self-serve insights. |
| Data Context Awareness |
Provided insights come from accessible data sources under consideration of metrics and semantic metadata. |
No more data searching—analysts won’t have to search for the right data manually. |
| Trustworthy Results |
Shows if used code is verified by Data Engineers to provide results that are deterministic and can be trusted. |
Provides a trustworthy ecosystem—non-deterministic or erroneous output of LLMs is mitigated. |
| Unified Structured & Unstructured Data |
Processes tables, documents, audio, and more in a single query. |
Eliminates silos—no more switching between databases and document stores. |
| Governance & Security |
Inherits Snowflake’s role-based access control (RBAC), ensuring only authorized data is exposed. |
Compliance-friendly AI—critical for regulated industries like pharma. |
Industry Deep Dives: Where Snowflake’s AI Delivers Impact
Snowflake's AI capabilities demonstrate their value especially in environments where data quality, traceability, and regulatory compliance are critical. A closer look at regulated industries shows how technology can be applied both responsibly and effectively.
Healthcare & Life Sciences: From Compliance to Cure Discovery
Why it matters in 2025:
- One of the fastest-growing verticals (alongside media/entertainment).
- Strict regulatory demands (GxP, HIPAA, GDPR) require auditable, secure data flows.
- AI-driven drug discovery and personalized medicine need scalable, unified data platforms.
Real-World Use Cases from the Snowflake World Tour
- Merck’s GxP-Compliant Data Pipeline (with Infomotion)
- Challenge: R&D generates large amounts of structured (e.g. clinical trials) and unstructured (e.g. research papers, regulatory submissions) data—but integrating it and making it available to AI systems on a scale while maintaining GxP compliance is complex.
- Solution:
- Direct data sharing from Snowflake Marketplace to avoid ETL bottlenecks.
- 4 isolated Snowflake accounts (dev, test, validation, prod) for reproducible validation—onboarding Snowflake for GxP use-cases was claimed to have been fairly easy.
- Leveraging Snowflake’s scalability, data governance, and self-service capabilities.
- Result: Faster regulatory submissions and reduced validation overhead.
- Boehringer Ingelheim’s AI-Powered Data Harmonization (Cortex in Action)
- Challenge: Global pharma companies deal with multilingual documents, inconsistent terminology, and PII risks.
- Solution:
- Cortex LLMs to standardize terms (e.g., translating across several languages into a single ontology).
- AI-driven PII redaction (summarizing documents while removing sensitive data).
- Cost optimization—using LLMs only for master data creation, not bulk processing.
- Result: Harmonization and standardization of data globally for advanced analytics.
Media & Entertainment: From Analytics to AI-Generated Content
Why it’s relevant:
- Streaming, gaming, and ad-tech generate massive unstructured data (video metadata, user interactions).
- Personalization at scale requires real-time analytics + AI.
Real-World Example: REWE International’s Data Democratization (jö Bonus Club)
- Challenge: Selecting a suitable and highly performing target group for CRM initiatives often involves time-consuming, manual coordination across multiple teams.
- Solution:
- Snowpark + Streamlit for a no-code CRM application.
- dbt ensures data lineage & governance through standardized data products.
- Real-time feedback loops (e.g., adjusting parameters with seamless re-calculation).
- Result: Simplified workflow with reduced turnaround times, data governance, and scalable, self-service capabilities.
Key Insight for HMS & its Partners
- The adaptation of our partner dbtLabs is growing fast through many projects with leaders trusting in strong governance and simplified transformation.
- Other products like Coalesce or Datavault Builder emerge as low-code alternatives for pharma data platforms.
- Application hosting on Snowflake (e.g. Streamlit) improves data collaboration for self-service users.
The Partner Ecosystem: Who’s Winning with Snowflake in 2025?
Snowflake’s partner-first strategy was a recurring theme. In this regard, we had many interesting talks with our partners and other Snowflake technology partners. Here are some examples that stood out to us:
| Tool/Partner |
Use Case |
Why It Matters |
| dbtLabs + Snowpark |
Data transformation & governance |
Daiichi Sankyo uses a data product registry with change management and automates PR reviews via LLM-assisted code checks and continuous validation. |
| Dataiku |
AI-driven analytics & ML workflows |
Enables teams to rapidly prototype and deploy predictive models without heavy coding; makes it easy to connect, cleanse, and prepare data for AI workloads. |
| Coalesce |
Low-code data modelling & cataloguing |
RSG Group built a full data platform with ~5 people—showing scalability and efficiency for lean teams. |
| dltHub |
Open-source data loading |
A lightweight, Python-native alternative to big ETL players like Fivetran, reducing cost and complexity for data ingestion. |
What’s Next: Three Considerations for Enterprises
- Assess Snowflake’s AI capabilities for Your Use Cases.
- If you’re in healthcare/life sciences: Test AISQL for data harmonization and Cortex for document processing.
- If you’re in media/entertainment: Explore natural language analytics for content performance and app hosting with Streamlit for lightweight apps next to your data.
- If you’re in supply chain: Pilot AI agents for automated inventory alerts.
- Evaluate partner tools for accelerated, yet governed, deployments.
- Need strict governance and transformation? → dbt
- Need easy data preparation + build GenAI apps? → Dataiku
- Need low-code? → Coalesce
- Need cost-effective ETL? → dltHub
- Prepare for the AISQL Shift.
- Train data teams on AI capabilities, the requirements and possible impact on your existing data platforms.
- Audit your data model—unstructured data (PDFs, audio, videos) will soon be queryable alongside tables.
Final Thought: From Data to Intelligent Action
The 2025 Snowflake World Tour and SPN Connect made one thing clear: Snowflake’s platform continues to evolve from storage to intelligent automation.
For enterprises — and for HMS and its partners — the potential is significant:
- Healthcare and life sciences can accelerate drug discovery through AI-augmented data.
- Media organizations can personalize content at scale without compromising governance.
- Supply chain leaders can turn operational data into self-optimizing networks.
The question is no longer whether to adopt these tools, but how to integrate them responsibly — and at the right pace for your business.
At HMS, we focus on that balance: translating technological potential into validated, compliant, and future-ready architectures that create measurable value from data and AI.
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