Let’s be honest — most enterprises have a mountain of unstructured data sitting in their systems, untouched and underutilized. Call transcripts, PDFs, Slack messages, contracts, clinical notes, customer feedback forms — you name it. It’s not that this data isn’t valuable. The problem is, it’s historically been too messy, too inconsistent, or too expensive to wrangle.

That’s starting to change.

With generative AI (GenAI) and platforms like Snowflake, we’re finally at a point where unstructured data isn’t just a liability to store or secure — it’s an asset you can analyze, summarize, and act on at scale. And for early adopters who are already thinking about how to operationalize AI across the business, this is a serious unlock.

Let’s break down why this matters, what challenges still remain, and how teams are already putting this into practice using Snowflake.

Why Unstructured Data Is a Big Deal

Most organizations are drowning in data — but starving for insight.

According to IDC, over 80% of enterprise data is unstructured. That means it’s not living neatly in rows and columns. It’s stored as freeform text, images, audio, or complex documents that traditional analytics tools can’t easily digest.

Here’s what that looks like in practice:

• A healthcare company has thousands of handwritten clinical notes or PDFs from providers

• A retail brand has transcripts from customer service calls and emails

• A law firm has terabytes of case files, contracts, and historical precedent, most of it in Word or PDF

• A government agency has FOIA request data or scanned records with no metadata

This is high-context, high-value content — but until recently, there was no good way to process it without armies of analysts or expensive OCR and tagging projects.

Generative AI changes that. And Snowflake is becoming one of the most accessible places to make it happen.

What’s Changed: Snowflake + GenAI

With the introduction of Snowflake Cortex, along with growing support for large language models (LLMs) and vector search, Snowflake is now more than just a data warehouse. It’s a full-fledged platform for running AI workloads—on your own data.

Here’s why that matters:

• You can keep sensitive unstructured data inside Snowflake (no moving it to external tools or cloud services)

• You can use SQL or Python to call GenAI functions (e.g., summarize a document, extract sentiment, classify text)

• You can embed these insights into BI dashboards, downstream apps, or other workflows with minimal friction

Snowflake supports embedding models, document chunking, retrieval-augmented generation (RAG), and integration with vector databases like Snowflake’s native vector search or external tools like Pinecone.

You’re not just storing unstructured data anymore — you’re unlocking it.

Real-World Use Cases for Unstructured Data in Snowflake

Let’s ground this in some real-world examples. Here’s how organizations are already using Snowflake and GenAI to put their unstructured data to work:

1. Customer Support Analysis

Problem: A global e‑commerce company had over 1 million customer service emails and chat logs with no tagging or sentiment classification.

Solution: Using GenAI models embedded in Snowflake, they summarized complaints, tagged each message with urgency and category, and created a sentiment dashboard by region.

Outcome: Reduced manual tagging time by 90% and identified emerging issues (e.g., shipping delays) days before they escalated.

2. Healthcare Documentation Summarization

Problem: A health system had thousands of clinical PDF notes that needed summarization for EMR entries and billing support.

Solution: By extracting text from scanned notes and running them through a GenAI summarizer in Snowflake, they generated SOAP-style summaries and highlighted billing-relevant information.

Outcome: Saved physicians 5 – 10 minutes per patient, improved EMR quality, and reduced billing errors.

3. Legal Contract Risk Review

Problem: A law firm had hundreds of vendor contracts, NDAs, and service agreements that required clause-level review for risk and compliance checks.

Solution: Contracts were indexed in Snowflake with vector embeddings. A GenAI pipeline was used to flag missing or risky clauses, like indemnification gaps or unusual arbitration terms.

Outcome: Reduced time spent on first-pass review by 70% and created a searchable knowledge base of clause variants.

4. Internal Knowledge Retrieval

Problem: A SaaS company wanted a way for support agents to quickly find answers buried in past tickets, documentation, and release notes.

Solution: Content was chunked, embedded, and indexed in Snowflake. A GenAI-powered search layer let agents ask natural language questions and get synthesized answers, with citations to sources.

Outcome: Average ticket resolution time dropped by 35%, and agent ramp-up time was cut in half.

Challenges to Watch For

Of course, this isn’t magic. There are still hurdles:

• Data quality matters: If your PDFs are messy scans or include handwritten notes, you’ll need OCR and cleanup first.

• Prompt engineering still requires iteration: You won’t get the perfect summary or classification on the first try.

• Governance and privacy: Running GenAI on sensitive legal or medical data? You’ll need guardrails in place.

• Model cost and latency: If you’re processing thousands of documents a day, be sure to benchmark cost and performance.

Snowflake makes it easier to integrate these components, but the implementation still needs thoughtful design.

Getting Started: What to Do First

If you’re ready to explore GenAI for unstructured data in Snowflake, start here:

• Inventory your unstructured data: Where does it live? How much of it is actually being used today?

• Pick one high-value use case: Look for bottlenecks — manual summarization, long review cycles, or support ticket triage.

• Prototype inside Snowflake: Use Cortex functions, test embeddings, and evaluate GenAI output quality.

• Build incrementally: You don’t need a perfect RAG architecture from day one. Start small and layer in complexity.

Final Thought: Stop Letting Your Best Data Sit in the Dark

Your unstructured data isn’t a burden — it’s a resource. And with GenAI and Snowflake, you’ve now got the tools to explore it, organize it, and extract real value from it.

It won’t be perfect right away. But the early adopters who lean into this now — who connect the dots between raw content and business insight — are going to find advantages their competitors won’t see coming.

The opportunity’s sitting there. You just have to unlock it.