AI is dominating boardroom agendas—but there’s a disconnect. While most enterprises are eager to build or adopt AI solutions, very few are truly ready. The technology is advancing fast, but the data foundations needed to support it are still shaky, especially when it comes to unstructured data.
At the heart of this challenge is a question that many teams can’t yet answer:
What data should we feed our AI models—and how do we get it there?
Diskover acts as a super connector—bridging fragmented, unstructured data environments and delivering enriched, curated datasets directly into Snowflake. Together, they make it easier to identify the right data, prepare it with context, and move it into AI pipelines with confidence.
Unstructured data: The hidden core of AI.
It’s time to rethink what unstructured data actually is—and why it matters so much for AI.
Unstructured data includes everything from PDFs, chat logs, and video files to microscope images, process logs, and design files. It doesn’t conform to rows, columns, or fixed schemas, making it difficult to manage, search, or use effectively. Yet it often holds a company’s most valuable intellectual property and insights.
- In life sciences, it’s lab notebook scans, clinical observations, and instrument output
- In manufacturing, it’s test logs, wafer inspection images, and simulation output
- In media, it’s raw footage, VFX assets, and archived creative files
These are the raw materials that can fuel AI—but only if they’re discoverable, enriched with context, and accessible through the platforms where AI actually happens.
The reality: Why AI enablement is so hard.
For most teams trying to build AI capabilities today, unstructured data is the biggest blind spot. The challenges are systemic and familiar:
1. “We don’t know what we have.”
Most enterprises are sitting on petabytes of unstructured data scattered across on-prem servers, legacy NAS systems, cloud buckets, and forgotten archives. Without a unified view, teams can’t even begin to assess what’s usable.
2. “We can’t find what matters.”
Even when data is located, it’s often miscategorized, duplicated, or buried in silos with no metadata. There’s no easy way to identify what’s high value versus what’s outdated, redundant, or irrelevant.
3. “We don’t know how to get it into Snowflake.”
Moving unstructured data into Snowflake or another analytics platform typically involves brittle custom scripts, manual tagging, or time-consuming staging processes that delay AI initiatives and create technical debt.
4. “We’re not confident in the quality of what we’re ingesting.”
Bad data in means bad data out. Feeding AI pipelines with irrelevant, unlabeled, or incomplete data can skew results, bias models, and waste compute. Without visibility and enrichment, even large datasets deliver limited value.
These challenges aren’t about theoretical strategy—they’re about real day-to-day blockers that keep teams from operationalizing AI.
The fix: Diskover + Snowflake make it seamless.
The partnership between Diskover and Snowflake is designed to address these challenges head-on—by closing the gap between raw data and AI-ready assets.
Step 1: Diskover finds and enriches your data.
Diskover acts as a global indexer across your entire storage landscape—on-prem, cloud, legacy, and hybrid. It scans file systems at scale, builds a searchable metadata catalog, and adds business context through intelligent tagging and enrichment.
No more guessing what’s out there. Diskover shows you:
- What data you have
- Where it lives
- How it’s being used (or not)
- Which datasets are most valuable for AI
It also lets you slice data by owner, project, age, access frequency, file type, and cost—giving you the clarity needed to make smart decisions fast.
Step 2: Curate and filter what matters.
Instead of dumping everything into Snowflake and hoping for the best, Diskover lets teams curate only what matters. You can define policies to identify:
- Cold data older than X months
- Duplicate files that can be ignored
- Files related to specific workflows, projects, or business units
- Specific types of files (e.g., large video files, microscopy images, system logs)
This curation step helps teams feed AI models with high-value, high-integrity data—while avoiding the clutter that can dilute performance and drive up costs.
Step 3: Move to Snowflake via Openflow.
Once data is curated and tagged, Diskover uses Snowflake Openflow to seamlessly move selected datasets into Snowflake’s environment—ready to be queried, joined, or fed into AI pipelines like Snowflake Cortex.
There’s no re-architecting or brittle ETL. And because the data arrives enriched with metadata and business context, it’s far more meaningful from the moment it lands.
“We’re seeing more customers adopt an AI-first data strategy, which depends on having access to all your data. Enterprises can’t unlock the full value of AI without knowing what unstructured data they have and how to use it. Our partnership with Diskover, in combination with Snowflake Openflow, makes that possible, acting as a super-connector to exabyte-scale unstructured data.”
— Harsha Kapre, Director, Snowflake Ventures
Industry examples: From theory to impact.
Let’s look at how this plays out in practice.
Life Sciences: Accelerating Drug Discovery
A research team working on new drug therapies needs to analyze years of experimental results, raw microscope images, and clinical notes. These datasets are stored in various formats across multiple environments.
With Diskover:
- They discover, tag, and organize the relevant assets—by experiment, date, or researcher
- Filter out irrelevant or low-quality data
- Seamlessly move curated datasets into Snowflake for model training, correlation analysis, and discovery
Semiconductor Manufacturing: Optimizing Chip Yield
A chipmaker wants to build predictive models to reduce manufacturing defects. The raw material? Process logs, inspection images, and test results—all unstructured and scattered across facilities.
With Diskover:
- They pinpoint the files that correlate to known defect patterns
- Enrich with metadata like product ID, location, and equipment
- Feed this clean, labeled data into Snowflake to power defect prediction models
Media & Entertainment: Powering Recommendation Engines
A media company wants to personalize its platform by analyzing viewer preferences across thousands of hours of archived content. But the video files are unlabeled and spread across aging storage.
With Diskover:
- They discover and tag content by show, actor, theme, or scene
- Remove outdated or duplicated assets
- Move curated metadata and assets into Snowflake for real-time recommendation and content repackaging
Getting started: From insight to ingestion.
Diskover will soon be available as a Connected App in the Snowflake Marketplace, allowing customers to:
- Purchase with Snowflake credits
- Integrate directly via Openflow
- Move from discovery to ingestion in just a few clicks
Want to see how it works with your data? Get in touch for a live demo or early access.
“Proud to support Diskover Data as they help companies uncover their most valuable data across legacy systems with a unified, searchable view.
Together with Snowflake’s easy, trusted, and connected platform, we’re helping customers seamlessly ingest critical data and build a strong, AI-ready foundation.”
— Sridhar Ramaswamy, CEO, Snowflake
Final thoughts: The future of AI starts with the right data.
AI isn’t just about algorithms or models. It’s about data. And not just any data—the right data.
With Diskover and Snowflake, teams now have a seamless way to:
- Discover what they have
- Curate what matters
- Enrich it with business context
- Ingest it directly into AI pipelines
- And do it all without rebuilding infrastructure or creating technical debt
Diskover and Snowflake give you a direct path from fragmented storage to AI-ready data—enriched, curated, and delivered where you need it.
Ready to harness the value of your unstructured data? Learn how Diskover can help you find, enrich, and deliver your data to power breakthrough AI use cases.