5 minute read
You have unified your data in a lakehouse. You’ve invested in Databricks or Snowflake or Microsoft Fabric. But when a business user asks, "Which trials were impacted by Germany’s 2023 regulation?"… your system draws a blank. That’s because your data has structure, but no meaning. Let’s fix that.
Knowledge graphs turn your lakehouse into an explainable, AI-ready asset. In this post, you’ll learn how Datavid Rover adds semantics to Snowflake, Databricks, or Fabric or Apache Iceberg powering GenAI, auditability, and better decisions in just 4 weeks.
In a recent blog post, we explored how a semantic layer accelerates AI data readiness. The core argument is that most AI challenges don’t stem from a lack of data, but from a lack of connected meaning.
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This is especially true as you adopt lakehouse platforms like Microsoft Fabric, Databricks, and Snowflake. These tools are excellent for scalability, performance, and storage but often lack semantic depth.
For instance, your lakehouse can’t distinguish "viral load" from "product launch." It can’t understand that “CIPLA 50mg” is a regulated compound referenced in multiple documents. And when someone asks, “Which trials were affected by the regulatory change in Germany last year?”, your lakehouse can’t reason its way to an answer.
That’s where knowledge graphs come in.
Your lakehouse isn’t enough
Platforms like Microsoft Fabric, Databricks, and Snowflake solve big problems like query performance, unification of data types, and cost-effective scalability. But they weren’t designed for AI. They’re schema-driven and don’t handle meaning or context well.
So even if you’ve modernized your infrastructure, you're likely still unable to answer:
- Which suppliers carry regulatory risk in Europe?
- What clinical trial cohorts share patient attributes?
- Where are product complaints clustered geographically?
These questions require semantic understanding, not just fast querying. According to Gartner, by 2025, 80% of LLM value will rely on contextualized internal data. Your data lakehouse gives you reach but not reasoning.
Why you need a knowledge graph layer
A knowledge graph adds meaning to your existing data. Instead of replacing your lakehouse, it enhances it by connecting data points like people, terms, products, and documents with metadata that reflects how your organization actually functions.
Consider a pharmaceutical company managing data in Snowflake. It may store drug descriptions in CSV files, trial results as PDFs, adverse event logs in Excel, research summaries in Word documents, and regulatory responses in email. While these data types are valuable, they’re siloed and disconnected.
Imagine a knowledge graph linking each drug to its therapeutic class, brand name, and region. Trial results are connected to the compounds they tested. Documents are enriched with metadata, including authorship, version history, and entity mentions.
Suddenly, semantic search becomes possible. You could ask: “Show me all Phase II trials for NSAIDs with GI-related side effects.”
This transformation from raw data to connected knowledge enables:
- Semantic and cognitive search across structured and unstructured formats
- Explainability in LLM outputs
- Graph-based Retrieval-Augmented Generation (GraphRAG)
- Agentic AI workflows
- Transparent audit trails and compliance tracing
It transforms flat data into business-ready insight.
The urgency of semantics for GenAI
If you're piloting GenAI initiatives, you've probably noticed how easily models hallucinate or deliver generic results. That’s not a model flaw; it’s a data context issue.
Large language models (LLMs) need structured, contextual information to ground their outputs. Without it, their reasoning is unreliable.
That’s why IDC projects that 40% of enterprise AI models will use graph techniques by 2026. Unfortunately, many still treat knowledge graphs as a future priority. In reality, they are what makes GenAI deliver real business value today.
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How to architect it with Datavid Rover
Datavid Rover works alongside your existing lakehouse infrastructure. It connects to your systems, enriches the content semantically, and makes it available via APIs and semantic interfaces.
From Chaos to Context: Datavid Rover accelerates taking fragmented data to actionable knowledge
Start with ingestion. Rover connects to Microsoft Fabric, Snowflake, Databricks, and many more. It pulls in structured and unstructured data (tables, PDFs, Word files, emails), preserves traceability back to the source, and supports over 150 connectors.
Next, enrich the data. Rover uses Named Entity Recognition (NER) and your industry taxonomies (like MeSH or SNOMED) to tag people, places, compounds, and relationships. Instead of a flat table, you now have a living map of how your data points interconnect. Relationships are established between entities: for example, Ibuprofen is classified as an NSAID, and “Trial ABC” tests “Drug X.”
Then, model your knowledge graph. Start with a supplied ontology or map one onto your domain. You can iterate and evolve, especially if your data spans multiple languages or jurisdictions.
Finally, expose and query the graph. Use SPARQL or GraphQL. Enable search interfaces for analysts. Or connect Rover to GenAI tooling like LangChain for RAG-driven workflows.
Now, you have a unified view that is accessible across your AI stack, business analysts, and compliance teams.
A pharma case: from silos to semantic search
A global pharmaceutical company had invested heavily in Databricks and Azure. But their clinical trials, lab results, and regulatory reports remained fragmented, each in separate lakehouse zones with no semantic linkage.
Their teams spent weeks correlating data. GenAI initiatives stalled because of inconsistent metadata and siloed documents.
With Datavid Rover, they integrated all relevant content using a pharma-specific ontology. They created a knowledge graph linking trials, substances, adverse effects, and filings. Search became intuitive: “Show all Phase III trials flagged for cardiac risks in Switzerland.”
The results?
- Time to insight dropped by 70%
- Regulatory compliance workflows improved significantly
- Graph-based RAG enabled explainable GenAI summaries with built-in audit trails
Common misconceptions: Is this just a graph project?
Not quite. Traditional knowledge graph projects often start with abstract modelling and end in long delivery timelines. They’re seen as slow, academic, and disconnected from business outcomes.
Datavid Rover changes that. You start with real content, not hypothetical models. Semantic enrichment begins during ingestion. You focus on business impact from day one, whether search accuracy, LLM explainability, or risk reduction.
And your graph evolves alongside your needs. No overengineering. Just usable semantics, fast.
Agentic AI needs semantics
AI systems are moving beyond static prompts. They're becoming agentic, able to reason, act, and adapt. But these agents rely on context.
Without structured semantics, your agents won’t know:
- Which contract clause governs which region
- What trial results relate to which substances
- Where a flagged product variation fits into your portfolio
To avoid AI guesswork, start with the lakehouse you already have. Add a semantic layer that links and enriches your data. Use it to drive explainable automation, safer decisions, and faster answers.
With Datavid Rover, this doesn’t take a year. It takes 2-4 weeks.
Conclusion: It’s time to connect the dots
The future of enterprise AI isn’t just about collecting more data. It’s about making data intelligible, explainable, and actionable.
Your lakehouse scales your data. Your LLM generates responses. Your knowledge graph ensures they’re grounded, traceable, and true.
Talk to our experts: See how Datavid Rover delivers value from day one.
