TRUSTED DATA REFINERY
AI Compute Cost Leakage
Stop wasting compute resources on Dark Data. Trusted Data Refinery eliminates ROT before it reaches your AI systems, dramatically reducing query volume and processing costs.
How Dark Data Inflates Your Compute Budget
Compute costs are now typically 25% of a CIO's budget. Security scans, compliance searches, analytics workloads, and AI queries operating on bloated datasets are among the most significant drivers of this cost. By eliminating ROT and ensuring that only policy-approved Trusted Data is mirrored into OneLake and active AI environments, Trusted Data Refinery dramatically reduces query volume, processing time, and unnecessary AI cycles.
The Problem: Runaway Compute Consumption
Every time Copilot, Fabric, or your analytics platform queries data, compute resources are consumed. When that data includes 60% ROT — Redundant, Obsolete, and Trivial content — you're paying to process garbage. This creates a massive swamp of unnecessary compute costs that overwhelms CIO budgets.
The Solution: Upstream Filtering with Trusted Data Refinery
Trusted Data Refinery acts as the "upstream refinery" for your data estate. Its primary function is to transform chaotic, unclassified Dark Data into AI-Ready, Actionable Trusted Data before it reaches Copilot or OneLake. By refining data in place using Data Reader, Data Identifier, and Trusted Data Portal, we ensure that compute resources are only spent on high-value, policy-approved content.
How Trusted Data Refinery Reduces Costs
The three components of Trusted Data Refinery work together to slash compute costs:
Data Reader scans 8 million files per hour without moving data, identifying candidates for elimination before they consume resources.
Data Identifier classifies ROT and enriches valuable data with metadata, ensuring only high-signal content proceeds.
Trusted Data Portal leverages curated Trusted Data Collections that AI-Ready Data Feeder mirrors to OneLake, smaller, faster, and cheaper to query.
Measurable Impact
Organizations deploying Trusted Data Refinery consistently report dramatic reductions in compute consumption. When AI systems operate on curated Trusted Data Collections instead of data swamps, query times decrease, results improve, and costs drop, often by 50% or more for analytics and AI workloads.
Key Benefits
- ROT Elimination: Remove 60%+ of low-value data before it consumes compute resources.
- Faster Queries: Smaller, higher-signal Trusted Data Collections mean faster AI responses.
- Predictable Costs: Control compute spend by controlling what data reaches AI systems.
- Better Results: Higher-quality data produces higher-quality AI outputs.
Conclusion
Stop paying for AI to process garbage. Trusted Data Refinery's upstream filtering ensures every compute cycle is spent on data that actually matters.
