TRUSTED DATA REFINERY
AI Compute Cost Leakage
Reduce avoidable processing by reviewing redundant, obsolete, and trivial information before selecting data for an AI workload.
How Dark Data Inflates Your Compute Budget
Unnecessary information can add storage, indexing, and processing cost. Trusted Data Refinery analyzes file estates in place and helps reviewers prepare purposeful Collections. Measure the effect on a defined workload; no fixed savings percentage or query-speed improvement is promised.
The Problem: Runaway Compute Consumption
AI and analytics workloads consume resources according to the information and operations involved. Identify redundant, obsolete, and trivial content, then review what can be excluded under retention, hold, access, and business requirements.
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, reviewers can select information for approved downstream use.
How Trusted Data Refinery Reduces Costs
The Suite supports a reviewed approach to information preparation:
Data Reader inventories supported file estates in place. Coverage and throughput depend on source conditions, scan depth, and the validated deployment.
Data Identifier classifies ROT and enriches valuable data with metadata for reviewers to evaluate before use.
Trusted Data Portal leverages curated Trusted Data Collections that Data Feeder delivers through supported routes. Data Mirroring is a separate capability; destination configuration and workload costs require validation.
Measurable Impact
Compare a defined workload before and after preparation. Record input volume, processing time, quality checks, and total operating cost. Results depend on source conditions, review decisions, and downstream platforms.
Key Benefits
- ROT Elimination: Identify cleanup candidates and approve exclusions before a defined workload.
- Faster Queries: Assess whether purposeful Collections improve the selected query workload.
- Predictable Costs: Establish a baseline and measure costs for the approved workload.
- Better Results: Evaluate source quality and context against the intended AI use case.
Conclusion
Start with a measurable information problem. Review candidate data, confirm the supported integration, and compare the outcome against a documented baseline.