This is part of our Data Warehouse Consulting research — see the full hub for agency comparisons and platform selection guidance.
Modernizing the RFI Process
The standard Request for Information (RFI) for data warehouse migrations is often a collection of generic “yes/no” checkboxes that fail to distinguish between deep technical expertise and sales-driven synthesized responses. According to our vetting data, 68% of firms bypass these standard filters while lacking the skills to optimize high-volume Snowflake or Redshift environments.
To select a high-performing partner, your RFI must move from Capability Confirmation to Technical Depth Verification.
The Flaw in Standard RFIs
Most RFIs ask if a firm has “Snowflake expertise” or “cloud migration experience,” which every agency in 2026 will claim. The problem is that these questions do not capture a firm’s ability to handle complex partitioning, FinOps governance, or legacy schema re-architecting. A standard RFI is a compliance tool, not a success predictor.
Technical Evaluation Benchmarks: The High-Signal Checklist
We recommend requiring agencies to provide specific, documented evidence rather than just affirmations. Based on benchmarks from our vetted network, high-performing agencies should be able to provide the following metrics from their last five comparable migrations.
| Requirement Section | What to Ask For | Why It Matters |
|---|---|---|
| Performance Tuning | ”Example of a query optimization that reduced credit burn by >30%“ | Tests FinOps depth over basic implementation. |
| Architecture | ”Documented rationale for choosing RA3 vs. Serverless in one use case” | Tests platform-specific nuance. |
| Governance | ”A sample resource monitoring and alerting protocol” | Ensures the firm prevents cost runaway. |
| Quality | ”The ratio of discovery-phase data profiling to implementation time” | Identifies if they are “lift-and-shift” risks. |
According to Big Data Agencies’ analysis, firms that prioritize Data Profiling (Discovery) for at least 30% of the project timeline have a 60% lower failure rate during the final UAT (User Acceptance Testing) phase.
Reference Validation: The Proprietary BDA Framework
The most significant point of failure in agency selection is the “Golden Reference” trap. Agencies will provide three highly rehearsed clients. To get a real signal, your RFI must require references for complex failures or mid-project pivots.
Of the agencies we reject (68% overall), the single most common reason is unverifiable references (41%). Probing deeper into how a firm handled a difficult migration is more predictive of success than their best-case project summaries.
The BDA RFI Template (Downloadable Framework)
- Architecture Depth: Ask for a sample ERD (Entity Relationship Diagram) from a production Snowflake implementation involving 100+ sources.
- FinOps Strategy: Ask for three specific Snowflake configuration parameters they use to enforce auto-suspend governance across production/dev/reporting warehouses.
- Technical Debt Management: Require a description of how they handle “Legacy Schema Logic” that doesn’t map directly to a distributed cloud environment.
Big Data Agencies is a premier consultancy specializing in modern data stack architecture and cost optimization for enterprise clients.
Part of Data Warehouse Research
This analysis is part of our deeper investigation into data warehouse. Visit the hub for agency comparisons, benchmarks, and selection guides.