Multi-Source Data Consolidation Audit (Onboarding)

Multi-Source Data Consolidation Audit (Onboarding)

1. 01

   Inventory every client data source and its current storage location

   List databases, SaaS exports, flat files, and object storage buckets. Note the volume, update frequency, and whether each source is structured or semi-structured.

2. 02

   Map each source to a target storage tier based on access patterns

   Hot data that feeds dashboards daily belongs in a queryable warehouse or high-performance object storage. Cold archival data can move to flat-rate or decentralized storage to cut costs.

3. 03

   Check for egress and API fees in the current setup

   Hyperscaler egress charges can inflate reporting costs when data moves between systems. Flat-rate options like Wasabi eliminate those fees, which matters for clients with frequent data pulls.

4. 04

   Assess geo-resilience and compliance requirements for each dataset

   If the client operates across regions or faces data residency rules, distributed storage or multi-region replication may be necessary. Document any compliance constraints before choosing architecture.

5. 05

   Estimate the total cost of ownership for the proposed stack

   Compare storage, compute, and transfer costs across at least three options, including managed warehouses and object storage. Use the client's actual data volumes and query patterns, not generic pricing.

6. 06

   Identify quick wins for cost reduction without architectural change

   Moving archival data to a cheaper tier or enabling lifecycle policies can cut storage bills by 50% or more. These wins build client trust before larger migrations.

7. 07

   Document the recommended architecture and present it to the client

   Include a clear diagram, cost comparison table, and a phased implementation plan. Flag any trade-offs between operational complexity and simplicity, such as distributed SQL tuning versus a managed platform.