Data Lake / Data Warehouse Modernization · Marketing Personalization · Real-time Analytics
Previously, however, ICA’s fragmented technical environment was holding it back. Data was siloed across legacy platforms and SQL server environments, creating high maintenance costs and preventing teams from sharing insights, while innovation stalled as data scientists struggled to push ML models into production.
Reported impact
Faster development of proofs of concept; Enables secure innovation across the retail business; Improves ML model training and deployment speed while lowering costs; ICA modernized its data architecture on Google Cloud to break down silos and accelerate AI innovation from 18 months to 10 weeks..
Security was built into the architecture from the start to meet strict regulatory requirements. The team engineered a custom encryption service to protect sensitive data at the column level. To allow data scientists to work with real information without compromising privacy, ICA implemented specialized decryption views. These views decrypt data only in transit when viewed by an authorized user, meaning the unencrypted data is never stored at rest in BigQuery. This approach eliminates the need to create mocked data when real data is too sensitive to access, which can often lead to inaccurate results. For added resiliency, ICA uses a hybrid key management strategy where a master key on an external cloud provider is required to unlock the key sets on Google Cloud.
stored or unified in BigQuery, Bigtable, orchestrated via Gemini platform, and surfaced through Gemini, Looker
Enables secure innovation across the retail business
Faster development of proofs of concept
ICA modernized its data architecture on Google Cloud to break down silos and accelerate AI innovation from 18 months to 10 weeks.
Improves ML model training and deployment speed while lowering costs
Scaling a century of service through
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