Success story.
A data diagnostic to lower costs and get the platform ready for AI.
A digital company had its data platform on Google Cloud with a well-planned flow, but with high costs and data that arrived with inconsistencies to its analytics tools.
We take on any challenge
At Zenta there are no obstacles we cannot overcome.
Challenge
With growth, the databases exceeded 2.4 TB, and everything coexisted within a single project, without separate environments, automated deployments, or a formal data governance model. It was necessary to identify where spending was concentrated and what to correct first.
Solution
In four weeks, we conducted an assessment that reviewed 11 areas against Google Cloud best practices. We delivered recommendations prioritized by criticality and effort, a simpler target architecture, and a phased roadmap, including how to prepare data for AI and machine learning.
Results from our management
69 prioritized recommendations in 11 areas, 34 of them critical.
Technical report of about 100 pages and roadmap in 4 phases.
Target architecture by zones, with separate projects and AI-ready data.
From the start to the complete roadmap in about 4 weeks.