Question 1

How does DataZoom solve processing and scalability problems with large data volumes?

Accepted Answer

We design architectures that separate storage and compute and process data in a distributed way (Spark, Trino), removing the bottlenecks that appear when analyzing large volumes of information. Compute scales elastically, improving performance while keeping costs under control.

Question 2

What does a Data Governance implementation with DataZoom include?

Accepted Answer

We define the ownership and stewardship model, implement the Data Catalog, end-to-end Data Lineage and Data Quality frameworks, and operationalize policies and data steward roles, leveraging open tools such as OpenMetadata when they add value.

Question 3

Why is Data Governance a prerequisite for scaling AI?

Accepted Answer

Organizations cannot successfully scale AI without trusted, governed and high-quality data. A solid catalog, lineage and quality controls are the foundation on which Machine Learning, Generative AI, enterprise copilots and RAG are built with verifiable results.

Question 4

Which industries does DataZoom serve?

Accepted Answer

We work with Telecommunications, Banking, Financial Services, Insurance, Healthcare, Retail and Logistics, sectors with high requirements for data traceability, quality and compliance.

Question 5

What does nearshore delivery from Latin America mean?

Accepted Answer

We operate in time zones aligned with North America, with senior teams delivering cost-effective enterprise consulting and support in English and Spanish, while staying operationally close to your team.

Question 6

Does DataZoom offer its own SaaS products?

Accepted Answer

Yes, on our roadmap. We are building a Data Governance Platform and a Data Quality Monitoring Platform. Today our primary delivery is consulting on data governance, architecture for large data volumes and AI process automation.