How a Leading LatAm Fintech Eliminated 400 Hours of Data Work a Month with Decube

Financial Services (SME-Focused Fintech)

LatAm (Mexico)

A leading fintech in Mexico built a trusted data governance foundation with Decube, eliminating hundreds of hours of manual data work and cutting regulatory reporting cycle time by more than 50%.

Key outcome
400 hours saved monthly

400 hours of manual data work eliminated per month

Decube modules
Automated Data Lineage
Data Catalog
Data Observability
AI Readiness
Regulatory driver

Mexican financial regulators (CNBV, Banxico, Condusef)

The challenge

The company is one of the largest fintechs serving small and medium businesses across Latin America, with a product line spanning working capital loans, corporate credit cards, payments, and an expanding suite of banking services. As adoption scaled, so did the volume of data behind it, with millions of data points and terabytes of information moving through more than 20 source systems every month.

Governance had not kept pace with growth. Mapping data lineage across those systems was a manual exercise costing close to 336 hours a month, and still fell short of real-time traceability. Analysts spent 40 or more hours a month tracing SQL logic by hand just to answer routine data access requests from product, compliance, and audit teams. Recurring ETL failures and data inconsistencies required escalating to multiple engineers each time, delaying clean data getting back into daily operations. Operating under close oversight from Mexican financial regulators, and pushing toward AI-driven lending and dynamic credit models, the company needed more than a reporting tool. It needed a foundation of trusted, governed data it could stand behind.

The solution

The company partnered with Decube to build that foundation, deploying automated data lineage, a business-facing data catalog, and proactive observability on top of its existing warehouse and pipeline estate, unified into a single governance layer rather than three disconnected tools.

Live, column-level lineage and audit trails replaced manually maintained ERD diagrams and spreadsheets, giving both engineering and audit teams real-time visibility into how data moved and transformed. A shared data catalog let product, compliance, and fraud teams find and validate datasets on their own, without routing every question back to engineering. Observability was layered in to flag schema changes, missing values, and volume anomalies upstream, before they reached downstream dashboards or operational teams. As the metadata layer matured, it also became the foundation for the company's AI initiatives, giving machine learning teams pre-validated, documented datasets to build on instead of tribal SQL knowledge.

Rollout moved quickly because of Decube's connector model. Using a read-only service account and native connectors spanning SQL engines, file formats, and hierarchical data, the team connected more than 30 databases and file sources within weeks, without re-architecting a single pipeline.

Results in practice

Automated lineage and audit readiness

Live lineage and column-level mapping eliminated roughly 336 hours a month of manual lineage work, worth close to $141,000 a year in reclaimed engineering time, while giving auditors continuously current lineage instead of static diagrams built ahead of each review.

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A catalog business teams could use without engineering

Product, compliance, and fraud teams began locating and validating datasets themselves instead of filing requests to engineering, saving roughly 480 analyst hours a year, worth about $21,600, and speeding up day to day decisions across those teams.

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Observability that catches issues before they escalate

Proactive anomaly detection on schema, volume, and missing values cut the need to pull three engineers into recurring ETL firefighting, avoiding close to $18,500 a year in incident cost and reducing delays in getting clean data to daily operations.

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Metadata that AI initiatives could build on

Pre-validated, documented datasets shortened onboarding for new machine learning use cases, saving roughly 48 hours across eight AI projects in a year and shortening the path from idea to a working model for the company's AI-driven lending work.

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The outcome

Across the four use cases, the company eliminated roughly 400 hours a month of manual lineage, catalog, and observability work, a combined value of close to $183,000 a year. Regulatory reporting cycle time dropped by more than 50%, critical lineage gaps flagged by internal audit were closed, and internal controls were strengthened to meet CNBV and data protection expectations, all without adding headcount.

“Decube gave us clarity in our chaos, and hope with trust and automation. It replaced spreadsheets and hard-coded SQL documentation,”

said the company's Head of Data & Engineering.

Build a data foundation you can trust

See how Decube helps financial institutions build trusted, governed data foundations, reduce manual data work, and keep pace with regulatory demands.

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