Architecture & lineage
Map sources, transformations, ownership and downstream consumers, then define a practical structure that makes critical data flows visible and maintainable.
The foundation beneath the model
Reliable modelling begins before the first algorithm.
Analytical credibility depends on knowing where data originated, how it was transformed, which controls were applied and whether the process can be reproduced. Fragmented spreadsheets and opaque pipelines create model risk long before model selection begins.
Arocha & Associates designs and reviews focused data solutions for actuarial, insurance and financial applications—connecting technical architecture with the evidence, ownership and controls required by the business.
Engagements are deliberately proportionate: from reviewing one analytical workflow to designing a controlled data pipeline or target-state architecture.
Core support
Connect sources, controls and analytical use.
Pipelines & reproducibility
Develop controlled extraction, transformation and analytical workflows with repeatable logic, clear dependencies, versioning and documented outputs.
Quality & validation controls
Establish reconciliations, reasonableness checks, exception handling, quality metrics and review evidence at the points where errors matter most.
From source to decision
Design controls around the full analytical journey.
- Identify authoritative sources, definitions and accountable data owners.
- Trace transformations, joins, assumptions and derived variables.
- Separate development, validation and production environments appropriately.
- Record versions, changes, exceptions and manual interventions.
- Monitor freshness, completeness, validity and reconciliation over time.
A focused engineering cycle
Fix the critical flow before expanding the platform.
The work begins with the analytical use case and builds only the architecture, automation and controls needed to support it reliably.
- 01
Map
Document sources, transformations, owners, consumers and failure points.
- 02
Design
Define the target flow, architecture, control points and operating responsibilities.
- 03
Build
Implement or prototype reproducible pipelines, checks and documentation.
- 04
Operate
Establish monitoring, exception handling, change control and handover.
Typical outcomes
More reliable data with less manual uncertainty.
Documented lineage from authoritative source through analytical output.
Reproducible pipelines with controlled transformations and versioning.
Targeted quality checks, reconciliations and exception reporting.
Clear ownership, operating procedures and change-control responsibilities.
Start with the trust gap
Where does confidence in the data flow break down?
We can map the current process, identify the material control and reproducibility gaps, and define a focused route to a more dependable analytical foundation.