Services
Technical services for reliable data systems.
Bernardus Applied Data designs, builds, and modernizes data and software systems for organizations working with complex scientific, environmental, and operational information.
The work can span architecture, implementation, and modernization. The examples below describe possible scopes and deliverables; each engagement should reflect the system, the people operating it, and the problem that needs to be solved.
Data Systems Architecture
Integrate fragmented systems and establish reliable, maintainable data flows.
When this helps
Critical information is spread across databases, files, and applications. Teams need a clear understanding of how those sources connect and where authoritative records should live.
Work can include
- Map source systems, data dependencies, and operational requirements.
- Define common schemas, interfaces, and validation boundaries.
- Design data flows that account for maintenance and future changes.
Possible deliverables
System and data-flow diagrams, schema definitions, interface specifications, and a practical implementation plan.
Data Engineering & Automation
Replace manual and fragile processes with repeatable ingestion, transformation, validation, and publication workflows.
When this helps
Recurring updates depend on spreadsheets, manual exports, or scripts that are difficult to inspect. Staff spend time moving and checking data each time a new batch arrives.
Work can include
- Build ingestion and transformation workflows for disparate sources.
- Automate validation, reconciliation, and publication steps.
- Add logging, record accounting, and support for repeatable processing.
Possible deliverables
Data pipelines, source mappings, automated quality checks, and documentation for running and troubleshooting the workflow.
Scientific & Environmental Software
Build reliable software around scientific monitoring, research, and environmental data.
When this helps
A research or monitoring workflow needs to support ongoing use. Scientific conventions, historical records, and domain knowledge must remain understandable as the software evolves.
Work can include
- Translate scientific requirements into explicit processing rules.
- Implement domain-specific validation, metadata handling, and enrichment.
- Turn research scripts into maintainable processing and archival workflows.
Possible deliverables
Scientific processing software, documented domain rules, validation tests, and standardized outputs for analysis or archival publication.
Machine Learning & Decision Support
Develop analytical systems around real operational decisions and measurable performance.
When this helps
An organization has data or models but needs a clearer connection to a decision: what should be predicted, how performance should be evaluated, and how the result will be used.
Work can include
- Define the decision, available data, and useful measures of performance.
- Develop analytical baselines and evaluate candidate models.
- Connect model outputs to operational workflows with documented limitations.
Possible deliverables
Data-readiness assessments, analytical prototypes, model evaluations, and recommendations for operational use.
Technical Strategy & Modernization
Create practical paths forward for aging software, data systems, and technical workflows.
When this helps
An existing system still supports important work, but changes are becoming difficult. The next step needs to account for dependencies, operational constraints, and knowledge embedded in the current workflow.
Work can include
- Assess existing code, data flows, dependencies, and maintenance concerns.
- Identify which parts to retain, improve, or replace.
- Plan staged changes with validation and continuity of operations in mind.
Possible deliverables
Technical assessments, prioritized modernization plans, migration designs, and targeted improvements to existing software.
Services in Practice
NOAA Phytoplankton Monitoring Network
An operational scientific data platform integrating more than two decades of observations from multiple generations of collection systems into a standardized, quality-controlled archival workflow.
The PMN case study brings data integration, scientific quality control, and historical archive modernization together in one system, supporting both incremental synchronization and complete historical rebuilds.
View case study →