Non-Functional Requirements (NFRs) Framework for Software Systems

Overview

Data Quality and Data Integrity Non-Functional Requirements (NFRs) define how accurate, complete, consistent, timely, valid, reconciled, and trustworthy the data used or produced by a software system or solution must be.

These requirements are critical for operational decisions, reporting, analytics, integrations, customer experience, compliance, AI-enabled capabilities, and audit readiness.

Best Practice: Define data accuracy non-functional requirements

Description

Data accuracy NFRs define how correctly data must represent the real-world business event, source record, calculation, status, identifier, or transaction it is intended to represent.

Benefits

Accuracy requirements reduce business errors, customer impact, reconciliation effort, regulatory exposure, and downstream decision risk.

Example non-functional requirements

• The system shall validate critical input fields against approved business rules and authoritative reference data before accepting transactions.

• Validation method: Validate through business-rule testing, reference-data testing, invalid-input testing, and sample transaction review.

• Example validation evidence: Test cases, rule configuration, reference-data snapshot, rejected transaction examples, and QA approval.

• Critical calculated values shall match approved calculation rules with no unresolved high-severity discrepancies before release.

• Validation method: Validate through calculation test cases, independent recalculation, sample comparison, and defect review.

• Example validation evidence: Calculation test report, sample result comparison, rule specification, defect log, and business signoff.

Related stakeholders

Typical stakeholders include product owners, data owners, data stewards, data governance teams, data architects, data engineers, integration teams, analytics teams, QA teams, operations teams, compliance stakeholders, and business users.

Related lifecycle phases

Accuracy NFRs are defined during data analysis, requirements, and design; validated during development testing, SIT, UAT, data migration testing, and production reconciliation.

Best Practice: Define data completeness non-functional requirements

Description

Data completeness NFRs define which required data elements, records, relationships, events, metadata, and evidence must be present for a process, transaction, integration, or report to be usable and trustworthy.

Benefits

Completeness requirements prevent downstream failures, manual rework, missing reporting fields, incomplete records, and poor operational decisions.

Example non-functional requirements

• The system shall reject or quarantine records missing mandatory fields required for processing, reporting, audit, or downstream integration.

• Validation method: Validate through required-field tests, invalid payload tests, quarantine workflow tests, and downstream integration testing.

• Example validation evidence: Test results, schema rules, rejected/quarantined record samples, integration logs, and defect records.

• Daily data loads shall achieve the approved completeness threshold for expected records, required attributes, and required relationships before dependent processing begins.

• Validation method: Validate through reconciliation counts, load-control reports, dependency gating tests, and sampling of loaded records.

• Example validation evidence: Load report, reconciliation report, threshold configuration, sample records, and operations approval.

Related stakeholders

Typical stakeholders include product owners, data owners, data stewards, data governance teams, data architects, data engineers, integration teams, analytics teams, QA teams, operations teams, compliance stakeholders, and business users.

Related lifecycle phases

Completeness NFRs are defined during data modeling, integration design, and reporting design; validated during SIT, data migration, batch/load testing, UAT, and production operations.

Best Practice: Define data consistency non-functional requirements

Description

Data consistency NFRs define how data values, statuses, identifiers, relationships, and business rules must remain aligned across records, components, systems, caches, replicas, reports, and integrations.

Benefits

Consistency requirements reduce reconciliation defects, duplicate records, conflicting reports, broken workflows, and loss of trust in system outputs.

Example non-functional requirements

• Shared business identifiers and status values shall remain consistent across the system of record, integration payloads, downstream systems, and reports within the approved synchronization window.

• Validation method: Validate through cross-system reconciliation, integration testing, report comparison, and synchronization timing review.

• Example validation evidence: Reconciliation report, integration logs, report comparison, synchronization metrics, and defect resolution evidence.

• The system shall detect and report conflicting values for critical data elements received from multiple sources before those values are used for downstream processing.

• Validation method: Validate through conflict scenario testing, rule execution review, alert testing, and sample conflict resolution review.

• Example validation evidence: Conflict test results, rule configuration, alert logs, exception queue records, and data steward signoff.

Related stakeholders

Typical stakeholders include product owners, data owners, data stewards, data governance teams, data architects, data engineers, integration teams, analytics teams, QA teams, operations teams, compliance stakeholders, and business users.

Related lifecycle phases

Consistency NFRs are defined during data architecture, integration design, and reporting design; validated during SIT, data reconciliation, UAT, production monitoring, and recurring data quality review.

Best Practice: Define data timeliness non-functional requirements

Description

Data timeliness NFRs define how current data must be, how quickly data must be captured, processed, synchronized, published, corrected, or refreshed, and what delay is acceptable for users or downstream systems.

Benefits

Timeliness requirements improve operational decision-making, integration reliability, reporting confidence, customer experience, and incident detection.

Example non-functional requirements

• Operational dashboards shall refresh critical status metrics within the approved freshness threshold during business operating hours.

• Validation method: Validate through dashboard refresh tests, source-to-dashboard timing measurement, and production monitoring of data age.

• Example validation evidence: Refresh test results, data age dashboard, monitoring metrics, dashboard configuration, and operations approval.

• Integration events shall be delivered to downstream consumers within the approved latency target for 95% of events during normal and peak processing windows.

• Validation method: Validate through event timing tests, peak-load tests, message broker metrics, and consumer acknowledgement review.

• Example validation evidence: Event latency report, load test results, broker metrics, consumer logs, and SLO review.

Related stakeholders

Typical stakeholders include product owners, data owners, data stewards, data governance teams, data architects, data engineers, integration teams, analytics teams, QA teams, operations teams, compliance stakeholders, and business users.

Related lifecycle phases

Timeliness NFRs are defined during workflow, integration, and reporting design; validated during performance testing, SIT, UAT, production monitoring, and operational service reviews.

Best Practice: Define validation, reconciliation, and lineage non-functional requirements

Description

Validation, reconciliation, and lineage NFRs define how data is checked, reconciled, traced, and explained from source through transformation, processing, storage, reporting, archival, and downstream use.

These requirements help teams understand where data came from, how it changed, and whether it remains fit for use.

Benefits

Validation, reconciliation, and lineage requirements improve trust, auditability, defect diagnosis, regulatory reporting, incident response, and data governance.

Example non-functional requirements

• Critical data flows shall include reconciliation controls that compare source counts, target counts, rejected records, transformed records, and exception counts for each processing cycle.

• Validation method: Validate through reconciliation control testing, batch/job execution review, exception scenario tests, and sample cycle review.

• Example validation evidence: Reconciliation report, job logs, exception records, sample cycle evidence, and data owner approval.

• Critical reports and downstream datasets shall maintain lineage from source systems through transformations, rules, loads, and publication points.

• Validation method: Validate through lineage review, transformation mapping review, sampled report-to-source traceability, and data catalog inspection.

• Example validation evidence: Lineage diagram, transformation mapping, data catalog entry, sampled traceability record, and governance review approval.

Related stakeholders

Typical stakeholders include product owners, data owners, data stewards, data governance teams, data architects, data engineers, integration teams, analytics teams, QA teams, operations teams, compliance stakeholders, and business users.

Related lifecycle phases

Validation, reconciliation, and lineage NFRs are defined during data architecture, integration design, reporting design, and governance planning; validated during SIT, data migration, batch testing, reporting tests, and recurring data governance review.

Best Practice: Define data quality and data integrity validation and evidence non-functional requirements

Description

Data quality and data integrity validation and evidence NFRs define how data quality requirements are proven, which evidence is retained, who approves the evidence, and how data quality defects are tracked and resolved.

They should cover validation rules, reconciliation results, data profiling, lineage evidence, exception handling, and production monitoring.

Benefits

Data quality validation requirements make data trust measurable and auditable. They also create a repeatable basis for release readiness, operational monitoring, and continuous improvement.

Example non-functional requirements

• Each release that changes critical data capture, transformation, integration, reporting, or reconciliation logic shall retain data quality validation evidence before production deployment.

• Validation method: Validate through release evidence review and sampling of changed data flows for quality tests, reconciliation evidence, and business approval.

• Example validation evidence: Data quality test report, reconciliation results, sample records, transformation mapping, approval record, and release checklist.

• Production data quality issues above the approved severity threshold shall be monitored, triaged, assigned, remediated, and reviewed for recurring root cause.

• Validation method: Validate through production monitoring review, defect sampling, remediation tracking, and recurring data quality review records.

• Example validation evidence: Data quality dashboard, issue log, defect tickets, remediation evidence, root-cause analysis, and governance meeting notes.

Related stakeholders

Typical stakeholders include product owners, data owners, data stewards, data governance teams, data architects, data engineers, integration teams, analytics teams, QA teams, operations teams, compliance stakeholders, and business users.

Related lifecycle phases

Data quality validation NFRs are defined during governance planning and quality planning; validated during development testing, SIT, UAT, migration, release readiness, production monitoring, and periodic data quality review.