Data quality is an accountability question
The issue is not only whether a field is right. It is whether the firm can explain source, transformation, validation, ownership, and use.
Signals / Data
Lineage, reporting, privacy, and evidence integrity
The data page turns reporting, risk aggregation, AI input, privacy, and record-keeping signals into practical questions about ownership, quality, lineage, and proof.
Supporting evidence
Ten further data signals
The shortlist carries the leadership read. These supporting rows preserve the source trail behind data lineage, reporting quality, privacy governance, AI data controls, and auditability.
- 06
BCBS 239 progress reviews keep accountability for data quality on the supervisory agenda
- 07
PRA reporting should connect templates, source systems, reconciliations, and senior sign-off
- 08
Transaction reporting quality depends on lineage, completeness, validation, and exception management
- 09
Surveillance data needs completeness and explainability before alerts can be trusted
- 10
Accuracy obligations should map to remediation, correction, and customer-impact evidence
- 11
Data sharing needs purpose, lawful basis, access, retention, and onward-use controls
- 12
Subject access readiness tests whether records can be found, explained, and disclosed safely
- 13
Records of processing should be usable evidence, not only a compliance inventory
- 14
Operational resilience depends on data recovery, data integrity, and service-level evidence
- 15
External statistical publications can reveal data-quality and reporting-change implications
Why it made the weekly brief
The editorial judgement
Data matters when the firm cannot evidence the information used for reporting, AI, customer decisions, risk aggregation, privacy, or operational recovery.
Risk, finance, compliance, data, technology, AI, privacy, and audit
The same data weakness can affect reporting, models, conduct, resilience, privacy, surveillance, and board decisions.
Lineage, controls, reconciliations, records, and sign-off
Good assurance shows where data came from, how it changed, who approved it, and how exceptions were resolved.
Control evidence checklist
What the reader should ask for
Data evidence should be useful to the people relying on the output, not only to the team maintaining the control inventory.
- Can the firm trace the data from source system through transformation, control, and final report or decision?Trace
- Which quality rules exist, who owns exceptions, and what happens when thresholds are breached?Validate
- Which reports, models, customer decisions, controls, or regulatory submissions rely on this data?Map
- Is lawful basis, retention, access, sharing, and deletion evidenced for the relevant data set?Govern
- Who is accountable for the final data product, and what evidence supports their attestation?Own
Data in the eight-stream map
Return to the cross-topic view and compare with AI, technology failure, cyber, and resilience.
Use when data is the weekly so-what
Promote the strongest signal into the consolidated weekly issue.
BCBS 239 risk data aggregation
Standing source for governance, architecture, accuracy, completeness, timeliness, and reporting usefulness.