Data Quality

Data quality in information security and GRC means that data used for risk, control, compliance, and security decisions is reliable, complete, accurate, consistent, timely, and appropriate for its purpose. It applies to records such as assets, users, vendors, risks, incidents, vulnerabilities, controls, evidence, and audit logs.

Data quality is important for compliance because organizations rely on data to prove that controls are operating, risks are being managed, and required activities are being completed. Inaccurate or incomplete data can lead to missed obligations, weak evidence, incorrect reports, and decisions based on the wrong assumptions.

Common data quality dimensions include accuracy, completeness, consistency, timeliness, validity, uniqueness, integrity, and fitness for use. For GRC teams, these dimensions help determine whether records can support control testing, remediation tracking, management reporting, and audit evidence.

Organizations measure data quality using metrics such as missing required fields, duplicate records, invalid values, stale records, unresolved exceptions, failed reconciliations, and error rates. They may also track whether key records are reviewed, approved, corrected, and updated within defined timeframes.

Controls that improve data quality include required field validation, defined data owners, approved sources of record, periodic reconciliations, automated checks, change review, exception handling, access controls, audit trails, and management review. These controls help ensure that data remains reliable as systems and processes change.

Poor data quality creates compliance risk by causing incomplete evidence, inaccurate reporting, missed remediation deadlines, incorrect access decisions, unreliable risk ratings, and weak audit trails. When data cannot be trusted, leadership and auditors may not be able to verify whether controls are working as intended.

Data quality describes whether data is accurate, complete, consistent, timely, and fit for use. Data governance is the broader set of roles, policies, standards, processes, and accountability structures used to manage data. Governance defines how data should be controlled; data quality measures whether the data meets those expectations.

Responsibility for data quality is usually shared. Business owners define what the data should mean, system owners maintain reliable sources, security and GRC teams use the data for oversight, and leadership sets accountability. Clear ownership is important because data quality problems often cross teams and systems.

Useful evidence includes data validation reports, reconciliation results, exception logs, review approvals, corrected record samples, change history, source-system exports, data quality dashboards, and procedures showing how issues are identified and resolved. Strong evidence should show both the control activity and the resulting correction or confirmation.

The Philippines Data Privacy Act expects personal information to be accurate, relevant, and, where necessary for the declared purpose of processing, kept up to date. Practical controls include defined data owners, reliable sources of record, validation checks, correction workflows, documented procedures, and traceable evidence showing that inaccurate or incomplete personal information is addressed.