Data Quality

Decisions, models and reports are only as good as the data behind them. Poor data quality can lead to wrong conclusions, financial losses, regulatory breaches and damaged trust.

Common dimensions of data quality include accuracy, whether data correctly reflect reality; completeness, whether required data are present; consistency, whether data match across systems; timeliness, whether data are up to date; validity, whether data follow required formats and rules; and uniqueness, whether records are duplicated.

For example, if a bank's customer database contains duplicate records with different addresses, it may send statements to the wrong place, miscalculate risk exposures or fail anti-money laundering checks.

Organisations improve data quality through validation rules when data are entered, regular cleansing and reconciliation, clear ownership of data and monitoring with quality metrics. In financial services, regulators expect banks to have reliable data for risk reporting, following principles set by the Basel Committee after weaknesses were exposed during the 2008 financial crisis.