Prior to being used for modelling purposes, the extracted data must go through a cleaning process to ensure that data meets a required quality standard. This process must consider, at a minimum, the following data characteristics:
(i)
Completeness: values are available, where needed,
(ii)
Accuracy: values are correct and error-free,
(iii)
Consistency: several sources across the institution lead to matching data,
(iv)
Timeliness: values are accurate as of the reporting date,
(v)
Uniqueness: values are not incorrectly duplicated in the same data set, and
(vi)
Traceability: the origin of the data can be traced.
5.4.2
Institutions must put in place process to accomplish a comprehensive data quality review. In particular, the quality of data can be improved by, amongst others, replacing missing data points, removing errors, correcting the unit basis (thousands vs. millions, wrong currency, etc.) and reconciling against several sources.
5.4.3
Institutions must put in place tolerance levels and indicators of data quality. These indicators must be mentioned in all model documentation. Data quality reports must be prepared regularly and presented to Senior Management and the Board as part of the DMF governance, with the objective to monitor and continuously improve the quality of data over time. Considering the essential role of data quality in supporting risk management and business decisions, institutions must also consider including data quality measures in their risk appetite framework.