Data Quality & Data Standards
GCD adheres to several defining data quality principles built in from when it started in 2004. These principles are used on all pooled data to ensure the data is of the highest quality: correctness, completeness and comparability.
The elements for controlling data quality are:
Rigourous validation rules
Banks are only able to submit the data if it passes a large number of strict quality tests. Based on these rules – set by the Methodology Committee - the Data Agent checks the data. If the rules are not met, the files will be rejected with an explanation enabling the Memberbank to do the necessary adjustments.
Full resubmission requirement
Banks joining GCD have greatly improved their data quality by cleaning their existing internal data to reach GCD data quality standards, this includes a full data resubmission requirement every 3 years which helps banks keep up to date with improving validations, new fields and changed definitions.
Data audit by senior credit experts
Each submission is accompanied by a rigid audit performed by an Global Credit Data Executive with senior credit experience and long history with the data model. Global Credit Data issues an audit letter for each bank, which highlights weaknesses and forms a data quality track record for their management. It is only by using this experienced human expertise that data gaps can be determined and fixed.
In order to encourage the member banks to improve the data quality, Global Credit Data has created a scoring system. Each member bank can see how its delivery compares to its peers in terms of data quality. In this way, a member bank can gauge the quality of the data by reference to their own known data quality and can judge the quality of individual elements with which they are concerned in their modelling.
Cleaning during RDS
Member banks are also advised and encouraged to examine the detailed raw data they receive to produce their own "representative data set" or RDS which comprises a filtering of the data to ensure that it matches the portfolio of the bank which will be using it. During this process the final data cleaning is done. For further information, please check our Reference Dataset Guidelines.