For the avoidance of doubt, the scope under consideration in this section includes the data employed for modelling and validation purposes, not the data employed for regular risk analysis and reporting. This section focuses on the construction of historical data sets for the purpose of modelling.
5.1.2
Accurate and representative historical data is the backbone of financial models. Institutions must implement rigorous and a comprehensive formal data management framework (“DMF”) to ensure the development of accurate models. Institutions must consider DMF as a structured process within the institution, with dedicated policies and procedures, and with the adequate amount of resources and funding. The DMF core principles are as follows:
(i)
It must be approved by Senior Management and the Board,
(ii)
It must be thoroughly documented with indication of limitations and assumptions,
(iii)
Its coverage must include the whole institution and all material risk types, and
(iv)
It must be independently validated.
5.1.3
The DMF must include, at a minimum, the following steps:
(i)
Identification of sources,
(ii)
Regular and frequent collection,
(iii)
Rigorous data quality review and control,
(iv)
Secure storage and controlled access, and
(v)
Robust system infrastructure.
5.1.4
The roles and responsibilities of the parties involved or contributing to the DMF must be defined and documented. Each data set or data type must have an identified owner. The owner is accountable for the timely and effective execution of the DMF steps for its data set or data type. The owner may not be responsible for performing each of the DMF steps, but she/he must remain accountable for ensuring that those are performed by other parties with high quality standards.