The specific guidelines in this section are:
The institution implements measures to ensure adequate quality levels in the master data and to improve the quality when necessary.
These measures, which are based on data quality goals and indicators, typically consist of corrective master data profiling and master data cleansing operations. In order to be cost effective, the data quality goals have to be clearly defined.
The institution implements preventive measures to foster the quality of the master data, especially by communicating data quality requirements to development teams and to master data operations and personnel responsible for master data-related tasks.
The institution manages the quality (e.g. completeness and accuracy) of the master data through a formalized and single institutional framework, with the aim of improving the reliability of the data used in the institution and, consequently, fostering confidence in related processes.
Should the master data not be of adequate quality, the functions involving these data will probably fail. In order to avoid the failure of key social security functions, it is necessary to carry out activities that ensure that the quality of the master data will be adequate for the tasks in which they will be used.
The specific guidelines in this section are:
The institution determines the value of the master data and performs master data governance and master data management practices to optimize the results expected from ICT investments (services and authorized assets of ICT) throughout the master data life cycle.
The institution establishes strategies, policies and plans for implementing the Master Data Governance and Master Data Management Programmes.
The institution carries out a unique and integrated programme for master data governance aligned to ICT and organizational governance, as well as a Master Data Management Programme that implements the Master Data Governance Programme.
The specific guidelines in this section are:
Social security operations and strategic decisions are based on the mission-critical availability of data related to the individuals and stakeholders involved in social programmes managed by institutions. As a consequence, the reliability of these operations and adjudications are based strongly on the reliability of the used data. Among the large volumes of data managed by social security institutions there is a key subset that is common to social programmes, and its quality and management have a strong impact on the overall activities of social security institutions.
