The institution defines architectures for the master data system, the master data governance system and the master data management system.

These three information systems should be adequately defined and conveniently integrated into the institutional architecture in order to better support the master data operations through the master data life cycle. This implies designing adequate architectural styles for the master data systems and the management information system in order to leverage maximum value for the institution’s master data.

The specific guidelines in this section are:

• Architectures for master data systems
• Implementation of master data systems
• Management of master data system evolution
• Master data system interoperability
• Security and privacy of master data

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:

• Master data quality management
• Preventive measures to foster the quality of master data
• Improvement of master data quality

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:

• Master Data Management and Master Data Governance Programmes
• Strategies, policies and roles
• Optimization of master data value