The institution establishes a framework for the management of the security and privacy of the master data based on the relevant regulations.

The institution implements effective and quality-preserving interoperability mechanisms not only with other systems within the institution but also with external systems.

In addition to providing the means of interaction with other systems, interoperability mechanisms should keep track of the provenance of data obtained from other institutions.

The institution puts into practice specific processes to manage change, maintenance and the evolution of the master data system.

As the master data system is at the core of the institution’s information systems and is used by a large number of systems, change and evolution have to be managed so as to minimize impacts and service disruptions. Therefore, the information model of the master data system should reflect the concepts used throughout the institution.

The institution implements the master data systems taking into account the functional requirements of all involved business areas of the institution.

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