ISO/IEC 38505-1:2017

ISO/IEC 38505-1:2017 provides guiding principles for members of governing bodies of organizations (which can comprise owners, directors, partners, executive managers, or similar) on the effective, efficient, and acceptable use of data within their organizations by - applying the governance principles and model of ISO/IEC 38500 to the governance of data, - assuring stakeholders that, if the principles and practices proposed by this document are followed, they can have confidence in the organization's governance of data, - informing and guiding governing bodies in the use and protection of data in their organization, and - establishing a vocabulary for the governance of data. ISO/IEC 38505-1:2017 can also provide guidance to a wider community, including: - executive managers, - external businesses or technical specialists, such as legal or accounting specialists, retail or industrial associations, or professional bodies, - internal and external service providers (including consultants), and - auditors. While this document looks at the governance of data and its use within an organization, guidance on the implementation arrangement for the effective governance of IT in general is found in ISO/IEC/TS 38501. The constructs in ISO/IEC/TS 38501 can help to identify internal and external factors relating to the governance of IT and help to define beneficial outcomes and identify evidence of success. ISO/IEC 38505-1:2017 applies to the governance of the current and future use of data that is created, collected, stored or controlled by IT systems, and impacts the management processes and decisions relating to data. ISO/IEC 38505-1:2017 defines the governance of data as a subset or domain of the governance of IT, which itself is a subset or domain of organizational, or in the case of a corporation, corporate governance. ISO/IEC 38505-1:2017 is applicable to all organizations, including public and private companies, government entities, and not-for-profit organizations. This document is applicable to organizations of all sizes from the smallest to the largest, regardless of the extent of their dependence on data.