How easily can users access data, manage data and maintain data for supporting their daily tasks and decisions? This means having the correct permisions and metadata in place - e.g. including the metadata with the data itself, making clear what permissions and governance apply to a particular set of data.

How is the maintenance of data assured in a way that data is complete, accurate, and consistent over its entire lifecycle? Data integrity ensures that data is protected against unauthorized modifications, corruption, or loss. It verifies that data adheres to predefined standards and guidelines, preventing the inclusion of erroneous or irrelevant information.

Data breaches can lead to huge losses for business, in terms of legal costs, financial compensation, as well as a company’s reputation. The Internet of Things (IoT) presents an extra layer of scurity concerns. It is vital any orgnization factor data ownership, privacy and security issues into their data governance. Data minimization is good practice, holding only the minimum of critical data needed to fulfil your purpose. Understanding the data risk of the organisation is crucial. Risks need to be described, assessed, the rationale documented. Start from the existing crontols that are used to monitor risks, and then define actions how to reduce the probability of a risk to happen and its impact.

A data governance plan should define who is the owner of which data within the organization and who is accountable for defined aspects of data, such as integrity, accuracy, controlling who can access it, or risk management. A good data governance programme should also define clear procedures for how the data can be used. A RACI (responsible, accountable, consulted, informed) model clarifies roles: who is responsible vs. who is accountable, whose opinion needs to be asked, and who just needs to be kept up-to-date on actions taken.