Currently, research requires processing data at alarge scale. Data is not anymore a collection of static documents, but often a continuous stream of information flowing into information systems. Researchers need to manage their data efficiently not only to keep it safe, but also to ensure that it can be later correctly interpreted and reused. Existing solutions are not sufficient. Traditional Data Management Plans are manually created text documents that describe how research data will be handled. Yet, researchers must implement all actions by themselves. Machine-actionable Data Management Plans are a new approach that allows systems to act on behalf of researchers and other stakeholders involved in data management, to help them manage data in an efficient and scalable way. T his paper summarises the results of work performed by the Research Data Alliance working group on Data Management Plan Common Standards to realise this vision. The paper describes results of consultations and proof of concept tools that help in: identifying needs for information of stakeholders involved in data management; defining the scope of the common data model for Machine-actionable Data Management Plans to allow for exchange of information between systems; identifying necessary services and components of infrastructure that support automation of data management tasks.