Data management plans (DMPs) are documents accompanying research proposals and project outputs. They describe data and tools employed in scientific investigations, mostly in free-form text. DMPs are often seen as an administrative exercise and not as an integral part of research practice. There is now widespread recognition that the DMP can have more thematic, machine-actionable richness with added value for all stakeholders: researchers, funders, repository managers, research administrators, data librarians, etc. The larger goal is to improve the experience for all involved by exchanging information across research tools and systems and embedding DMPs in existing workflows. This will enable parts of the DMP to be automatically generated and shared, thus reducing administrative burdens and improving the quality of information within a DMP. This paper presents 10 simple rules outlining specific steps to put machine-actionable DMPs into practice and realize their benefits.