Data management planning overview

Data management planning overview

Why a data management plan is crucial

A data management plan helps achieve optimal handling, organising, documenting and enhancing of research data. It is particularly important for facilitating data sharing, ensuring the sustainability and accessibility of data in the long-term and allowing data to be reused for future research.

For the effective management of data, planning must start when research is being designed and needs to consider both how data will be managed during the research and how they will be shared afterwards. This involves thinking critically about the sharing of research data, what might limit or prohibit data sharing, and whether any steps can be taken to remove such limitations.

Good practice is also to regularly update the data management plan as research progresses, for example during six-monthly project meetings. That way, the plan becomes important documentation and a quality assurance statement for your research data in the long term.


Planning helps to focus on the resources and funding needed to implement good data management practices. This applies to data storage, for example. It also helps to clarify, at an early stage, individual and institutional roles and responsibilities. Planning is also essential to facilitate compliance with ethical codes and data protection laws.

Many public research funders require a data management and sharing plan as part of research grant applications. They also expect data to be shared. In the UK, all seven research councils require data management plans, as do the Wellcome Trust, DFID and Cancer Research UK. The ESRC also introduced this requirement in 2011.

Key data management planning issues

The key issues to think about at the start of data management planning are:

  • Know your legal, ethical and other obligations regarding research data, towards research participants, colleagues, research funders and institutions.
  • Know your institution’s policies and services, such as storage and backup strategy, research integrity framework, Intellectual Property rights policy, and any data sharing facilities like an institutional repository.
  • Assign roles and responsibilities to relevant parties.
  • Design data management according to the needs and purpose of the research.
  • Aim to incorporate data management measures as an integral part of your research cycle.
  • Implement and review the management of data as part of continued research progression and review.

Our data management checklist can point you to key matters to consider in a data management and sharing plan.