Data Management encompasses a vast variety of tools, processes and techniques that aid an organization structure the vast amounts of data it accumulates each day, while also making sure that the collection and use conform to all regulations and laws, and current security standards. These best practices are crucial for businesses looking to harness data to improve the efficiency of their business processes while reducing risks and enhancing productivity.

The term "Data Management", which is often used to refer to Data Governance and Big Data Management (though most formalized definitions focus on how an company manages its data and other assets from start to finish) encompasses all of these activities. This includes storing and collecting of data, sharing and distributing of data as well as creating, updating and deletion data and giving access to data for analysis and application.

Data Management is a vital element of any research study. It can be done before the study begins (for many funders), or within the first few months (for EU funding). This is crucial to ensure that the integrity of the research is maintained and that the results of the study are built on accurate and reliable data.

Data Management challenges include ensuring that users have the ability to locate and access relevant information, particularly when data is spread out across multiple storage locations in various formats. Tools that can combine disparate data sources are useful and so are metadata-driven data linesage records and dictionaries that provide evidence of how the data originated from different sources. The data should be accessible to other researchers for long-term reuse. This requires using interoperable formats like as.odt or.pdf instead Microsoft Word document formats, and ensuring that all relevant information is recorded and documented.