Data management is a broad term that includes a variety of processes, tools, and techniques. These tools help organizations organize the vast amount of data they accumulate each day while also ensuring their collection and use comply with all applicable laws, regulations, and current security standards. These best practices are essential for organizations that want to utilize data in a way that enhances business processes while reducing risk and enhancing productivity.

The term “Data Management”, which is often used as a synonym for Data Governance and Big Data Management (though the most formal definitions focus on the way an organization manages its data and other assets from start to finish) encompasses all of these activities. This includes collecting and storing data; sharing and distributing data; creating, updating and deleting data; as well as providing access to the data to use in applications and analytics processes.

One of the most important aspects of Data Management is outlining a strategy for managing data before (for many funders) or in the early months following (EU funding) an investigation begins. This is essential to ensure that scientific integrity is maintained and the conclusions of the study are based on accurate and reliable data.

The challenges of Data Management include ensuring that end users can easily find and access relevant data, particularly when the data is distributed across multiple storage locations that are in different formats. Tools that can integrate data from different sources are beneficial as are metadata-driven data linesage records and dictionaries that can show the source of directory the data from various sources. The data must be accessible to other researchers for reuse over time. This includes using interoperable file formats like as.odt and.pdf instead of Microsoft Word document formats and ensuring that all the necessary information required to understand the data is recorded and documented.