Data Management encompasses a broad array of tools, processes and techniques that aid an organization structure the vast amounts of data that it collects every day, while making sure that its collection and use adhere to all laws and regulations, as well as up to date security standards. These best practices are vital for businesses that want to utilize data in a way that improves the efficiency of business processes while reducing risk and increasing productivity.

Often, the term “Data Management” is used interchangeably with terms such as Data Governance and Big Data Management, however the most formal definitions of the issue focus on how an organization manages its information assets and data from end to the very end. This encompasses collecting and storing data; sharing and delivering data by creating, updating, and deleting data; as well as providing access to data to use in analytics and applications.

Data Management is a vital aspect of any research study. This can be accomplished prior to the start of the study (for many funders) or within the first few months (for EU funding). This is essential to ensure that the scientific performance reports examples integrity of the study is preserved and that the study’s results are based on accurate data.

Data Management challenges include ensuring end users can find and access the relevant information, especially when data is spread across multiple systems and storage spaces in different formats. Tools that can integrate data from different sources are beneficial, as are metadata-driven data dictionary and data lineage records that show the source of the data from various sources. Another concern is making sure that the data is accessible for long-term re-use by other researchers. This involves using interoperable file formats like as.odt and.pdf instead of Microsoft Word document formats and making sure that all the information needed to understand the data is collected and documented.