Data Management encompasses a vast range of processes, tools and techniques that assist an organization organize the massive amounts of data it accumulates every day, while making sure that its collection and use are in line with all laws and regulations, as well as current security standards. These best practices are vital for organizations that want to use data in a way that improves business processes while reducing risk and enhancing productivity.
Often the term “Data Management” is often used interchangeably with terms like Data Governance and Big Data Management, but the most formal definitions of the area are focused on how a company manages its information assets go to website and data from end to the very end. This includes collecting and storing of data, sharing and distributing of data, creating, updating and deleting data as well as giving access to data use in analytics and applications.
Data Management is a vital element of any research study. This can be done before the study starts (for many funders) or within the first few months (for EU funding). This is essential to ensure that scientific integrity is maintained and that the conclusions of the study are based on accurate and reliable data.
The challenges of Data Management include ensuring that users can easily locate and access relevant data, particularly when the data is spread across multiple systems and storage locations with different formats. Data dictionary, data lineage records and other tools that integrate different sources of information are beneficial. The data must also be available to other researchers for reuse in the future. This means 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.
