Data Management covers a broad array of tools, processes and techniques that aid an organization organize the massive amounts of data that it collects every day, while making sure that its collection and use are in line with all laws and regulations and current security standards. These best practices are essential for organizations who want to utilize data in a manner that improves business processes, while reducing risk and increasing productivity.
The term “Data Management”, which is often used in conjunction with Data Governance and Big Data Management (though the most formal definitions focus on how an company manages its data and other assets end-to-end) encompasses all these actions. This covers collecting and storing data; sharing and delivering data as well as creating, updating and deleting data; as well as giving access to the data to be used in analytics and applications.
One of the most crucial aspects of Data Management is outlining a data management strategy before (for many funders) or during the first months following (EU funding) the research study starts. This is essential to ensure that the integrity of the research of the study is maintained, and that the study’s findings are based on reliable data.
The difficulties 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. Tools that integrate data from different sources are helpful, as are metadata-driven data such as data lineage records and dictionaries that can show how the data originated from various sources. Another challenge is ensuring that the data can be made available for long-term re-use by other researchers. This includes using interoperable formats like as.odt or.pdf instead of Microsoft Word document formats, and ensuring all necessary information is documented and recorded.