Data Management encompasses a broad array of tools, processes and techniques that help an organization structure the vast amounts of data that it collects each day, while also ensuring its collection and usage comply with all laws and regulations and up to date security standards. These best practices are crucial for organizations seeking to leverage data in ways that can enhance 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 most formalized definitions focus on the way an company manages its data and other assets from beginning to end) encompasses all these activities. This includes collecting and storing of data, sharing and distributing of data as well as creating, updating and deleting data as well as giving access to data use in analytics and applications.
One of the most important aspects of Data Management is outlining a data management strategy before (for many funders) or during the first months following (EU funding) a research study begins. This is essential to ensure that the integrity of the research of the study is maintained and that the study’s results are based on accurate data.
Data Management challenges include ensuring that end users can locate and access relevant information, especially when data is spread across multiple storage locations in various formats. Tools that combine disparate data sources are beneficial, as are metadata-driven data such as data lineage records and dictionaries that provide evidence of the source of the data from different sources. Another issue is ensuring that the data can be utilized for re-use by other researchers. This includes using interoperable formats like as.odt or.pdf instead Microsoft Word document formats, and making sure that all the information needed is recorded and documented.