Data Management encompasses a wide 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 the collection and use adhere to all laws and regulations, as well as up to date security standards. These best practices are essential for businesses 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 interchangeably 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) covers all of these actions. This includes storing and collecting of data, sharing and distributing of data by creating, updating and deletion data and giving access to data analysis and application.
Data Management is a vital aspect 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 crucial to ensure that the integrity of the research is maintained and that the findings of the study are built on accurate and reliable data.
Data Management challenges include ensuring that users are able to find and access the relevant information, particularly when data is spread across multiple systems and storage locations in various formats. Tools that can integrate data from different sources are beneficial and so are metadata-driven data such as data lineage records and dictionaries which can reveal the source of the data from various sources. Another concern is making sure that the data can be utilized for re-use by other researchers. This requires using interoperable formats like as.odt or.pdf instead of Microsoft Word document formats, and ensuring that all relevant information is captured and documented.