Data Management covers a broad range of tools, processes and techniques that assist an organization organize the massive amounts of data that it collects every day, while ensuring its collection and usage are in line with all regulations and laws, and current security standards. These best practices are crucial for businesses that want to utilize data in a manner that enhances business processes while reducing risk and increasing productivity.
The term “Data Management” is frequently 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 end-to-end), encompasses all of these actions. This includes the collection and storage of data, sharing and distributing of data in the form of creating, updating, and deleting data, as well as giving access to data for use in applications and analytics.
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) the study is launched. This is essential to ensure that scientific integrity is maintained and the findings of the study are based on accurate and reliable data.
The challenges of Data Management include ensuring that end users can easily find and access relevant data, especially when the data is distributed across multiple storage facilities in various formats. Data dictionaries, data lineage records and tools that integrate disparate sources of data are helpful. The data must also be available to other researchers to make it available for reuse in the long run. This means using interoperable file formats such 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.