Data Management encompasses a wide range of processes, tools and techniques that help an organization organize the huge amounts of data it collects each day, while also making sure its use and collection comply with all laws and regulations as well as current security standards. These best practices are essential for companies looking to use data in ways that can improve business processes while reducing risks and increasing productivity.
Often the term “Data Management” is used interchangeably with terms like Data Governance and Big Data Management however, the most formal definitions of this area focus on how an organization manages its information assets and data from end to the end. This includes the collection and storage of data, delivering and sharing of data as well as creating, updating and deleting data, and giving access to data for use in applications and analytics.
One of the most crucial aspects of Data Management is outlining a strategy for managing data prior to (for many funders) or in the initial months after (EU funding) the research study starts. This is crucial to ensure that the scientific integrity of the research is maintained and that the study’s findings are based on reliable data.
Data Management challenges include ensuring that users have the ability to locate and access the relevant information, especially when data is spread out across multiple storage spaces in different formats. Tools that integrate data from different sources are helpful, as are metadata-driven data such as data lineage records and dictionaries that show how the data originated from different sources. The data should be accessible to other researchers for reuse over time. This involves using interoperable file formats such as.odt and.pdf instead of Microsoft Word document formats and ensuring that all the necessary details needed to comprehend the data is captured and www.vdronlineblog.com documented.
