Data management is a broad term that includes a variety of processes, tools, and techniques. They assist an organization manage the huge amount of data they gather every day while also making sure that their use and collection are in line with all applicable laws regulations, rules, and current security standards. These best practices are crucial for organizations who want to use data in a way that improves business processes while reducing risk and increasing productivity.
Often the term “Data Management” is used interchangeably with terms such as Data Governance and Big Data Management, but the most formal definitions of the issue focus on how an organization manages data and information assets from end to end. This covers collecting and storing data; delivering and sharing data as well as creating, updating and deleting data; and providing access to the data to use in analytics and applications.
One of the most important aspects of Data Management is outlining a strategy for managing data prior to (for many funders) or in the early months following (EU funding) a research study begins. This is crucial to ensure that scientific integrity is maintained and the findings of the study are founded on reliable and accurate data.
Data Management challenges include ensuring that users are able to find and access relevant information, particularly when data is spread across multiple storage spaces in different formats. Data dictionaries, data lineage records and tools that connect disparate sources of data are helpful. The data must be accessible to other researchers for reuse over time. This requires using interoperable formats such as.odt or.pdf instead of Microsoft Word www.vdronlineblog.com/docyard-document-management-software-reivew/ document formats, and ensuring that all information is gathered and documented.