Data management is broad term that encompasses a variety of methods, tools, and techniques. These help an organization organize the vast quantities of data that they collect every day while also making sure their collection and usage is in compliance with all applicable laws and regulations as well as current security standards. These best practices are crucial for businesses looking to harness data to improve business processes while reducing risks and enhancing productivity.
Often, the term «Data Management» is often used interchangeably with terms such as Data Governance and Big Data Management, but the most formal definitions of the subject focus on how an organization manages information assets and data from end to the end. This encompasses collecting and storing data; sharing and delivering data; creating, updating and deleting data; as well as providing access to data to use in analytics and applications.
Data Management is a vital element of any research study. This can be accomplished before the study starts (for many funders) or within the first few months (for EU funding). This is essential to ensure that the integrity of the research is maintained and that the conclusions of the study are built on accurate and reliable data.
The difficulties of Data Management include ensuring that end users are able to easily locate and access relevant data, particularly when the data is spread across multiple storage locations in different formats. Tools that can integrate data from different sources are helpful as are metadata-driven such as data lineage records and dictionaries that show how the data came from different sources. The data should also be available to other researchers for reuse in the future. This includes using interoperable formats such as.odt or.pdf instead of Microsoft Word document formats, and ensuring all necessary information is documented and recorded.
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