Data management is a broad term that includes many techniques, tools, and techniques. These help an organization organize the massive quantities of data that they collect every day, while ensuring that their use and collection are in line with all applicable laws, regulations, and current security standards. These best practices are crucial for organizations seeking to leverage data in ways that can enhance business processes while reducing risks and boosting productivity.
Often the term “Data Management” is often used interchangeably with terms like Data Governance and Big Data Management, but the most formalized definitions of this topic concentrate on how an organization manages data and information assets from end to end. This encompasses collecting and storing data; delivering and sharing data in the form of creating, updating and deleting data; as well as providing access to the data to use in analytics and applications.
Data Management is a vital element of any research study. This can be completed prior to the start of the study (for many funders) or within the first few months (for EU funding). This is crucial to ensure that the integrity of the study is maintained and that the research findings are built on accurate and reliable data.
Data Management challenges include ensuring end users can find and access the relevant information, especially when data is spread across multiple storage spaces in different formats. Data dictionary, data lineage records and tools that integrate disparate sources of data are helpful. The data should also be available to other researchers for reuse in the future. This includes 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 captured and documented.
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