Data management is broad term that covers various techniques, tools, and techniques. They assist an organization organize the massive amount of data they gather every day, while ensuring their collection and usage is in compliance with all laws and regulations as well as current security standards. These best practices are vital for companies looking to use data to enhance business processes while reducing risks and boosting productivity.

Often, the term «Data Management» is often used interchangeably with terms such as Data Governance and Big Data Management however, the most formal definitions of this topic focus on how an organization manages information assets and its data from beginning to the very end. This includes the collection and storage of data, sharing and distributing of data as well as creating, updating and deleting data as well as giving access to data analysis and application.

One of the most crucial aspects of Data Management is outlining a plan for managing data prior to (for many funders) or in the early months following (EU funding) the research study starts. This is essential to ensure that the integrity of the research is maintained and the findings of the study are founded on reliable and accurate data.

The challenges of Data Management include ensuring that end users are able to easily locate and access relevant information, particularly when the data is spread across multiple storage locations with different formats. Tools that connect disparate data sources are helpful as are metadata-driven linesage records and dictionaries which can reveal the source of the data from various sources. Another concern is making sure that the data is utilized for re-use by other researchers. This means using interoperable file formats such as.odt and.pdf instead of Microsoft Word document formats and making sure that all the details needed to comprehend the data is collected and documented.

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