Data Management covers a broad variety of tools, processes and techniques that assist an organization organize the massive amounts of data it accumulates every day, while making sure that its collection and use are in line with all regulations and laws, and current security standards. These best practices are crucial for companies looking to use data to improve business processes while reducing risks and increasing 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 this topic focus on how an organization manages information assets and its data from end to the end. This encapsulates the collection and storage of data; delivering and sharing data; creating, updating and deleting data; and giving access to the data to use in analytics and applications.
One of the most crucial aspects of Data Management is outlining a data management strategy before (for many funders) or in the initial months after (EU funding) a research study begins. This is vital to ensure that the integrity of the research is maintained and the results of the study are supported by reliable and accurate data.
The difficulties of Data Management include ensuring that end users can easily find and access relevant data, especially when the data is spread across multiple systems and storage facilities in various formats. Tools that can connect disparate data sources are helpful, as are metadata-driven data dictionaries and data lineage records that can show how the data came from different sources. Another challenge is ensuring that the data can be used for a long-term reuse by other researchers. This requires using interoperable document formats like as.odt and.pdf instead of Microsoft Word document formats and ensuring that all the necessary details needed to comprehend the data is collected and documented.
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