Data Management encompasses a broad range of tools, processes and techniques that help an organization organize the massive amounts of data it accumulates each day, while also making sure that its collection and use conform to all laws and regulations as well as current security standards. These best practices are essential for organizations who want to utilize data in a manner that enhances business processes while reducing risk and increasing productivity.
Often the term “Data Management” is often used in conjunction with terms such as Data Governance and Big Data Management, but the most formal definitions of the topic concentrate on how an organization manages information assets and its data from end to the very end. This encapsulates the collection and storage of data; sharing and distributing data by creating, updating, and deleting data; as well as giving access to the data to be used in analytics and applications.
One of the most important aspects of Data Management is outlining a data management strategy before (for many funders) or during the initial months after (EU funding) the study is launched. This is essential to ensure that the integrity of research is maintained and that the findings of the study are supported by reliable and accurate data.
Data Management challenges include ensuring that users have the ability to locate and access relevant information, especially when data is spread out across multiple systems and storage locations in various formats. Tools that integrate data from different sources are helpful as are metadata-driven dictionary and data lineage records which can reveal how the data originated from various sources. Another issue is ensuring that the data is accessible for long-term re-use by other researchers. This involves using interoperable formats such as.odt or.pdf instead of Microsoft Word document formats, and ensuring all necessary information is gathered and documented.