Data management is broad term that covers different methods, tools, and techniques. They help organizations manage the huge amount of data they accumulate every day while also making sure the collection and use of data is in accordance with all applicable laws, regulations, and current security standards. These best practices are vital for organizations that want to use data in a manner that improves 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 this area are focused on how a company manages data and information assets from end to the very end. This includes collecting and storing data; delivering and sharing data by creating, updating, and deleting data; as well as providing access to the data to be used in analytics and applications.
Data Management is a vital element of any research study. This can be done 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 research is maintained and that the findings of the study are based on accurate and reliable data.
Data Management challenges include ensuring that users are able to find and access the relevant information, especially when data is spread across multiple systems and storage facilities in various formats. Tools that integrate data from different sources are useful as are metadata-driven data linesage records and dictionaries that can show how the data came from different sources. Another challenge is ensuring that the data is accessible for long-term re-use by other researchers. This includes using interoperable file formats such as.odt and.pdf instead of Microsoft Word document formats and making sure that all the information required to understand the data is recorded and documented.