Data management is broad term that covers a variety of processes, tools, and techniques. These help an organization organize the enormous amount of data they accumulate every day, while also ensuring the collection and use of data is in accordance with all applicable laws regulations, rules, and current security standards. These best practices are essential for companies that wish to utilize data in a way that enhances business processes while reducing risk and enhancing productivity.
Often, the term “Data Management” is used interchangeably with terms such as Data Governance and Big Data Management, however the most formal definitions of the topic concentrate on how an organisation manages its information assets and data from end to the very end. This encompasses collecting and storing data; sharing and distributing data by creating, updating, and deleting data; as well as giving access to the data to use in applications and analytics processes.
One of the most crucial aspects of Data Management is outlining a strategy for managing data prior to (for many funders) or in the early months after (EU funding) the research study starts. This is vital to ensure that the scientific integrity of the research is maintained and to ensure that the study’s findings are based on accurate data.
The difficulties of Data Management include ensuring that end users are able to easily locate and access relevant data, especially when the data is spread across multiple storage locations that are in different formats. Data dictionary, data lineage records and other tools that integrate different sources of data are helpful. The data must also be available to other researchers for long-term reuse. This includes using interoperable formats like as.odt or.pdf instead of Microsoft Word document formats, and making sure that all the information needed is documented and recorded.