Data management is broad term that covers different methods, tools, and techniques. These help an organization organize the massive amounts of data they collect every day, while also ensuring the collection and use of data is in accordance with all laws, regulations, and current security standards. These best practices are vital for organizations that want to use data in a manner that improves the efficiency of business processes while reducing risk and enhancing productivity.
Often the term “Data Management” is used interchangeably with terms like Data Governance and Big Data Management, but the most formalized definitions of this issue are focused on how a company manages data and information assets from end to the end. This includes storing and collecting of data, sharing and distributing of data, creating, updating and deleting data, and giving access to data for use in analytics and other applications.
Data Management is a vital aspect of any research study. It can be done prior to the start of the study (for many funders), or within the first few months (for EU funding). This is essential to ensure that the integrity of the research is maintained and that the conclusions of the study are based on accurate and reliable data.
Data Management challenges include ensuring that end users can locate and access relevant information, especially when data is spread across multiple systems and storage facilities in various formats. Tools that connect disparate data sources are helpful and so are metadata-driven data dictionary and data lineage records that can show how the data originated from different sources. The data should also be available to other researchers for long-term reuse. This involves using interoperable formats such as.odt or.pdf instead of Microsoft Word document formats, and making sure that all the information needed is recorded and documented.
