Data Management encompasses a wide range of tools, processes and techniques that assist an organization organize the massive amounts of data it collects 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 vital for organizations looking to utilize data to improve business processes while reducing risks and increasing productivity.
Often the term “Data Management” is used interchangeably with terms like Data Governance and Big Data Management, though more formal definitions of this subject are focused on how a company manages information assets and data from beginning to the end. This includes collecting and storing of data, delivering and sharing of data in the form of creating, updating, and deletion data and giving access to data for use in applications and analytics.
Data Management is a vital element of any research study. This can be completed 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 scientific integrity of the study is preserved, and that the study’s conclusions are based on reliable data.
The difficulties of Data Management include ensuring that end users are able to easily locate and access relevant data, particularly when the data is spread across multiple systems and storage locations that are in different formats. Data dictionaries, data lineage records and tools that connect disparate sources of information are beneficial. The data should also be available to other researchers for long-term reuse. This involves 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 collected and documented.
