Data management is the development and execution of architectures, policies, practices and procedures in order to manage the information lifecycle needs of an enterprise in an effective manner.

Data management is the process of controlling the information generated during a research project. Any research will require some level of data management, and funding agencies are increasingly requiring scholars to plan and execute good data management practices.

Managing data is an integral part of the research process. It can be challenging particularly when studies involve several researchers and/or when studies are conducted from multiple locations. How data is managed depends on the types of data involved, how data is collected and stored, and how it is used - throughout the research lifecycle.

The outcome of your research depends in part on how well you manage your data. Managing data helps you as a researcher organize research files and data for easier access and analysis. It helps ensure the quality of your research. It supports the published results of your work and, in the long term, helps ensure accountability in data analysis. Effective data management practices include:

• Designating the responsibilities of every individual involved in the study.
• Determining how data will be stored and backed up.
• Implementing the data management plan.
• Deciding how data will be dealt with through each modification of the study.
• This page will provide an overview of the following data management principles:
• Research Data - defines the materials covered in a data management plan
• Data Planning - outlines steps to take before beginning a research project
• Data Management - describes procedures for organizing and controlling research data
• Data Security - provides considerations for data access and long-term data stability
• Data Sharing - explains why sharing research data is important