Data is a valuable asset for any organization, and to make the most of it, it must be managed properly. Data management standards are the guidelines that define how data is described and recorded. These standards are produced by consensus of subject matter experts and ratified by a standards authority such as the International Organization for Standardization (ISO) and the Federal Geographic Data Committee (FGDC).Data managers and data managers can help determine the appropriate data standards to use in a project. Researchers are responsible for implementing the use of data standards in their projects.
To share, exchange, combine and understand data, we must standardize format and meaning. Automation will need to be considered to monitor and enforce compliance. In this article, we will discuss the four main data management standards, how they can help increase research efficiency, and how to implement them in your organization. We will also cover data quality, data architecture, data governance, and more. The four main data management standards are Content Standard for Digital Geospatial Metadata (CSDGM), Common Data Elements (CDE), Data Quality (DQ), and Data Architecture (DA). The CSDGM is used to document dataset-level data standards in the Entity and Attribute Overview section of the metadata, while parameter-level data standards can be documented in Entity and Attribute Details. The CDE standard is also called the CDE 360º view.
It provides optimal conditions for data management across the organization. The DQ standard helps ensure that data is accurate, complete, consistent, and up-to-date. The DA standard describes data elements from a technological perspective and includes information such as logical data models, source and destination systems, table and field structures, and system dependencies. Data governance is an important part of any successful data management strategy. It helps strike a balance between data collection practices and privacy mandates.
It also resolves disputes between different business units over data definitions and formats. A typical responsibility for data stewards is to set up data quality metrics and track trends in data quality KPIs. Ideally, executives and other representatives of an organization's business operations should be involved in data governance efforts, as well as IT and data management teams. The program manager usually leads a data governance team that works on the program full time. This framework provides your organization with a holistic approach to collecting, managing, protecting and storing data. To summarize, effective data management requires an understanding of the four main standards: CSDGM, CDE, DQ, and DA.
Automation should be used to monitor and enforce compliance. Data governance should be implemented to ensure that data is accurate, complete, consistent, and up-to-date. Finally, executives and other representatives of an organization's business operations should be involved in the process.
