Data Quality Objectives (DQO) help guide the process for formulating a problem, identifying the decisions to be made, specifying the quality requirements for the decisions—which lead to the quality requirements for the data—and finally developing a defensible sampling and analysis plan. This structured planning process can help lead to the collection of "good" environmental data to support decision-making.

Why Use the DQO Process?

The DQO process is a planning tool for data collection activities. It provides a basis for balancing decision uncertainty with available resources. The DQO process is required for all significant data collection projects within DOE's Office of Environment, Safety and Health, Corporate Performance Assessment, Quality Assurance Programs, EH-31, per the September 7, 1994 memo from Thomas P. Grumbly, Assistant Secretary for Environmental Management: "Institutionalizing the Data Quality Objectives Process for EM's Environmental Data Collection Activities."

Steps in the DQO Process

There are seven steps in the DQO process. Working through these steps, stakeholders capture the logic required to define the type, quality, and quantity of data needed for environmental decision making.

Guidance

• U.S. Environmental Protection Agency. 2006. Guidance on Systematic Planning Using the Data Quality Objectives Process. EPA/240/B-06/001, U.S. Environmental Protection Agency, Office of Environmental Information Washington, DC.
• The DQO Website was developed by Pacific Northwest National Laboratory for the U.S. Department of Energy's Office of Health, Safety and Security, Corporate Safety Analysis, Corporate Safety Programs, HS-31.