Neptune was instrumental in the development of the data quality objective (DQO) process now used extensively by EPA and other agencies. We have applied this systematic planning to contaminated locations throughout the United States. For multiple agencies and private clients, we have also provided DQO facilitation and training in support of major quality assurance programs.

What is the DQO Process?

The DQO process is a seven-step strategic planning approach based on the scientific method that is used to prepare for a data collection activity. It provides a systematic procedure for defining the criteria that a data collection design should satisfy, including when to collect samples, where to collect samples, the tolerable level of decision errors for the study, and how many samples to collect. The DQO process should be used during the planning stage of any study that requires data collection before the data are collected.

What are the Seven Steps?

1. State the Problem

2. Identify the Goal of the Study

3. Identify Information Inputs

4. Define the Boundaries of the Study

5. Develop the Analytic Approach

6. Specify Performance or Acceptance Criteria

7. Develop the Plan for Obtaining Data

Why Use It?

• Helps to assure that the type, quantity, and quality of data used in decision making will be appropriate for the intended application.

• Saves money and time by making data collection operations more resource-effective and reducing the need for additional sampling.

• Provides a convenient way to document activities and decisions, making it easy to communicate the data collection design to other project participants and stakeholders.

• Enables data users and relevant project experts to participate in the data collection planning process to specify their specific needs prior to collecting data.

• Encourages the clarification of vague objectives.

• Can be applied to any project, regardless of size.