Data Quality Objectives
Neptune and Company, Inc. provides data collection planning and environmental survey design assistance to multiple agencies and private clients. We were instrumental in the development of the data quality objective (DQO) process used by EPA and other agencies, and have applied this systematic planning to contaminated locations throughout the United States. We provide DQO facilitation and training at federal facilities throughout the country 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?
- State the Problem
- Identify the Goal of the Study
- Identify Information Inputs
- Define the Boundaries of the Study
- Develop the Analytic Approach
- Specify Performance or Acceptance Criteria
- 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 resources by making data collection operations more resource-effective.
- 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.