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Data Quality
Objectives
Data Quality Objectives (DQOs) are qualitative and quantitative statements derived from the outputs of the first six steps of the DQO process that: clarify the study objective, define the most appropriate type of data to collect, determine the most appropriate conditions from which to collect the data, and specify tolerance limits on the data used to make decision. DQOs are not restatements of laboratory limits for precision and accuracy, but statements that result from the DQO process.
The seven steps of the DQO process are:
1- State the problem - concisely describe the problem to be studied,
2- Identify the decision - identify what the study will resolve,
3 - Identify the inputs to the decision - identify the information that is needed,
4 -Define the boundaries of the study - specify time periods and spatial areas,
5 - Develop a decision rule - define the statistical parameter of interest,
6 - Specify tolerable limits on decision errors - define the decision maker's error limits, and
7 - Optimize the design for obtaining data - generate alternative data collection designs.
By following the DQO process, one can improve the effectiveness, efficiency, and defensibility of decisions in a resource-effective manner. The systematic approach to ensuring quality also used for developing Quality Assurance Project Plans (QAPjP).
In 1983, the EPA published Interim Guidelines and Specification for Preparing Quality Assurance Project Plans, QAMS-005/80. QAMS/80 specified the 16 elements that should be considered for a quality assurance project plan (QAPjP) and served industry as a check-list of items that should be considered during project planning. Although required only under CERCLA, QAMS/80 was adopted by numerous States and programs as the definition of environmental quality assurance and quality control (QA/QC). Frequently, however, QAPjPs became "boiler-plate" documents that presented data quality objects as laboratory control limits and statements such as "typically completeness is better than 85%". Quality was added to a project by performing data validation after the project was finished.
"Quality" is not something that can be added after a project is complete. It is something that comes from understanding project goals and proper planning. Recent guidance (Guidance for the Data Quality Objective Process, EPA QA/G-4) makes it clear that quality is added to a project by a process that includes understanding how decisions will be made and what those decisions are. The EPA is in the process of preparing several other guidance documents as an integral part of the principles of Quality Management. One of these documents (Guidance for Quality Assurance Project Plans) is a long anticipated replacement for QAMS/80. If used properly, these documents will allow you to achieve the necessary level of quality for your project, without incurring additional cost for un-needed data.
Information on the DQO process and other QA/QC documents is available on the world wide web.
Guidance For The Data Quality Objective Process, EPA QA/G-4 (epaqag4.pdf)
Information on the Quality Assurance Project Plans is available on the world wide web.
These document are in PDF format. If you need a viewer for PDF files,
The EPA provides a search engine for documents viewing and online ordering service.
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Last update: April 5, 2005.