Data Quality Objective

{
  "id": "MLC-T-ENV-007326",
  "term": "Data Quality Objective",
  "field": "Environment",
  "definition": "1. RAGS Volume I, Part A, Chapter 4 defines a DQO as \"qualitative and quantitative statements to ensure that data of know and documented quality are obtained during an RI/FS to support an Agency decision.\" DQOs are qualitative and quantitative statements specified to ensure that data of known and appropriate quality are obtained. The DQO process is a series of planning steps, typically conducted during site assessment and investigation, that is designed to ensure that the type, quantity, and quality of environmental data used in decision making are appropriate. The DQO process involves a logical, step-by-step procedure for determining which of the complex issues affecting a site are the most relevant to planning a site investigation before any data are collected.\n\n2. User-defined criteria established for each parameter to evaluate usability of data.\n\n3. \"Quantitative and qualitative statements of the overall level of uncertainty that a decision-maker is willing to accept in results or decisions derived from environmental data DQOs provide the statistical framework for planning and managing environmental data operations consistent with the data user's needs\" (QAMS 1993, 8). A data quality objective may include goals for accuracy, precision, and limits of detection. It may also include goals for completeness, comparability, and representativeness. Data quality objectives are established before sampling is begun and may influence the level of effort required to select a sample.\n\n4. Qualitative or quantitative statement derived from the DQO process that clarify study objectives, define the appropriate type of data, and specify tolerable levels of potential decision errors that will be used as the basis for establishing the quality and quantity of data needed to support the decisions.\n\n5. Qualitative and quantitative statements of the overall level of uncertainty that a decision-maker will accept in results or decisions based on environmental data. They provide the statistical framework for planning and managing environmental data operations consistent with user’s needs.\n\n6. Quantitative statistics and qualitative descriptors that are used to interpret the degree of acceptability or utility of data to the user. The principal data quality indicators are bias, precision, accuracy, comparability, completeness, and representativeness (QAMS 1993, 7).",
  "abbreviation": "DQO",
  "subject": "Policy and guidance",
  "references": [
    "Waste and Cleanup Risk Assessment Glossary",
    "Great Lakes Quality Mgmt Plan Glossary",
    "Environmental Modeling and Assessment Program (EMAP) Master Glossary",
    "EPA 2004",
    "EPA 2005b",
    "EMAP Master Glossary"
  ],
  "url": "https://mlchart.com/terminology/environment/data-quality-objective/"
}