Data Quality Indicator

{
  "id": "MLC-T-ENV-007324",
  "term": "Data Quality Indicator",
  "field": "Environment",
  "definition": "1. Precision, bias, representativeness, completeness, comparability, and sensitivity.\n\n2. The quantitative statistics and qualitative descriptors that are used to interpret the degree of acceptability or utility of data to the user. The principal indicators of data quality are precision, bias, accuracy, representativeness, comparability, completeness, and sensitivity.\n\n3. A performance measure for sampling and analytical procedures; a quantitative measure of the achievement of data quality objectives; qualitative statistics and quantitative descriptors that are used to interpret the degree of acceptability or utility of data to the user. The principal data quality indicators are precision, accuracy, comparability, completeness, and representativeness. [EPA/240/R-02/008; QA Glossary]\n\n4. 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": "DQI",
  "subject": "Ecosystems",
  "references": [
    "Great Lakes: Quality Management Training - Data Review Glossary",
    "Forum on Environmental Measurements (FEM) Glossary",
    "Environmental Sampling and Analytical Methods (ESAM) Program Glossary",
    "EMAP Master Glossary"
  ],
  "url": "https://mlchart.com/terminology/environment/data-quality-indicator/"
}