Ten practical questions to improve data quality

Authors: McCord, Sarah; WELTY, JUSTIN - Us Geological Survey (USGS); COURTWRIGHT, JENNIFER - Utah State University; DILLON, CATHERINE - New Mexico State University; TRAYNOR, ALEX - Bureau Of Land Management; BURNETT, SARAH - Bureau Of Land Management; Courtright, Ericha; FULTS, GENE - Natural Resources Conservation Service (NRCS, USDA); KARL, JASON - University Of Idaho; Van Zee, Justin; WEBB, NICHOLAS - New Mexico State University; TWEEDIE, CRAIG - University Of Texas - El Paso

Submitted to: Rangelands
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/7/2021
Publication Date: 8/24/2021
Citation: McCord, S.E., Welty, J., Courtwright, J., Dillon, C., Traynor, A., Burnett, S., Courtright, E.M., Fults, G., Karl, J.W., Van Zee, J.W., Webb, N., Tweedie, C. 2021. Ten practical questions to improve data quality. Rangelands. 44(1):17-28. https://doi.org/10.1016/j.rala.2021.07.006.
DOI: https://doi.org/10.1016/j.rala.2021.07.006

Interpretive Summary: High quality monitoring data are key to data-supported decision making and adaptive rangeland management. This paper presents ten QA&QC questions that scientists and managers can address to ensure data quality and thereby increase the efficacy of monitoring. Given the expense of collecting and managing rangeland data, improving data quality workflows will reduce the frequency of costly errors and ensure that monitoring data are fit for use in decision making and in rangeland research and modeling. Research and monitoring programs can improve data quality by thoroughly describing the data ecosystem, clearly defining roles and responsibilities, adopting appropriate data collection and data management strategies, identifying sources of error, preventing those errors where possible, and describing sources of measurement variability. Ensuring data quality is an iterative process and improves through adaptive management of monitoring programs. The QA&QC questions posed in this paper apply to all members of the rangeland community and all data collected in experimental studies, inventories, short-term monitoring, and long-term monitoring programs. We encourage interagency and interdisciplinary partnerships to discuss these questions early so that data quality is ensured as a collaborative process. Improving data quality will improve our ability to detect condition, pattern, and trends on rangelands, which are needed to inform research and adaptive management.

Technical Abstract: High quality monitoring data are critical to supporting adaptive management, however concrete, cost-saving steps to ensure data quality are often poorly defined and understood. Data quality is more than data management. Ensuring data quality requires clear communication between team members, appropriate sample design, training of data collectors, data managers, and data users, observer and sensor calibration, and active data management. Quality assurance and quality control processes help rangeland managers and scientists identify, prevent, and correct errors in monitoring data. We present 10 guiding data quality questions to help managers and scientists identify appropriate workflows to improve data quality by describing the data ecosystem, creating a data quality plan, identifying roles and responsibilities, building data collection and data management workflows, training and calibrating data collectors, detecting and correcting errors, and describing sources of variability. Iteratively improving rangeland data quality is a key part of adaptive monitoring.