Purpose – Only limited attention has been paid to the issue of Measurement Data Quality (MDQ) in a metrology context. To address this critique of the literature a methodology to assure MDQ was proposed. Methodology – The study proposes a methodology which consists of four steps can be used to 1 identify the importance of a measurement (identification), 2 determine accuracy and precision (determination), 3 evaluate the criticality of the measurement to its impact on the final result (evaluation) and 4 record the facts that influenced the decision making process (documentation). Findings –When followed and properly documented, these four steps can help ensure our measurements are valid and worthwhile. Identifying the important measurements that are made, determining the level of accuracy required and then using the proper tools to make the measurements will yield valid, useful results.
[1]
Jie,et al.
Modeling data quality for risk assessment of GIS
,
2008
.
[2]
Ying Su,et al.
Assuring Information Quality in Sharing Platform for Disaster Management
,
2009,
J. Softw..
[3]
Stuart E. Madnick,et al.
Improving data quality through effective use of data semantics
,
2006,
Data Knowl. Eng..
[4]
Diane M. Strong,et al.
Beyond Accuracy: What Data Quality Means to Data Consumers
,
1996,
J. Manag. Inf. Syst..
[5]
N. Radziwill,et al.
Foundations for Quality Management of Scientific Data Products
,
2006
.