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3. Data Life Cycle

3.4 Quality

Whether it is a scientific advice, a discovery, an analysis or a report, its credibility is directly linked to the quality of the data used to produce it. Quality management is therefore a key component of the science data management process.

3.4.1 QA/QC


Quality assurance refers to actions beyond tests and controls, and aims at identifying and preventing non conformity problems. QA also includes training and audits.

Real-time data acquisition involves specific procedures due to the fact that data is being used as soon as it is produced. This presents a level of risk for users who could misinterpret data or could not recognize inaccuracies. As an example, the Integrated Ocean Observing System – IOOS has developed a series of automated procedures for real-time ocean data control (QARTOD: Quality Assurance for Real-Time Oceanographic Data).1


Quality control generally refers to activities aiming at products & services quality verification by looking at problems and abnormal elements. In the area of data management, data is examined in order to identify errors, missing data or acquisition problems (see: section 3.4.4 Quality Flagging).


Quality management represents a higher level of management e.g. organizational or institutional. ISO standard 9001 describes a quality management model including procedures, management of responsibilities, resources and services, as well as management of quality assessment, analysis and improvement.

Statistics Canada − an organization based on data – defines its quality management framework and the quality of its information products in terms of their fitness for use by clients. It is a multidimensional concept including the relevance of the information to how to make chronological order in essay users' needs, as well as its do my gis homework accuracy, timeliness, accessibility, interpretability and coherence.2