There are four phases of the Data Life Cycle: planning, implementation, assessment, and reporting. The Data Life Cycle illustrates how data are generated and used.
In the planning stage, prospective data users decide what type, quantity, and quality of data will be needed to serve their needs.
The planning stage begins with the Data Quality Objectives (DQO) Process, a systematic planning process based on the scientific method that helps investigators define the problem to be investigated; the constraints and limitations of the investigation; and the type, quantity, and quality of the data needed. Investigators also use the DQO Process to develop a sampling design for collecting the data. The outputs of the DQO Process and the resulting sampling design are documented in the Quality Assurance Project Plan (QAPP). The QAPP also details the management authorities, personnel, schedule, policies, and procedures for the data collection event. Where possible, the QAPP incorporates Standard Operating Procedures (SOPs), which ensure that data are collected using approved protocols and quality measures. Some sample QA materials are provided. It is useful to include random spiked samples with field samples as a form of routine testing of laboratory QA/QC; you want to know how well the laboratory performs when you haven't told it you are doing a QA check!
In the implementation stage, data are collected according to the methods and procedures documented in the QAPP. During the data collection event, technical assessments (TAs) are conducted to assess whether or not data are being collected as stated in the QAPP; these assessments also generate QA/QC data that accompany the results during the assessment phase.
In the assessment stage, analysts use technical knowledge and statistical methods to determine whether or not the collected data meet the user's needs. The data are verified and validated to ensure that the measured values are free of gross errors due to procedural or technical problems. Investigators may then analyse the data using the Data Quality Assessment (DQA) Process, which determines whether or not the data meet the user's performance criteria as stated in the outputs of the DQO Process. Next, investigators examine the results of the DQA Process and develop scientific conclusions to the problem.
In the reporting phase, the data collected by the study are reported with all the relevant quality assurance and quality control (QA/QC) data so that decision makers can judge the quality of scientific information available to support their decisions. Reporting also helps the future users of the data determine whether and how these data might be applied to additional studies or in different contexts.