EQuIS Data Qualification Module (DQM) is an EQuIS Professional module for assisting environmental professionals with data validation tasks. DQM performs automated checks on a user-selected data set loaded in the EQuIS Database. DQM has a variety of laboratory audit and standard checks to assess data for holding time, blank contamination, surrogate recovery, precision, accuracy, and reporting limits. DQM users may select existing data checks, tailor existing checks, or build tailored data checks. DQM can add and modify data quality checks within the provided framework to meet client requirements and enable project-specific rules, quality assurance/quality control (QA/QC) limits, and data qualifiers. DQM allows data comparisons to tailored or calculated criteria.
Once DQM is run, users can review any data exceptions noted by DQM and perform further assessment of the data. A summary-level report, an exceptions report by check, and a report containing all analytical results and associated qualifiers in the data set can be exported to an Excel spreadsheet.
DQM includes additional reports, an EDD format, and requires the EQuIS DQM Schema.
The Data Qualification Module is organized around data quality checks that can be selected to run against a data set. Any or all of the checks can be selected to run against the data set. Each check has one or more rules that are applied to each result record in the data set to determine if a data qualifier and/or reason code should be applied to that record. Each rule has its own qualifier, but more than one rule can have the same qualifier. Each check also has a set of parameters, which allows DQM customization without changing any code.
When using DQM, the qualification rules for the project Quality Assurance Project Plan (QAPP) or Sampling and Analysis Plan (SAP) will need to be tailored by setting up a corresponding DQM QAPP. For the creation of a DQM QAPP, review the project documentation to determine the project requirements for each of the DQM checks. Configuration of DQM checks, rules and parameters must be completed for each project QAPP prior to performing data validation in DQM. See Getting Started with DQM for information and instructions on how to install and configure DQM.
Each data validation effort is performed as a distinct event in DQM. A DQM event is a review of analytical results dataset(s) that are run through any or all of the automated checks for the selected DQM QAPP. Data validators then review the DQM generated flags by check, sample, or other filters. Any in-process events can be saved for future reference and can be edited at a later time. These changes are stored in a variety of DQM tables in the EQuIS Database but changes to other data tables does not occur until the DQM event is complete. Finally, completing a DQM event saves the qualified updates to the DT_RESULT table and moves the event to the history list.
Check – EQuIS DQM is organized around data quality checks, to which any data set may be compared. One or more checks can be selected to run against the data set at one time. A check is a group of one or more rules. There are a variety “out-of-the-box” checks available for use in DQM (see DQM Checks ), and each of the checks may be modified to desired specifications.
DQM Event – Each data validation effort is performed as a distinct event in DQM, which entails selection of analytical results data set(s) that are run through any or all of the automated checks for the selected DQM QAPP and is followed by a user review of the DQM generated flags.
Parameter – Each rule has a set of parameters. Parameters make up the elements used in each check and allow for each check to be tailored for different analytes or methods.
Qualifier – Data qualifiers are flags assigned to laboratory analytical results by the laboratory and/or the data reviewer when QA/QC criteria are not met and are intended to assist the data user to use the data appropriately in a manner consistent with project objectives. Within DQM, the qualifier is the value applied to the data set if a rule is violated. Each rule has its own qualifier, but more than one rule can have the same qualifier.
Quality Assurance Project Plan (QAPP) – A written document that provides a framework for environmental data collection, which will meet speciﬁc project objectives and includes procedures to obtain data of known and adequate quality. Basic QA/QC elements should be addressed in the QAPP, including: precision, accuracy, sensitivity, representativeness, completeness, and comparability.
Reason Code – Provides a short reference to a longer description of why that qualifier has been added to a result.
Rule – Each check has one or more individual rules that are used to compare to each result record in the data set and used to assign qualifiers.
Sample Delivery Group (SDG) – An SDG is a group of samples sent to a laboratory for analysis in a single consignment. This is distinct from a laboratory batch.
Copyright © 2021 EarthSoft, Inc • Modified: 16 Jul 2021