Issues Related To Maintaining Integrity Of Data Collection Data collection is known as one of the advantageous practices for extracting, collecting, formatting and storing data digitally. The main purpose of data collection is to ensure that an extracted information is accurately measured in a systematic manner in order to facilitate data analysis and help organizations with cataloging, processing and management of data. To ensure data credibility, companies consider to outsource data collection so as to reap tangent benefits such as- smart partnership, latest technology access, faster turn-around time and project implementation, low operational cost and stringent security measures. This is also imperative in terms of gaining competitive edge over competitors by analyzing customer’s opinion about their products and services. A proper data collection is crucial, otherwise chances are, errors could occur leading to enlisted consequences: Failing to properly answer research questions Failing to validate the study Inappropriate data results in wasted resources Could mislead other researchers to continue the research Could compromise decisions However, depending on the requirements consequences could vary. To ensure data integrity, begin with detecting errors in the data collection process, and analyze if they are random or are made intentionally. There are two defined ways to ensure data integrity: Quality assurance – it is done prior to beginning with data collection process. Quality control –it is done during and after completion of data collection process. Quality Assurance Since quality assurance heads data collection, it can prevent occurring of errors at initial stages, and can significantly help to maintain data’s credibility. This proactive measure involves creating a comprehensive and detailed manual for data collection to reduce the risks of listed failures:
Inability of knowing timings, methods, or identifying responsible persons for reviewing data. Incomplete or inaccurate list of the items to be collected Inappropriate description of data collection instruments to be used. Improper content or plan of action for training staff members.
This way, with defining complete procedure at initial stages quality assurance activity help businesses to conduct data extraction in a proper way. A deep analysis of the extracted data provides valuable insights of customer’s behavior, that later help companies to strategize their product development, launch and marketing activities. Quality Control Quality control is done during or after completion of data collection process. It involves detailed monitoring of the activities happening during and after data collection process. Once done, all the details are then documented in the procedures manual. This monitoring could be carried out as direct staff observation during conference calls or interviews or by reviewing data reports, in order to identify inconsistencies or invalid data collection. In quality control check, necessary measures are also determined in order to correct faulty data collection practices and to minimize future occurrences. But if, reports are vaguely written, it would become difficult to implement necessary steps. Some keys issues occurred with data collection includes:
Errors in listing data items Systematic errors Protocol not properly followed Staff Performance Misconduct
Companies providing data collection services, don’t rely on single information. They keep on query about the same information several times during surveys or interviews in different ways to ensure data quality. Measures of honest responses is crucial therefore, data quality should be checked for each individual measurement, individual observation, and for complete data set.