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Data collection ordata gathering is the process of gathering andmeasuringinformation on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes.Data collection is aresearch component in all study fields, includingphysical andsocial sciences,humanities,[2] andbusiness. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same. The goal for all data collection is to capture evidence that allowsdata analysis to lead to the formulation of credible answers to the questions that have been posed.
Regardless of the field of or preference for defining data (quantitative orqualitative), accurate data collection is essential to maintain research integrity. The selection of appropriate data collection instruments (existing, modified, or newly developed) and delineated instructions for their correct use reduce the likelihood oferrors.
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Data collection andvalidation consist of four steps when it involves taking acensus and seven steps when it involvessampling.[3]
A formal data collection process is necessary, as it ensures that the data gathered are both defined and accurate. This way, subsequent decisions based on arguments embodied in the findings are made using valid data.[4] The process provides both a baseline from which to measure and in certain cases an indication of what to improve.
Data management platforms (DMP) are centralized storage and analytical systems for data, mainly used inmarketing. DMPs exist to compile and transform large amounts ofdemand and supply data into discernible information. Marketers may want to receive and utilize first, second and third-party data. DMPs enable this, because they are the aggregate system ofDSPs (demand side platform) andSSPs (supply side platform). DMPs are integral for optimizing and future advertising campaigns.
The main reason for maintainingdata integrity is to support the observation of errors in the data collection process. Those errors may be made intentionally (deliberatefalsification) or non-intentionally (random orsystematic errors).[5]
There are two approaches that may protect data integrity and secure scientific validity of study results:[6]
QA's focus is prevention, which is primarily a cost-effective activity to protect the integrity of data collection. Standardization of protocol, with comprehensive and detailed procedure descriptions for data collection, are central for prevention. The risk of failing to identify problems and errors in the research process is often caused by poorly written guidelines. Listed are several examples of such failures:
There are serious concerns about the integrity of individual user data collected bycloud computing, because this data is transferred across countries that have different standards of protection for individual user data.[7] Information processing has advanced to the level where user data can now be used to predict what an individual is saying before they even speak.[8]
Since QC actions occur during or after the data collection, all the details can be carefully documented. There is a necessity for a clearly defined communication structure as a precondition for establishing monitoring systems. Uncertainty about the flow of information is not recommended, as a poorly organized communication structure leads to lax monitoring and can also limit the opportunities for detecting errors. Quality control is also responsible for the identification of actions necessary for correcting faulty data collection practices and also minimizing such future occurrences. Ateam is more likely to not realize the necessity to perform these actions if their procedures are written vaguely and are not based on feedback or education.
Data collection problems that necessitate prompt action:
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