True information for economic reactivation in the pandemic context

Abstract

Large volumes of data result in a new computing paradigm where multiple workers or work nodes need to be orchestrated. The research analyzes the factors that can interfere or contribute so that the quality of the information is maintained within the different information systems or data sets of the universe of
data in a certain industry. The main method consists of studying the trace or footprint that Big Data systems leave and that contribute as evidence to be able to measure the rates, probability and relationships due to duplicity, total or partial absence of information, so that it does not affect the processes where use the same. This can be for forensic analysis, monetization, network security and other categories that the industry requires. Among the main results, four groups of complete information are observed, two of them are similar in shape, and the other two have extreme characteristics; that is, a very small group
and, on the far right, a very large group, where the process has remained consistent for a while. A set of eight samples from a total population of fifty-three information quality indicator variables is analyzed for a big data data set. These variables have presented important errors in the investigated sample, so it is determined
that the source is not 100% reliable given the error rates. This is due to problems in the process that generates the data set or the operational primary source.