| dc.description.abstract | Big data is a revolution in terms of data storage. Such data allow for several analyzes, possibilities for generating valuable insights and to support decision-making, however, the vast majority of these analyzes still have a high failure rate or low profitability when observing the amount invested versus the results obtained. Considering this scenario, the present work has as the main objective to analyze how thick data combined with big data can be used to make the analyzes more assertive. In summary, thick data intends to understand human behavior and how the human relationship with a particular product or service evolves over time. Understanding such issues allow more accurate extraction of data, the execution of better analyzes and, consequently, tends to generate better results. To conduct the research, the Design Science Research (DSR) research method was used. The paper presents the proof of concept of the model developed as well as two evaluations to observe the viability of the model, one theoretical using two cases from the literature and another practical based on Twitter data and Federal Government data about Covid-19. The main result obtained from this work is to demonstrate the possibility of combining thick data with big data to achieve more comprehensive data analysis. Furthermore, among the main contributions of this research can be cited the artifact generated, that is, the model that adds thick data to a big data structure, with a focus on using qualitative data together with quantitative data | en |
