Selection of the best Big Data platform using COBRAC-ARTASI methodology with adaptive standardized intervals

Abstract

The advanced technologies emerging in Industry 4.0 are forcing companies in different industries to review their business models and become more compatible with advanced technological practices. While traditional business models are increasingly inadequate in the face of increasing competition, business models developed thanks to advanced technologies such as deep learning and machine learning have begun to replace them. However, developing business intelligence and intelligent applications using these technologies requires more data processing. In this context, Big Data technology is a unique instrument in providing the data businesses need to design more intelligent systems. In conclusion, the Big Data platform can significantly speed up the processes of structuring and processing the data and information generated and increase businesses' efficiency, performance, and agility. However, being a relatively new concept, the knowledge about the Big Data concept is limited, leading to several challenges for decision-makers concerning choosing the appropriate platform. Also, the number of studies on this subject is highly scarce. Hence, practitioners in various industries lack sufficient support from the research society on this issue. We could not find crisp and definite values to evaluate the BD alternatives despite comprehensive investigation. In that regard, as data, we addressed appraisals and opinions of IT professionals with vast knowledge and experience in assessment, selection, installation, and operation. We developed a novel decision-making model to evaluate and select the most proper BD platforms by processing these data. In this connection, the current investigation suggests a novel, robust, practical decision-making model for defining the combination of the weight of criteria based on pairwise comparisons of adjacently ranked criteria (COmparisons Between RAnked Criteria- COBRAC) and the ARTASI (Alternative ranking technique based on adaptive standardized intervals -ARTASI). It can handle complex ambiguities encountered in appraisal processes to address the Big Data platform selection problem. In addition, the current work developed a negotiation process quantitificated to determine the influential criteria affecting the selection of the Big Data platform. When we evaluate the outcomes of the suggested model, the most influential criterion affecting the selection of the Big Data platform is C12 "Ease of Use." in addition, the most suitable Big Data platform for large-scale enterprises has been identified as A3 Microsoft SQL Server. The proposed model and its results have been validated based on extensive sensitivity and comparison analysis. These results also offer practical and managerial implications for the industry. Although many studies indicate that installation cost and speed are the most critical factors, this research found that, unlike these studies, ease of use is the most critical factor in choosing a BD platform. In this context, the BD alternative that provides the highest ease of use can produce more efficient results and reduce complexities in collecting and processing high volumes of structured and unstructured data.