Opportunities of big data management in libraries and information center: Structural-interpretive analysis and finding a solution

Big Data is a collection of massive and complex data sets and data volume that has characteristics, such as valence, validity, value, variability, variety, velocity, veracity, visualization, volatility, and volume. These features of big data make it difficult to process and manage with a typical database, but extracting value from it can improve many organizational and non-organizational processes, although it may cost and requires the use of modern ICT infrastructure and techniques. The aim of this research was to identify, determine, prioritize and analyze the opportunities of big data; to analyze structural-interpretation of it; and to suggest solutions for managing big data in libraries and information centers in Tehran. The methodology of this applied research was descriptive survey and the total number of chief managers of central libraries of public universities in Tehran was considered as its research population (35 chief managers). The data were collected through library studies and questionnaire. In fact, previous studies and the documentary research method were used to review the fundamental opportunities of big data in order to design the questionnaire based on the research model. Data was analyzed through descriptive and inferential statistical methods by the SPSS and the Smart PLS softwares. Finally, the second questionnaire which was designed based on Interpretive Structural Modeling (ISM). It was distributed among 15 experts in the field of big data and analyzed manually. The results showed that from the viewpoints of managers of libraries and information centers in Tehran, opportunities of big data management are as follows in order of importance: managerial-organizational, essential, procedural and human resources. However, according to the experts' opinions, the opportunities of big data were as follows: essential, procedural, human resources and managerial-organizational. One of the main reasons for this difference was that managers were more focused on managerial-organizational issues and they had less familiarity with big data; while experts, with a more understanding of the concept of big data, focused on essential opportunities, and the managerial-organizational opportunities were less important from their viewpoint.