BIG DATA IMPLEMENTATION LEVELS IN ORGANIZATIONS: A THEORETICAL PROPOSAL

The evolution in the data and information management has led to a change in the way organizations make data-driven decisions, given the increase in the robustness and complexity in the data generation in this era governed by social networks, intelligent devices, automation, virtual assistants and other technologies that have changed how we communicate, work and live. To face these challenges, organizations need an understanding of the impact that Big Data and their related technologies have on their processes, how they affect organizational decision-making, and what methods, techniques, and human factors have emerged from the Information Systems (IS), Big Data, Data Science, and Industry 4.0 perspective to face these challenges. For this reason, this paper will present some factors that organizations must take into account to get the resources, capabilities, and skills required to extract value from Big Data. Taking these factors as a starting point we propose different Big Data implementation levels and its associated technologies in an organization. Later, this proposal may serve as a basis to generate a measurement scale of Big Data implementation or to derive criteria selection for case studies of Big Data application, contributing this way to go further in Big Data theory.