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Top1. Introduction
Big Data is characterized as a large data set, which is challenging to store, process, analyze, and understand using traditional database processing tools (S. Huang & Chaovalitwongse, 2015). The advancement in the use of Big Data has driven a generation of technologies and architectures designed to extract economic value through analysis (Gantz & Reinsel, 2011). Thus, decisions oriented to data analysis stand out compared to decisions based on intuition (McAfee & Brynjolfsson, 2012). With the increase in the generation of unstructured data, the analysis and interpretation through structured tables with rows and columns becomes increasingly complex.
A Big Data project, in its turn, can be defined as a data-intensive project that presents large-scale problems with restrictions of volume, variety, speed (Becker, 2017), and still veracity of the data (Barham & Daim, 2018). Therefore, the culture of data-driven decision-making is essential to the success of a Big Data project (Dutta & Bose, 2015). Big data projects, using cutting-edge analysis involving artificial intelligence and machine learning, condition computers to identify what these data represent by identifying patterns more assertively.
Decision-making based on data depends on the successful implementation of Big Data projects (Dutta & Bose, 2015). However, the excessive focus on data and the neglect of adequate decision-making has been evidenced as an aspect that reflects the failure of Big Data projects (Chiheb, Boumahdi, & Bouarfa, 2019). Big Data project characteristics reflect potential highly uncertain inputs (for example, the relevance of data) and also potentially highly uncertain results (for example, information may be inaccurately derived from data analysis) (J. Saltz, Shamshurin, & Connors, 2017). Big Data projects have also provided a good discussion regarding success factors. In a broad perspective, there is a convergence between studies that investigated success factors in big data projects and that encompass five predominant characteristics: Governance, Objectives, Processes, Data, Team, and Tools (Gao, Koronios, & Selle, 2015; J. S. Saltz & Shamshurin, 2016).
Despite the evolution made in the Big Data area in recent years, practitioners reinforce the need for research related to guides, models, or even methodologies (Lara, De Sojo, Aljawarneh, Schumaker, & Al-Shargabi, 2020). As a result, opportunities arise for project management professionals to contribute by identifying appropriate measures and types of data, helping to interpret the data, and placing them in an evaluation context (Olsson & Bull-Berg, 2015). In addition to identifying technological trends in specific fields (Abbas, Bilal, Zhang, & Khan, 2015), patent analysis can identify recurring problems in a specific area, as patent registrations are intended to solve a practical problem.
Previous patent analysis studies of Big Data technology have been performed. Examples include abstract analysis of Chinese Big Data patents (Z. Yang et al., 2017), hot classification fields of Big Data technology (Gui, Liu, Bai, & Zhang, 2017), methods technology assessment using quantitative patent analysis for technology transfer in Big Data marketing (Jun, Park, & Jang, 2015), business interests and activities around Big Data (Y. Huang et al., 2016) and an analysis of the patenting activities of global jurisdictions in the Big Data field (Saheb & Saheb, 2020). While these studies analyze Big Data patents, they do not directly address the specific area of Big Data projects.
Singh (2019) stated that Big Data projects involve research in purely technical areas, such as ubiquitous information and integration in Big Data ecosystems. Thus, given the relevance of Big Data projects and the scarcity of studies characterizing the patents generated in this area, we took this as an opportunity for new research. Herein, an exploratory analysis of the Espacenet patent database was performed on January 10, 2021. to identify and subsequently describe registered Big Data projects patents. based on an initial sample of 109 records.