Effective and efficient usage of big data analytics in public sector

Abstract

Purpose This study aims to achieve three goals: present a holistic, flexible and dynamic model; define the model’s factors and explain how these factors lead to effective and efficient usage of big data; and generate indexes based on experts’ input to rank them based on their importance. Design/methodology/approach This paper uses the analytic hierarchy process, a quantitative method of decision-making, to evaluate the importance of the factors presented in the model. The fundamental principle of the overall model is that of a dynamo which is borrowed from electromagnetic physics. The model is also based on three IS theories. Findings Technological advancements and data security are among the most important factors that may impact the effectiveness and efficiency of big data usage. Authentication, governments’ focus on it and transparency and accountability are the most important factors in techno-centric, governmental-centric and user-centric factors, respectively. Research limitations/implications The findings of this paper confirmed earlier findings in the literature and quantitatively assessed some of the factors that were conceptually presented. This paper also presented a framework that can be used in future studies. Practical implications Policy and decision-makers may need to upgrade pertinent technologies such as internet security, frame policies toward information technology (IT) and train the users. Originality/value This paper fills a gap in the literature by presenting a comprehensive study of how different factors dynamically contribute to the effective usage of big data in the public sector. It also quantitatively presents the importance of the factors based on the data collected from 12 IT experts.

Building SMEs’ resilience in times of uncertainty: the role of big data analytics capability and co-innovation

“…Thus, CI can be activated through BDA capability that actualizes colearning activities in a domain of knowledge characterized by the intensive and shared use of data, managing digital tools and technological platforms among and between companies, users, customers and other socioeconomic actors to exchange and integrate resources, ideas and information for cooperative innovating (Saragih and Tan, 2018). In this logic, BDA capability helps in sharing knowledge, especially through data integration and data analysis (Merhi and Bregu, 2020), to improve the innovation response and optimise business processes to achieve dynamic capabilities (Božič and Dimovski, 2019). Hence, BDA capability acts as a knowledge asset of strategic importance for CI processes, increasing the absorptive capacity of the companies and reducing their time, costs and risks (Troisi et al , 2018; Chen et al , 2019).…”

Characterizing stewardship and stakeholder inclusion in data analytics efforts: the collaborative approach of Kansas City, Missouri

“…and necessary technologies (Gamage, 2016;Surbakti et al, 2020); and/or the absence or poor execution of data governance principles (Surbakti et al, 2020), among which accountability and transparency are critical for the public sector (Merhi and Bregu, 2020).…”

“…According to Zeng et al (2020), data analytics technologies are key for local governments to achieve their goals and produce value through their projects and programs, a perspective that suggests that data analytics adoption and use in the public sector requires understanding of organizational characteristics and the context in which an initiative is embedded (Fountain, 2004). Impediments or challenges to data analytics use may diverge, but are often associated with cultural resistance to data-driven practices (Huang et al , 2021; Ku and Gil-Garcia, 2018; Mergel et al , 2016); the workforce’s lack of skills and specialized training in data analysis and necessary technologies (Gamage, 2016; Surbakti et al , 2020); and/or the absence or poor execution of data governance principles (Surbakti et al , 2020), among which accountability and transparency are critical for the public sector (Merhi and Bregu, 2020).…”

Oppsummering og refleksjoner om veien videre

“…Similarly, several studies argue that management promotion and embracing of DDD culture in public organizations may increase the citizens' trust in governmental practices and decisions (van Ooijen et al, 2019), achieve sustainability objectives and promote DDD among employees (Gelderman et al, 2015), and eventually enhance service quality (Manikam et al, 2019). Several papers have deemed that the digital transformation of public agencies and types of digital tools used by employees affects the organizational readiness to embrace data science and analytics, enabling a step forward towards data-driven cultures (da Rosa & de Almeida, 2018; Gong, et al, 2020;Han et al, 2020;Merhi & Bregu, 2020;Reis et al, 2018;Seres et al, 2018). Others have stressed the importance of enhancing employees' competence in regard to data science, analytical thinking, and data analytics (Mentsiev et al, 2020;van Ooijen et al, 2019;Veale & Brass, 2019).…”