Framework for Big Data integration in e-government

Abstract

This article describes researches regarding Big Data integration in e‑government decision‑making, for instance, in areas like solar energy provisioning, environmental protection, agricultural and natural resources exploitation, health and social care, education, housing and transportation management, among others. These studies refer to regions that have integrated Big Data in e‑government, where South America is still in the early adoption stages. Hence, this study proposes three steppingstones for Big Data integration in e‑government decision‑making, production, management and application. The proposed framework aims to be a reference in South America for Big Data adoption in e‑government and thus help to mitigate the technology delay regarding other regions. Finally, a case study with open data obtained from the Instituto Nacional de Estadística y Censos of Ecuador (Ecuadorian Statistics and Census Agency) is presented.

Virtual state, where are you? A literature review, framework and agenda for failed digital transformation

“…This fragmentation inhibits the integration of datasets into high-quality ‘big data’ that could provide valuable insights and or predictions for the public sector (Adad et al, 2020; Madanian et al, 2019). As such, the literature suggests that improving inter-organizational information integration should become a key priority; not only to do more, but to do better (Martinez-Mosquera et al, 2019). Current databases are badly linked leading to problems when this data is used as input for algorithmic decision-making.…”

Exploring the Landscape of Data Analysis: A Review of Its Application and Impact in Ecuador

“…Despite its potential, around 90% of the data held by credit card providers is not used in data analytics applications to improve processes [52]. Even at the residential level, data analysis can provide insights that influence decision-making, such as the increasing use of low-consumption light bulbs [29] and the determination of socioeconomic levels using information from airtime recharges of cell phones [28]. In [25], a computational algorithm was developed in RStudio, and in conjunction with ArcGIS software for geographic information, spatial segregation patterns were identified in Ambato, Ecuador.…”