In the 2000s and in the 2010s, the expression ‘big data’ has come to the fore embracing discourses on technical tools, organizations, and uses of datasets that are too large to be handled through current available technologies (Fosso Wamba et al., 2015). However, this simple definition is problematic. While Batty (2015) argues that some 40 definitions of big data exist, a very popular scheme is provided by the so-called ‘multiple V model’, which implies the description of the volume, velocity, variety, value, and veracity of the dataset at hand (Assunção et al., 2015; Hashem et al., 2015; Russom, 2011). The multiple V model is straightforward and covers, although implicitly, other key aspects such as political stakes, organizational commitment, openness, security, and spatial features of the data involved. The adoption of big data and their analytics in many organizations is challenging, since it is hindered by typical barriers involving technological skills, mentality, data sharing, and privacy issues (Villars, Olofson and Eastwood, 2011; Edosio, 2014). In urban and regional science, a relevant paradigm is that of ‘smart cities’, invoking a process toward more efficient forms of management through continuous use of big and open data analytics disentangling the rationale of complex networks interlaced in urban areas (Batty et al., 2012; Townsend, 2013). Environmental management and planning imply the development of information-intensive processes, not least because the increasing complexity of environmental issues and institutional apparatuses have spurred the need for designing and maintaining large datasets (Vitolo et al., 2015). This contribution develops on the multiple V model defining big data and uses the resulting analytical framework to pinpoint strengths and weaknesses of a possible big data driven evolution of the Regional Environmental Information System (REIS) of Sardinia, Italy.

Big data and environmental management: the perspectives of the Regional Environmental Information System of Sardinia, Italy

LAI, SABRINA;
2016

Abstract

In the 2000s and in the 2010s, the expression ‘big data’ has come to the fore embracing discourses on technical tools, organizations, and uses of datasets that are too large to be handled through current available technologies (Fosso Wamba et al., 2015). However, this simple definition is problematic. While Batty (2015) argues that some 40 definitions of big data exist, a very popular scheme is provided by the so-called ‘multiple V model’, which implies the description of the volume, velocity, variety, value, and veracity of the dataset at hand (Assunção et al., 2015; Hashem et al., 2015; Russom, 2011). The multiple V model is straightforward and covers, although implicitly, other key aspects such as political stakes, organizational commitment, openness, security, and spatial features of the data involved. The adoption of big data and their analytics in many organizations is challenging, since it is hindered by typical barriers involving technological skills, mentality, data sharing, and privacy issues (Villars, Olofson and Eastwood, 2011; Edosio, 2014). In urban and regional science, a relevant paradigm is that of ‘smart cities’, invoking a process toward more efficient forms of management through continuous use of big and open data analytics disentangling the rationale of complex networks interlaced in urban areas (Batty et al., 2012; Townsend, 2013). Environmental management and planning imply the development of information-intensive processes, not least because the increasing complexity of environmental issues and institutional apparatuses have spurred the need for designing and maintaining large datasets (Vitolo et al., 2015). This contribution develops on the multiple V model defining big data and uses the resulting analytical framework to pinpoint strengths and weaknesses of a possible big data driven evolution of the Regional Environmental Information System (REIS) of Sardinia, Italy.
978-88-9052-964-1
Big Data, Environmental Information, Multiple V model
File in questo prodotto:
File Dimensione Formato  
INPUT2016_Lai_et_al.pdf

Solo gestori archivio

Tipologia: versione editoriale
Dimensione 773.02 kB
Formato Adobe PDF
773.02 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/180849
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact