A fuzzy ELECTRE structure methodology to assess big data maturity in healthcare SMEs

Abstract

Advances in technology and an increase in the amount and complexity of data that is generated in healthcare have led to an indispensable revolution in this sector related to big data. Analytics of information based on multimodal clinical data sources require big data projects. When starting big data projects in the healthcare sector, it is often necessary to assess the maturity of an organization with respect to big data, i.e. its capacity in managing big data. The assessment of the maturity of an organization requires multi criteria decision making as there is no single criterion or dimension that defines the maturity level regarding big data but an entire set of them. Based on the ISO 15504, this article proposes a fuzzy ELECTRE structure methodology to assess the maturity level of small and medium-sized enterprises in the healthcare sector (SMEHs). The obtained experimental results provide evidence that this methodology helps to determine and compare maturity levels in big data management of organizations or the evolution of maturity over time. This is also useful in terms of diagnosing the

Znanstveno-raziskovalni izzivi na poti digitalne preobrazbe

“…Za zagotovitev celovite ocene podatkovne zrelosti organizacije moramo podatkovno zrelost obravnavati z vidika več dejavnikov oziroma kriterijev, kot so na primer kakovost podatkov (Günther et al, 2019;Loshin, 2011;Ryu et al, 2006), varnost podatkov (Spruit & Pietzka, 2015a), zasebnost podatkov (Guangming idr., 2017;Marchildon et al, 2018), podatkovna kultura (Kiron et al, 2013;Sternkopf & Mueller, 2018), uporaba digitalnih tehnologij, s katerimi organizacija zajema in analizira svoje podatke (Parra et al, 2019;Peña et al, 2018), upoštevanje razvitosti podatkovne arhitekture (Guangming et al, 2017;Ulrich, 2010) in druge. Podatkovne zrelosti tako ne moremo oceniti le z upoštevanjem enega kriterija ali dejavnika, ampak skozi prizmo več posameznih, zato jo moramo obravnavati kot večkriterijski problem.…”

“…Pomemben dejavnik predstavlja uporaba tehnologij, s katerimi lahko podjetje upravlja s podatki, ter opredelitev strategije. Kot ugotavljajo (Coleman et al, 2016;Moonen et al, 2019;Parra et al, 2019;Peña et al, 2018;Pillay & van der Merwe, 2021) iz svojih modelov, je pri ocenjevanju podatkovne zrelosti pomembno predvsem vodstvo oziroma njegova podpora, skozi katero se razvija podatkovna kultura podjetja. Vodstvo je tisto, ki mora za uresničitev napredovanja podatkovne zrelosti sprejemati odločitve na podlagi podatkov in ne zgolj na podlagi intuicije (Parra et al, 2019;Pillay & van der Merwe, 2021;Windt et al, 2019).…”

“…Osmih dejavnikov nismo izbrali, ker so že del drugih izbranih dejavnikov širšega pomena ali pa niso zajeti v nobenem od zgornjih modelov (tabela 1). ( Ryu et al, 2006); (Cai & Zhu, 2015); (Günther et al, 2019); (Ardagna et al, 2018); (Kilkenny & Robinson, 2018) (Pillay & van der Merwe, 2021); (Spruit & Pietzka, 2015a); (Peña et al, 2018); (Mouhib et al, 2020);…”

Big data maturity models for the public sector: a review of state and organizational level models

“…However, more recent research suggests that there may be some variance in big data maturity modeling depending on the context such as healthcare and air transport (Carvalho et al, 2019;Peña et al, 2018;Hausladen and Schosser, 2020). For example, Carvalho et al (2019, p. 279) argue that earlier big data models "do not present specific characteristics that represent the specificity of the IS in the health area."…”

“…Furthermore, in certain models, the dimensions of big data maturity can be specific enough to give a point scoring to each sub-category evaluation. For example, the model proposed by Peña et al (2018) uses the related ELECTRE method to assess big data maturity in healthcare sector.…”