Solutions for Data Quality in GIS and VGI: A Systematic Literature Review

Author(s):  
Gabriel Medeiros ◽  
Maristela Holanda
2021 ◽  
Vol 13 (20) ◽  
pp. 11382
Author(s):  
Dignity Paradza ◽  
Olawande Daramola

Organisations must derive adequate business value (BV) from Business Intelligence (BI) adoption to retain their profitability and long-term sustainability. Yet, the nuances that define the realisation of BV from BI are still not understood by many organisations that have adopted BI. This paper aims to foster a deeper understanding of the relationship between Business Intelligence (BI) and business value (BV) by focusing on the theories that have been used, the critical factors of BV derivation, the inhibitors of BV, and the different forms of BV. To do this, a systematic literature review (SLR) methodology was adopted. Articles were retrieved from three scholarly databases, namely Google Scholar, Scopus, and Science Direct, based on relevant search strings. Inclusion and exclusion criteria were applied to select ninety-three (93) papers as the primary studies. We found that the most used theoretical frameworks in studies on BI and BV are the Resource-Based View (RBV), Dynamic Capabilities Theory (DCT), Technology-Organisation-Environment (TOE), and Contingency Theory (CON). The most acknowledged critical factors of BV are skilled human capital, BI Infrastructure, data quality, BI application and usage/data culture, BI alignment with organisational goals, and top management support. The most acclaimed inhibitors of BV are data quality and handling, data security and protection, lack of BI Infrastructure, and lack of skilled human resource capital, while customer intelligence is the most acknowledged form of BV. So far, many theories that are relevant to BI and BV, critical factors, inhibitors, and forms of BV were marginally mentioned in the literature, requiring more investigations. The study reveals opportunities for future research that can be explored to gain a deeper understanding of the issues of BV derivation from BI. It also offers useful insights for adopters of BI, BI researchers, and BI practitioners.


Author(s):  
Anandhi Ramasamy ◽  
Soumitra Chowdhury

Although big data has become an integral part of businesses and society, there is still concern about the quality aspects of big data. Past research has focused on identifying various dimensions of big data. However, the research is scattered and there is a need to synthesize the ever involving phenomenon of big data. This research aims at providing a systematic literature review of the quality dimension of big data. Based on a review of 17 articles from academic research, we have presented a set of key quality dimensions of big data.


2019 ◽  
Vol 12 (3) ◽  
pp. 280-295 ◽  
Author(s):  
Alison Nuske ◽  
Fiona Rillotta ◽  
Michelle Bellon ◽  
Amanda Richdale

2014 ◽  
Author(s):  
Heather T. Snyder ◽  
Maggie R. Boyle ◽  
Lacey Gosnell ◽  
Julia A. Hammond ◽  
Haley Huey

2018 ◽  
Vol 19 (4) ◽  
pp. 600-611 ◽  
Author(s):  
Nathan Beel ◽  
Carla Jeffries ◽  
Charlotte Brownlow ◽  
Sonya Winterbotham ◽  
Jan du Preez

2017 ◽  
Vol 41 (3) ◽  
pp. 222-233 ◽  
Author(s):  
David J. Bumgarner ◽  
Elizabeth J. Polinsky ◽  
Katharine G. Herman ◽  
Joanne M. Fordiani ◽  
Carmen P. Lewis ◽  
...  

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