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Big data and Smart data: two interdependent and synergistic digital policies within a virtuous data exploitation loop
- Source :
- Journal of High Technology Management Research, Journal of High Technology Management Research, Elsevier, 2021, 32 (1), pp.100406. ⟨10.1016/j.hitech.2021.100406⟩, Journal of High Technology Management Research, 2021, 32 (1), pp.100406. ⟨10.1016/j.hitech.2021.100406⟩
- Publication Year :
- 2021
- Publisher :
- HAL CCSD, 2021.
-
Abstract
- International audience; This research examines for the first time the relationship between Big data and Smart data among French automotive distributors. Many low-tech firms engage in these data policies to improve their decisions and performance through the predictive capacities of their data. A discussion emerges in the literature according to which an effective policy lies in the conversion of a mass of raw data into so-called intelligent data. In order to understand better this digital transition, we question the transformation of data policies practiced in low-tech firms through the founding model of 3Vs (Volume, Variety and Velocity of data). First of all, this empirical study of 112 French automotive distributors develops the existing literature by 2 proposing an original and detailed typology of the data policies practiced (Low data, Big data and Smart data). Secondly, after specifying the elements of the differences between the quantitative nature of Big data and the qualitative nature of Smart data, our results reveal and analyse for the first time the existence of their synergistic relationship. Companies transform their Big data approach into Smart data when they move from massive exploitation to intelligent exploitation of their data. The phenomenon is part of a high-end loop data exploitation. Initially, the exploitation of intelligent data can only be done by extracting a sample from a large raw data pool previously made by a Big data policy. Secondly, the organization's raw data pool is in turn enriched by the repayment of contributions made by the Smart data approach. Thus, this study develops three important ways. First off, we identify, detail and compare the current data policies of a traditional industry. Secondly, we reveal and explain the evolution of digital practices within organizations that now combine both quantitative and qualitative data exploitation. Finally, our results guide decision-makers towards the synergistic and the legitimate association of different forms of data management for better performance.; Nombreuses sont les firmes qui désormais s’engagent dans des politiques Big data ou Smart data afin d’améliorer leurs décisions. Malgré leur popularité, peu de connaissances sont à ce jour proposées concernant la nature de ces deux démarches que l’on oppose souvent en raison de leurs différents degrés de finesse d’exploitation des données. Notre étude quantitative auprès de 112 PME de la distribution automobile décrit puis explique leur différenciation et leur complémentarité vertueuse.
- Subjects :
- velocity
Information Systems and Management
Computer science
Strategy and Management
Data management
media_common.quotation_subject
Big data
Qualitative property
Sample (statistics)
automotive distribution
JEL: M - Business Administration and Business Economics • Marketing • Accounting • Personnel Economics/M.M1 - Business Administration
Empirical research
Management of Technology and Innovation
0502 economics and business
media_common
Marketing
Smart data
volume
business.industry
05 social sciences
Data science
Computer Science Applications
Variety (cybernetics)
Interdependence
variety
[SHS.GESTION]Humanities and Social Sciences/Business administration
050211 marketing
business
Raw data
050203 business & management
Subjects
Details
- Language :
- English
- ISSN :
- 10478310
- Database :
- OpenAIRE
- Journal :
- Journal of High Technology Management Research, Journal of High Technology Management Research, Elsevier, 2021, 32 (1), pp.100406. ⟨10.1016/j.hitech.2021.100406⟩, Journal of High Technology Management Research, 2021, 32 (1), pp.100406. ⟨10.1016/j.hitech.2021.100406⟩
- Accession number :
- edsair.doi.dedup.....c25a35eb6400a11da8b133a6f9789253
- Full Text :
- https://doi.org/10.1016/j.hitech.2021.100406⟩