Data Ecosystem and Data Value Chain: An Exploration of Drones Technology Applications

Cristina Simone (), Federica Ceci () and Cristina Alaimo ()
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Cristina Simone: Luiss University Rome
Federica Ceci: University G. d’Annunzio Chieti-Pescara
Cristina Alaimo: Luiss University Rome

A chapter in Sustainable Digital Transformation, 2023, pp 203-218 from Springer

Abstract: Abstract The drones’ market is anticipated to grow significantly because of its increasing use for commercial and professional purposes. They are currently used in many industries and the next frontier is represented by the automotive industry. The volume of disposable data is growing exponentially, reaching more than 44 zettabytes of valuable data in 2020. In order to be able to fully reap the value of data available, firms must delve in big data and Data Value Chain (DVC) discipline. This paper explores the ecosystem formation as well as the challenges and criticalities encountered by Dronus and Here in their interaction and exchange of data. Ecosystems are network of distributed companies that shows multiple links toward their environment that interact via standard and business rules which permit them to be independent but also connected. Data have been collected through multiple sources, i.e. semi-structured interviews with firm’s representatives and direct observation. This study will give a contribution for future literature as it focuses on the analysis and research of an important gap that sees knowledge of bottlenecks as a way of conscious and efficient growth. In this way, firms and ecosystems that have successfully understood the importance of these variables found will be able to better answer to changes becoming more agile. Successful big data integration, accessibility and standard business rules, as we identify as the main roadblocks, will allow firms in data-driven sectors, and in particular in computer vision-based data set, to be efficient in the interconnection and to become more agile.

Keywords: Data ecosystem; Data value chain; Digital transformation (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-15770-7_13

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