Defining Beneficiaries of Emerging Data Infrastructures Towards Effective Data Appropriation: Insights from the Swedish Space Data LabVise andre og tillknytning
2021 (engelsk)Inngår i: Information and Software Technologies / [ed] Audrius Lopata; Daina Gudonienė; Rita Butkienė, Springer, 2021, s. 32-47Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]
The increasing collection and usage of data and data analytics has prompted development of Data Labs. These labs are (ideally) a way for multiple beneficiaries to make use of the same data in ways that are value-generating for all. However, establishing data labs requires the mobilization of various infrastructural elements, such as beneficiaries, offerings and needed analytics talent, all of which are ambiguous and uncertain. The aim of this paper is to examine how such beneficiaries can be identified and understood for the nascent Swedish space data lab. The paper reports on the development of persona descriptions that aim to support and represent the needs of key beneficiaries of earth observation data. Our main results include three thorough persona descriptions that represent the lab’s respective beneficiaries and their distinct characteristics. We discuss the implications of the personas on addressing the infrastructural challenges, as well as the lab’s design. We conclude that personas provide emerging data labs with relatively stable beneficiary archetypes that supports the further development of the other infrastructure components. More research is needed to better understand how these persona descriptions may evolve, as well as how they may influence the continuous development process of the space data lab.
sted, utgiver, år, opplag, sider
Springer, 2021. s. 32-47
Serie
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1486
Emneord [en]
Beneficiary, Data appropriation, Data infrastructure, Data lab, Persona, Data Analytics, Earth observation data, Mobilisation, Space data, Swedishs, Laboratories
HSV kategori
Forskningsprogram
Informationssystem; Maskininlärning
Identifikatorer
URN: urn:nbn:se:ltu:diva-88259DOI: 10.1007/978-3-030-88304-1_3ISI: 000869711400003Scopus ID: 2-s2.0-85118139059OAI: oai:DiVA.org:ltu-88259DiVA, id: diva2:1618290
Konferanse
27th International Conference on Information and Software Technologies (ICIST 2021), Kaunas, Lithuania, October 14-16, 2021
Merknad
ISBN för värdpublikation: 978-3-030-88303-4, 978-3-030-88304-1
2021-12-092021-12-092022-11-11bibliografisk kontrollert