Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 159-191 |
| Seitenumfang | 33 |
| Fachzeitschrift | Information Systems and e-Business Management |
| Jahrgang | 21 |
| Ausgabenummer | 1 |
| Frühes Online-Datum | 5 Feb. 2023 |
| Publikationsstatus | Veröffentlicht - März 2023 |
Abstract
Self-service business intelligence and analytics (SSBIA) empowers non-IT users to create reports and analyses independently. SSBIA methods and processes are discussed in the context of an increasing number of application scenarios. However, previous research on SSBIA has made distinctions among these scenarios only to a limited extent. These scenarios include a wide variety of activities ranging from simple data retrieval to the application of complex algorithms and methods of analysis. The question of which dimensions are suitable for differentiating SSBIA application scenarios remains unanswered. In this article, we develop a taxonomy to distinguish among SSBIA applications more effectively by analyzing the relevant scientific literature and current SSBIA tools as well as by conducting a case study in a company. Both researchers and practitioners can use this taxonomy to describe and analyze SSBIA scenarios in further detail. In this way, the opportunities and challenges associated with SSBIA application can be identified more clearly. In addition, we conduct a cluster analysis based on the SSBIA tools thus analyzed. We identify three archetypes that describe typical SSBIA tools. These archetypes identify the application scenarios that are addressed most frequently by SSBIA tool providers. We conclude by highlighting the limitations of this research and suggesting an agenda for future research.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Information systems
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in: Information Systems and e-Business Management, Jahrgang 21, Nr. 1, 03.2023, S. 159-191.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Self-service business intelligence and analytics application scenarios
T2 - A taxonomy for differentiation
AU - Passlick, Jens
AU - Grützner, Lukas
AU - Schulz, Michael
AU - Breitner, Michael H.
PY - 2023/3
Y1 - 2023/3
N2 - Self-service business intelligence and analytics (SSBIA) empowers non-IT users to create reports and analyses independently. SSBIA methods and processes are discussed in the context of an increasing number of application scenarios. However, previous research on SSBIA has made distinctions among these scenarios only to a limited extent. These scenarios include a wide variety of activities ranging from simple data retrieval to the application of complex algorithms and methods of analysis. The question of which dimensions are suitable for differentiating SSBIA application scenarios remains unanswered. In this article, we develop a taxonomy to distinguish among SSBIA applications more effectively by analyzing the relevant scientific literature and current SSBIA tools as well as by conducting a case study in a company. Both researchers and practitioners can use this taxonomy to describe and analyze SSBIA scenarios in further detail. In this way, the opportunities and challenges associated with SSBIA application can be identified more clearly. In addition, we conduct a cluster analysis based on the SSBIA tools thus analyzed. We identify three archetypes that describe typical SSBIA tools. These archetypes identify the application scenarios that are addressed most frequently by SSBIA tool providers. We conclude by highlighting the limitations of this research and suggesting an agenda for future research.
AB - Self-service business intelligence and analytics (SSBIA) empowers non-IT users to create reports and analyses independently. SSBIA methods and processes are discussed in the context of an increasing number of application scenarios. However, previous research on SSBIA has made distinctions among these scenarios only to a limited extent. These scenarios include a wide variety of activities ranging from simple data retrieval to the application of complex algorithms and methods of analysis. The question of which dimensions are suitable for differentiating SSBIA application scenarios remains unanswered. In this article, we develop a taxonomy to distinguish among SSBIA applications more effectively by analyzing the relevant scientific literature and current SSBIA tools as well as by conducting a case study in a company. Both researchers and practitioners can use this taxonomy to describe and analyze SSBIA scenarios in further detail. In this way, the opportunities and challenges associated with SSBIA application can be identified more clearly. In addition, we conduct a cluster analysis based on the SSBIA tools thus analyzed. We identify three archetypes that describe typical SSBIA tools. These archetypes identify the application scenarios that are addressed most frequently by SSBIA tool providers. We conclude by highlighting the limitations of this research and suggesting an agenda for future research.
KW - Business intelligence
KW - Self-service
KW - Software archetypes
KW - SSBIA application scenarios
KW - Taxonomy
UR - http://www.scopus.com/inward/record.url?scp=85147361000&partnerID=8YFLogxK
U2 - 10.1007/s10257-022-00574-3
DO - 10.1007/s10257-022-00574-3
M3 - Article
AN - SCOPUS:85147361000
VL - 21
SP - 159
EP - 191
JO - Information Systems and e-Business Management
JF - Information Systems and e-Business Management
SN - 1617-9846
IS - 1
ER -