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Success factors for managing the SSBI challenges of the AQUIRE framework
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Informationsystem, Information Systems (IS))
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Informationsystem, Information Systems (IS))ORCID iD: 0000-0003-0488-6841
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Informationsystem, Information Systems (IS))
2023 (English)In: Journal of Decision Systems, ISSN 1246-0125, E-ISSN 2116-7052, Vol. 32, no 2, p. 491-512Article in journal (Refereed) Published
Abstract [en]

Self-service business intelligence (SSBI) enables all users, including those with limited technical skills, to perform business intelligence (BI) tasks without the support of BI experts. SSBI reduces pressure on BI experts, gives more freedom to self-reliant users and speeds up decision-making. Recent research has illustrated how organisations experience numerous challenges when trying to obtain SSBI benefits. The AQUIRE framework organises 37 identified SSBI challenges in five categories: Access and use of data, Data Quality, User Independence, creating Reports and Education. SSBI literature does poorly address how these challenges can be tackled. This research study aimed to identify strategies on how to manage those 37 SSBI challenges. The performed case study includes 24 semi-structured interviews with respondents from two organisations which have been heavily involved in SSBI implementation. The results reveal how nine identified SSBI success factors are related to the 37 AQUIRE challenges and how they can be addressed over time.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2023. Vol. 32, no 2, p. 491-512
Keywords [en]
Self service business intelligence, success factors, challenges, SSBI, education
National Category
Information Systems
Research subject
Information Systems
Identifiers
URN: urn:nbn:se:his:diva-21038DOI: 10.1080/12460125.2022.2057006ISI: 000780314700001Scopus ID: 2-s2.0-85127119742OAI: oai:DiVA.org:his-21038DiVA, id: diva2:1650347
Funder
Swedish Civil Contingencies Agency, 2016-3046
Note

CC BY-NC-ND 4.0

Published online: 23 Mar 2022

CONTACT Christian Lennerholt christian.lennerholt@his.se School of Informatics, University of Skövde, Skövde, Sweden

This research was supported by Grant [2016-3046] of the Swedish Civil Contingencies Agency

Available from: 2022-04-06 Created: 2022-04-06 Last updated: 2023-08-15Bibliographically approved
In thesis
1. Facilitating the Implementation and Use of Self Service Business Intelligence
Open this publication in new window or tab >>Facilitating the Implementation and Use of Self Service Business Intelligence
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In traditional Business Intelligence (BI), there is a request-response scenario between technical power users serving non-technical casual users. Today, when data volumes and the frequency of use of a traditional BI system increase, power users are unable to serve all requests from casual users. Self-service Business Intelligence (SSBI) is an upcoming trend that addresses this problem since it enables all users to use BI in a self-reliant manner without support from power users.

The aim of SSBI is to simplify the usage of traditional BI by letting all users conduct their own analysis. Users are allowed to access and use data as desired for their analysis when making decisions, which speeds up the process to use BI. At the same time the pressure on the IT department is relieved, since no power user is needed to support the process. SSBI enables organisations to make time critical decisions without waiting for reports that normally take weeks or months to be delivered. This saves organisational resources since all users can work more effectively and in a self-reliant manner compared to traditional BI. 

Although SSBI offers many benefits compared to traditional BI, many organisations are still struggling and failing to implement SSBI. The process to implement and use SSBI is not an easy task. There is no clear roadmap for how to achieve the SSBI benefits. Therefore, the aim of this thesis is to facilitate the implementation and use of SSBI. Two objectives have been formulated to address this aim. First, it is important to identify what challenges organisations are facing when implementing and using SSBI. The second objective aims to identify success factors for managing the associated SSBI challenges. 

Case study research has been chosen as an appropriate research method to fulfil the research aim and objectives. The case study involves one BI consultancy firm and two of their main customers. The customers are medium-sized organisations which are considered to be the most experienced with implementing SSBI in the consultancy firm’s client base. 

With regard to research objective 1, 37 SSBI challenges have been identified and organized in in five categories of the AQUIRE framework: Access and use of data; Data Quality; User Independence; creating Reports; and Education. For research objective 2, nine success factors for SSBI implementation and use have been revealed as well as how they can be applied over time.  Initially, pilot groups and champions can increase interest in SSBI. Next, user groups and their data needs should be identified, and these user groups should get responsibility to change faulty data. Later, common data definitions and standard reports can simplify the use of data sources. Only then, top management support is needed to accomplish that business governs SSBI data content and that business employees and IT department employees work together in integrated settings. Finally, ongoing SSBI education should target non-technical and technical users differently and change its content over time.

 

Place, publisher, year, edition, pages
Skövde: University of Skövde, 2022. p. 90
Series
Dissertation Series ; 42
National Category
Information Systems
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-21049 (URN)978-91-984919-6-8 (ISBN)
Public defence
2022-05-05, Insikten, Kanikegränd 3A, Skövde, 13:00 (English)
Opponent
Supervisors
Note

Publications with lower relevance:

Rose, J. & Lennerholt, C. (2017). Low Cost Text Mining as a Strategy for Qualitative Researchers. Electronic Journal of Business Research Methods, 15, 2-16.

Available from: 2022-04-13 Created: 2022-04-12 Last updated: 2022-04-13Bibliographically approved

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Lennerholt, Christianvan Laere, JoeriSöderström, Eva

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