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Publications (10 of 66) Show all publications
Berndtsson, M., Jonsson, A.-C., Carlsson, M. & Svahn, T. (2023). A Strategy for Scaling Advanced Analytics. Communications of the ACM, 66(12), 29-31
Open this publication in new window or tab >>A Strategy for Scaling Advanced Analytics
2023 (English)In: Communications of the ACM, ISSN 0001-0782, E-ISSN 1557-7317, Vol. 66, no 12, p. 29-31Article in journal (Refereed) Published
Abstract [en]

Key elements for scaling advanced analytics.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2023
National Category
Information Systems
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-23371 (URN)10.1145/3582075 (DOI)001103094100011 ()2-s2.0-85178243314 (Scopus ID)
Projects
Ökad användning av dataanalys
Funder
Vinnova, 2022-01211
Note

Opinion

We are very grateful for the comments received by the anonymous reviewers. The research is partially supported by VINNOVA, Sweden’s innovation agency.

Available from: 2023-11-20 Created: 2023-11-20 Last updated: 2024-03-14Bibliographically approved
Berndtsson, M. & Ekman, S. (2023). Assessing Maturity in Data-Driven Culture. International Journal of Business Intelligence Research, 14(1)
Open this publication in new window or tab >>Assessing Maturity in Data-Driven Culture
2023 (English)In: International Journal of Business Intelligence Research, ISSN 1947-3591, E-ISSN 1947-3605, Vol. 14, no 1Article in journal (Refereed) Published
Abstract [en]

Research on assessing a group’s maturity in data-driven culture is rare and fragmented. This article investigates how maturity in data-driven culture can be assessed from a historical perspective. A case study was done on how the Education Council evolved in analytics maturity and as a group during 2014-2023. The assessment showed that the Education Council experienced both successful progression of group development and usage of analytics, as well as regression in group development and analytics usage. The practical implications of the findings are that group leaders need to be aware of the interplay between analytics usage and group development when planning to improve their group’s maturity in data-driven culture.

Place, publisher, year, edition, pages
IGI Global, 2023
Keywords
analytics, data-driven culture, group development, maturity model
National Category
Information Systems
Research subject
Information Systems; GAME Research Group
Identifiers
urn:nbn:se:his:diva-23330 (URN)10.4018/IJBIR.332813 (DOI)2-s2.0-85175970789 (Scopus ID)
Projects
Miljöåterkoppling i realtid för att skynda på energiomställningen
Funder
Swedish Energy Agency, P2022-01069
Note

CC BY 4.0

This research was partially funded by Swedish Energy Agency under grant No P2022-01069.

Available from: 2023-10-27 Created: 2023-10-27 Last updated: 2023-11-16Bibliographically approved
Ericsson, A. & Berndtsson, M. (2022). A Heatmap Approach for Master Data Management Programs. Journal of Information Systems and Technology Management, 19, Article ID e202219017.
Open this publication in new window or tab >>A Heatmap Approach for Master Data Management Programs
2022 (English)In: Journal of Information Systems and Technology Management, ISSN 1809-2640, E-ISSN 1807-1775, Vol. 19, article id e202219017Article in journal (Refereed) Published
Abstract [en]

Master data management programs are large by nature since the aim is to provide the entire enterprise with a shared trusted view of the organisation’s most critical data assets. In this paper, we present what dimensions and activities a master data management program in a large organisation should consider and how to monitor such a program once it is up and running. A heatmap approach is used to visualize the inherent complexity of a master data management program. Our approach is derived from participating in four different master data management programs in four different global organisations during 2007-2020.

Place, publisher, year, edition, pages
Universidade de Sao Paulo, 2022
Keywords
data quality, master data, master data management, master data management programs, heatmap
National Category
Computer and Information Sciences
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-22084 (URN)10.4301/s1807-1775202219017 (DOI)
Note

CC BY 3.0

Available from: 2022-11-29 Created: 2022-11-29 Last updated: 2023-01-16Bibliographically approved
Berndtsson, M. & Svahn, T. (2022). A Matrix for Assessing Data-Driven Culture in Teams. In: Proceedings of the 2022 Pre-ICIS SIGDSA Symposium: . Paper presented at 2022 Pre-ICIS SIGDSA Symposium Special Interest Group on Decision Support and Analytics (SIGDSA), Symposium on Analytics for Digital Frontiers, December 10, 2022 at the Copenhagen Business School in Copenhagen, Denmark. Association for Information Systems, Article ID 6.
Open this publication in new window or tab >>A Matrix for Assessing Data-Driven Culture in Teams
2022 (English)In: Proceedings of the 2022 Pre-ICIS SIGDSA Symposium, Association for Information Systems, 2022, article id 6Conference paper, Published paper (Refereed)
Abstract [en]

Establishing a data-driven culture in teams is on the agenda for many managers and analytics leaders. With a data-driven culture in place, it is envisioned that investments in analytics can be used to their full potential. In practice, most organizations struggle to establish a data-driven culture in teams and have few tools available to assess the level of maturity.

Related research has focused on maturity models in business intelligence & analytics that target the organizational level. Hence, these maturity models provide limited support for assessing the team level, e.g., why some teams do not develop a data-driven culture.

This paper used a systematic literature review and an online questionnaire to develop a matrix for assessing a team's maturity in data-driven culture. The matrix synthesizes previous work in analytics and group development. Findings from the literature review revealed a mismatch between problems addressed by the research community and perceived problems in practice by organizations.

Place, publisher, year, edition, pages
Association for Information Systems, 2022
Keywords
Data-driven culture, data-driven organizations, analytics, business intelligence, maturity models, group development
National Category
Information Systems, Social aspects
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-22108 (URN)
Conference
2022 Pre-ICIS SIGDSA Symposium Special Interest Group on Decision Support and Analytics (SIGDSA), Symposium on Analytics for Digital Frontiers, December 10, 2022 at the Copenhagen Business School in Copenhagen, Denmark
Projects
Ökad användning av dataanalys
Funder
Vinnova, 2022-01211
Available from: 2022-12-12 Created: 2022-12-12 Last updated: 2023-01-25Bibliographically approved
Berndtsson, M., Lennerholt, C., Svahn, T. & Larsson, P. (2020). 13 Organizations' Attempts to Become Data-Driven. International Journal of Business Intelligence Research, 11(1), 1-21
Open this publication in new window or tab >>13 Organizations' Attempts to Become Data-Driven
2020 (English)In: International Journal of Business Intelligence Research, ISSN 1947-3591, E-ISSN 1947-3605, Vol. 11, no 1, p. 1-21Article in journal (Refereed) Published
Abstract [en]

Becoming a data-driven organization is a vision for several organizations. It has been frequently mentioned in the literature that data-driven organizations are likely to be more successful than organizations that mostly make decisions on gut feeling. However, few organizations make a successful shift to become data-driven, due to a number of different types of barriers. This article investigates, the initial journey to become a data-driven organization for 13 organizations. Data has been collected via documents and interviews, and then analyzed with respect to: i) how they scaled up the usage of analytics to become data-driven; ii) strategies developed; iii) barriers encountered; and iv) usage of an overall change process. The findings are that most organizations start their journey via a pilot project, take shortcuts when developing strategies, encounter previously reported top barriers, and do not use an overall change management process.

Place, publisher, year, edition, pages
IGI Global, 2020
National Category
Other Computer and Information Science
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-18032 (URN)10.4018/IJBIR.2020010101 (DOI)2-s2.0-85077520007 (Scopus ID)
Funder
Knowledge Foundation
Note

CC BY 4.0

Available from: 2019-12-25 Created: 2019-12-25 Last updated: 2023-09-25Bibliographically approved
Berndtsson, M., Ericsson, A. & Svahn, T. (2020). Scaling Up Data-Driven Pilot Projects. The AI Magazine, 41(3), 94-102
Open this publication in new window or tab >>Scaling Up Data-Driven Pilot Projects
2020 (English)In: The AI Magazine, ISSN 0738-4602, Vol. 41, no 3, p. 94-102Article in journal (Refereed) Published
Abstract [en]

Conducting pilot projects are a common approach among organizations to test and evaluate new technology. A pilot project is often conducted to remove uncertainties from a large-scale project and should be limited in time and scope. Nowadays, several organizations are testing and evaluating artificial intelligence techniques and more advanced forms of analytics via pilot projects. Unfortunately, many organizations are experiencing problems in scaling-up the findings from pilot projects to the rest of the organization. Hence, results from pilot projects become siloed with limited business value. In this article, we present an overview of barriers for conducting and scaling-up data-driven pilot projects. Lack of senior management support is a frequently mentioned top barrier in the literature. In response to this, we present our recommendations on what type of activities can be performed, to increase the chances of getting a positive response from senior management regarding scaling-up the usage of artificial intelligence and advanced analytics within an organization.

Place, publisher, year, edition, pages
Association for the Advancement of Artificial Intelligence, 2020
National Category
Other Computer and Information Science
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-19097 (URN)10.1609/aimag.v41i3.5307 (DOI)000574631600007 ()2-s2.0-85092796964 (Scopus ID)
Available from: 2020-09-24 Created: 2020-09-24 Last updated: 2020-10-29Bibliographically approved
Berndtsson, M. & Svahn, T. (2020). Strategies for Scaling Analytics: A Nontechnical Perspective. Business Intelligence Journal, 25(1), 43-53
Open this publication in new window or tab >>Strategies for Scaling Analytics: A Nontechnical Perspective
2020 (English)In: Business Intelligence Journal, ISSN 1547-2825, Vol. 25, no 1, p. 43-53Article in journal (Refereed) Published
Place, publisher, year, edition, pages
The Data Warehousing Institute (TDWI), 2020
Keywords
scaling analytics
National Category
Computer and Information Sciences
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-18504 (URN)
Available from: 2020-06-12 Created: 2020-06-12 Last updated: 2023-09-26Bibliographically approved
Berndtsson, M., Lennerholt, C., Larsson, P. & Svahn, T. (2019). A Blueprint for Training Future Users of Self-Service Business Intelligence. Business Intelligence Journal, 24(1), 30-38
Open this publication in new window or tab >>A Blueprint for Training Future Users of Self-Service Business Intelligence
2019 (English)In: Business Intelligence Journal, ISSN 1547-2825, Vol. 24, no 1, p. 30-38Article in journal (Refereed) Published
Place, publisher, year, edition, pages
The Data Warehousing Institute (TDWI), 2019
Keywords
self-service business intelligence
National Category
Computer and Information Sciences
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-17278 (URN)
Available from: 2019-06-24 Created: 2019-06-24 Last updated: 2022-04-13Bibliographically approved
Berndtsson, M. (2019). How a Picture Postcard Can Help You Develop a Data-Driven Analytics Culture. TDWI Upside
Open this publication in new window or tab >>How a Picture Postcard Can Help You Develop a Data-Driven Analytics Culture
2019 (English)In: TDWI UpsideArticle in journal (Other (popular science, discussion, etc.)) Published
Keywords
business intelligence, data-driven organisation, analytics, data-driven culture
National Category
Other Computer and Information Science
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-17805 (URN)
Available from: 2019-10-21 Created: 2019-10-21 Last updated: 2019-10-21Bibliographically approved
Berndtsson, M., Forsberg, D., Stein, D. & Svahn, T. (2018). Becoming a data-driven organisation. In: U. Frank; K. Kautz K.; P. M. Bednar (Ed.), : . Paper presented at 26th European Conference on Information Systems (ECIS2018), Beyond Digitization - Facets of Socio-Technical Change, Portsmouth, United Kingdom, June 23-28, 2018. Association for Information Systems, Article ID 43.
Open this publication in new window or tab >>Becoming a data-driven organisation
2018 (English)In: / [ed] U. Frank; K. Kautz K.; P. M. Bednar, Association for Information Systems , 2018, article id 43Conference paper, Published paper (Refereed)
Abstract [en]

Organisations seeking competitive advantage in the age of big data often adopt the strategy of becoming data-driven. The paper describes research in progress with an organisation pursuing this strategy. Initial results from literature study and preliminary interviews are outlined, including a two layer factor model and prototype maturity model. The next research steps are also explained.

Place, publisher, year, edition, pages
Association for Information Systems, 2018
Keywords
Analytics, Data-Driven Organisations, Business Intelligence
National Category
Information Systems
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-16398 (URN)2-s2.0-85061304326 (Scopus ID)9781861376671 (ISBN)
Conference
26th European Conference on Information Systems (ECIS2018), Beyond Digitization - Facets of Socio-Technical Change, Portsmouth, United Kingdom, June 23-28, 2018
Projects
BISON
Note

"Research-in-Progress Papers"

Available from: 2018-11-15 Created: 2018-11-15 Last updated: 2023-01-03Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8362-3825

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