Location

Online

Event Website

https://hicss.hawaii.edu/

Start Date

3-1-2023 12:00 AM

End Date

7-1-2023 12:00 AM

Description

A key objective of data-driven transformations is to utilize big data analytics (BDA) to create data-driven business value (DDBV). While prior research shows the potential of BDA to achieve DDBV, the concept remains blurry and an overview of realizable DDBVs is still lacking. To better understand the multidimensionality of the DDBV concept and to obtain insights into the bandwidth of achievable DDBVs, we conducted a systematic review of the information systems literature. Based on our results, we present a comprehensive overview of 34 DDBVs, which are classified according to their tangibility and locus of value realization. Furthermore, we describe three research deficiencies: (1) the missing operationalization of the DDBV concept, (2) the lack of explanatory mechanisms for DDBV realization, and (3) missing qualitative, in-depth insights into DDBV realization processes. Future research may build upon our systematization and help closing these research gaps, thereby increasing the success likelihood of data-driven initiatives.

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Jan 3rd, 12:00 AM Jan 7th, 12:00 AM

Delineating the Business Value of Data-driven Initiatives in Organizations – Findings from a Systematic Review of the Information Systems Literature

Online

A key objective of data-driven transformations is to utilize big data analytics (BDA) to create data-driven business value (DDBV). While prior research shows the potential of BDA to achieve DDBV, the concept remains blurry and an overview of realizable DDBVs is still lacking. To better understand the multidimensionality of the DDBV concept and to obtain insights into the bandwidth of achievable DDBVs, we conducted a systematic review of the information systems literature. Based on our results, we present a comprehensive overview of 34 DDBVs, which are classified according to their tangibility and locus of value realization. Furthermore, we describe three research deficiencies: (1) the missing operationalization of the DDBV concept, (2) the lack of explanatory mechanisms for DDBV realization, and (3) missing qualitative, in-depth insights into DDBV realization processes. Future research may build upon our systematization and help closing these research gaps, thereby increasing the success likelihood of data-driven initiatives.

https://aisel.aisnet.org/hicss-56/os/org_issues_in_bi/6