Pathways of Data-driven Business Model Design and Realization: A Qualitative Research Study

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet


Maximizing the value from data has become a key challenge for companies as it helps improve operations and decision making, enhances products and services, and ultimately, leads to new business models (BMs). Aiming to achieve the latter, companies take different pathways. Building on a grounded theory research approach, we identified four pathways for designing and realizing data-driven business models (DDBMs). To achieve this goal, we conducted 16 semi-structured interviews with experts from consulting and industry firms. The results fill the gap in the literature on the design and realization of DDBMs and act as a guide for companies.
TitelProceedings of the 54th Annual Hawaii International Conference on System Sciences, HICSS 2021
HerausgeberTung X. Bui
Anzahl der Seiten10
VerlagUniversity of Hawaiʻi at Mānoa
ISBN (Print)978-0-9981331-4-0
ISBN (elektronisch)9780998133140
PublikationsstatusErschienen - 2021
Veranstaltung54th Annual Hawaii International Conference on System Sciences - HICSS 2021 - University of Hawaiʻi at Mānoa, Honolulu, USA / Vereinigte Staaten
Dauer: 04.01.202108.01.2021
Konferenznummer: 54

Bibliographische Notiz

Funding Information:
funded by the business units which also have the analytical capabilities. These companies tend to develop use cases in a lab environment with external parties to justify larger investments once the value was proven. The endeavor is motivated by the BU’s vision and sponsored by the BU lead with support from the CIO/CDO. Cases comprising companies with low data understanding and high self-incentive (see Figure 1: quadrant II) invest in the technology first. These companies have little understanding of their data and potential application fields but have decided to heavily invest in BDA as part of their digital initiatives. Great effort is made to understand technology options and solution functionalities. However, the BDA use is described with short use cases, and the technology selection is prioritized. The endeavor is sponsored by the head of the IT department and funded by the budget for the digital transformation. Companies with low data understanding and low self-incentive (see Figure 1: quadrant III) remain in a pending state. They invest in use case development within the business units and conduct software selection projects but do not take the next step toward a DDBM. These companies tend to initiate cases reactively as a competitive response and under digital pressure. On the opposite side are cases in companies with a high degree of data understanding and self-incentive (see Figure 1: quadrant IV). Having a clear vision and deep analytical capabilities allow these companies to invest in new DDBMs immediately. The initiatives are sponsored by the CEO and financed with funds for new business opportunities. The new DDBM is either integrated into the existing organizational structure, or a new company is established putting the new DDBM forward as a startup. Use case– and technology-centric cases have the ambition to develop DDBMs as goal, “BDA projects pave the way for DDBM” [IP2]. Similar statements were made by IP1, IP3, IP4, IP8, and IP9. We received use case descriptions from IP1 and IP9. The use cases for gradual enhancement of the traditional business model were very detailed, but the potential new DDBMs were described on a higher level. Furthermore, the realization of the use cases was suggested in a sequence beginning with the enhancement of the traditional BM and introducing the DDBM as a so-called “north star.” For example, the use case of a pharma company for data-driven automation that would lead to cost optimization was described in detail with quantifications, but the use case that would imply a new DDBM was outlined with less detail and quantification ranges [IP9].

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