Identifying Strategic Data Assets for Data-centric Transformation
Despite claims that "data is the new oil," firms still struggle to effectively use and monetize their data. Among manifold challenges, there is no accurate way to assess how data assets may be recombined and reused in efforts to create value (Piccoli, Rodriguez, Asadi Someh, et al., 2022). The ability to bridge the gap between discovery, identifying data assets with potential value, and using such assets in value-creating efforts are significant challenges in data monetization (Wixom & Piccoli, 2020). Recent IT infrastructure research accounts for the decoupling of software, hardware, and data components (Mikalef et al., 2021; Tiwana et al., 2010). This viewpoint's proponents contend that organizational applications and data can be designed as a collection of distinct atomic elements that can be “recombined and restructured to quickly construct new solutions” (Mikalef et al., 2021, p. 518). As a result, they provide a view of data that is no longer bound to specific applications but is independently created for numerous anticipated and unforeseen future purposes. By adopting this perspective, the analysis is focused on data assets, which are thought of as cohesive groupings of connected data (Piccoli, Rodriguez, & Grover, 2022; Wixom et al., 2021). Existing research demonstrates the importance of information and analytics capabilities in firm performance (Park et al., 2017) and details data monetization capabilities and practices (Wixom & Ross, 2017). However, knowledge regarding how established organizations can increase their ability to reuse and recombine data is lacking.
This thesis considers the challenges mentioned above in the context of a research project with Audi and other associated partners (Automotive Initiative 2025). Audi aims to transform its production landscape by 2025 to become a data-centric firm and leverage the full potential of their data assets. One of the key success factors in this transformation effort is ensuring that data is continuously provided through processes and applications, avoiding inconsistencies, duplicates, and other potential issues. However, tightly governing the provision of each and every single data flow in the organization might be unfeasible and uneconomical. In practice, this leads to the pivotal question of how firms can assess what data assets are of strategic importance to their business and how accessible these data assets are currently to their needs. Thereby, firms can streamline their activities to identify and provide strategic data assets for their business systematically.
To help identify and assess strategic data assets, potential research questions include:
Strategic data value:
- What are strategic data assets?
- How can strategic data assets be identified?
- How can their value be assessed?
- What are approaches to measure the re-combinability and reusability of data assets?
- How are strategic data assets currently accessed?
- How can data accessibility be improved?
- What is the current quality of strategic data assets?
- What are approaches to measure to re-combinability and reusability of data assets?
Other research questions to be pursued in the thesis can be suggested and discussed.
These are general tasks that we will adapt together to the specific research questions:
- Conduct a structured literature review on data continuity within organizations (Webster & Watson, 2002)
- Develop an approach or tool to assess the strategic value or liquidity of data assets following the guidelines of Design Science Research (Hevner et al., 2004; Peffers et al., 2007)
- Conduct expert interviews to gain further input and
- Evaluation of prototypes, e.g. of the developed approach or tool
- Derive design recommendations for future digital transformation efforts regarding current and planned initiatives
- Interest in current topics of data-driven business models, digital transformation and innovation
- High degree of autonomy and individual responsibility
- Experience in and willingness to conduct scientific studies
- Structured, reliable, and self-motivated work style
The thesis can be written in English or German. The topic can also be adapted to your interests. If you have further questions, please do not hesitate to contact me directly. Please send your application including our application form, a current transcript of records, and your CV to email@example.com and firstname.lastname@example.org. Please note that we can only consider applications with complete documents.
Hevner, March, Park, & Ram. (2004). Design Science in Information Systems Research. MIS Quarterly, 28(1), 75. https://doi.org/10.2307/25148625
Mikalef, P., Pateli, A., & Van De Wetering, R. (2021). IT architecture flexibility and IT governance decentralisation as drivers of IT-enabled dynamic capabilities and competitive performance: The moderating effect of the external environment. European Journal of Information Systems, 30(5), 512-540. https://doi.org/10.1080/0960085x.2020.1808541
Park, Y., El Sawy, O. A., & Fiss, P. (2017). The role of business intelligence and communication technologies in organizational agility: a configurational approach. Journal of the Association for Information Systems, 18(9), 1.
Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A Design Science Research Methodology for Information Systems Research. Journal of Management Information Systems, 24(3), 45–77. https://doi.org/10.2753/mis0742-1222240302
Piccoli, G., Rodriguez, J., Asadi Someh, I., & Wixom, B. (2022). Data Liquidity: Conceptualization, Measurement and Determinants.
Piccoli, G., Rodriguez, J., & Grover, V. (2022). Digital Strategic Initiatives and Digital Resources: Construct Definition and Future Research Directions. MIS Quarterly.
Tiwana, A., Konsynski, B., & Bush, A. A. (2010). Platform evolution: Coevolution of platform architecture, governance, and environmental dynamics. Information Systems Research, 21(4), 675-687.
Webster, J., & Watson, R. T. (2002). Analyzing the Past to Prepare for the Future: Writing a Literature Review. MIS Quarterly, 26(2), xiii–xxiii.
Wixom, B., & Piccoli, G. (2020). Actioned Analytics Pave the Way to New Customer Value. MIT Sloan Management Review. https://sloanreview.mit.edu/article/actioned-analytics-pave-the-way-to-newcustomer-value/
Wixom, B. H., Piccoli, G., & Rodriguez, J. (2021). Fast-Track Data Monetization With Strategic Data Assets. MIT Sloan Management Review, 62(4), 1-4.
Wixom, B. H., & Ross, J. W. (2017). How to monetize your data. MIT Sloan Management Review, 58(3).