Start Date
10-12-2017 12:00 AM
Description
We study how a European postal service organization (LogiCo) tried to create and appropriate value from big data. Our analysis uncovers four tensions shaping this process: 1) Using existing data versus finding data elsewhere; 2) Thin versus thick processing of data; 3) Self-analytics versus analytics service, and 4) Ex-ante versus ex-post proof of data value. We discuss how these tensions may have emerged by looking at the nature of the data and see three characteristics playing a role: granularity (the level of detail of the data), interconnectivity (being able to combine different data), and portability (being able to transfer and remotely access data). We argue that realizing value from big data is not a linear and straightforward process, but involves continuously playing with its characteristics. Finally, our findings implicate the need for value co-creation when trying to create and appropriate value from big data.
Recommended Citation
Günther, Wendy; Rezazade Mehrizi, Mohammad Hosein; Huysman, Marleen; and Feldberg, Frans, "Rushing for Gold: Tensions in Creating and Appropriating Value from Big Data" (2017). ICIS 2017 Proceedings. 4.
https://aisel.aisnet.org/icis2017/Strategy/Presentations/4
Rushing for Gold: Tensions in Creating and Appropriating Value from Big Data
We study how a European postal service organization (LogiCo) tried to create and appropriate value from big data. Our analysis uncovers four tensions shaping this process: 1) Using existing data versus finding data elsewhere; 2) Thin versus thick processing of data; 3) Self-analytics versus analytics service, and 4) Ex-ante versus ex-post proof of data value. We discuss how these tensions may have emerged by looking at the nature of the data and see three characteristics playing a role: granularity (the level of detail of the data), interconnectivity (being able to combine different data), and portability (being able to transfer and remotely access data). We argue that realizing value from big data is not a linear and straightforward process, but involves continuously playing with its characteristics. Finally, our findings implicate the need for value co-creation when trying to create and appropriate value from big data.