Data-driven, Data-based or Data Innovation?
Data are the building blocks of the ongoing digital revolution. They fuel incremental and breakthrough innovations to organizational process, products and services, and digital data underpin whole new ways of organizing productive activities.
Yet, we seldom stop and think what data actually are or exactly how data perform their critical role as the key resource and medium of organizing in contemporary economy. This is reflected in how we talk about data-driven innovation using ambiguous terminology that lacks resolution even at the most general level. The following is a simple typology for thinking clearly about innovations related to digital data.
First, innovation maybe data-driven in the sense that data analytics and artificial intelligence are used, for instance, to segment the market in a new way to increase the effectiveness of product development. For example, Netflix may study its subscribers viewing patterns to identify opportunities to serve as large portion of its viewers as possible with a new type of series.
Second, innovation may also be data-based in the sense that a new product or service is essentially fashioned out of data (Aaltonen et al., 2021). For instance, advertising audiences, institutional rankings, credit scores, and popularity metrics are literally measured into existence and would not exist without data. In contrast to data-driven innovation (such as the TV series in the example above), data-based innovation would not be possible without data.
Third, it is important to recognize that data itself can be an innovation. A data innovation is “a new valuable way to render phenomena as data that can be processed computationally” (Aaltonen and Penttinen, 2021, pp. 5928–5929). For instance, it has taken twenty years to develop eXtensible Business Reporting Language (XBRL) that allows the reporting of company financial statements in a machine-readable format. New types of valuable data do not appear by accident but need to be often painstakingly created.
The above typology is admittedly crude and the distinctions between the categories may not always be clear cut — a particular innovation can incorporate features from all the three types of data-related innovations. Even so, the typology shows that is possible and, indeed, I would argue critical to make more nuanced distinctions to understand the role of digital data in contemporary innovation.
REFERENCES
Aaltonen, A., Alaimo, C., & Kallinikos, J. (2021). The making of data commodities: Data analytics as an embedded process. Journal of Management Information Systems, 38(2), 401–429. https://doi.org/10.1080/07421222.2021.1912928
Aaltonen, A., & Penttinen, E. (2021). What makes data possible? A sociotechnical view on structured data innovations. Proceedings of the 54th Hawaii International Conference on System Sciences, USA, 5922–5931. http://hdl.handle.net/10125/71336
