Making sense of data: familiarizing it or keeping it distinctive?

Increasingly, more and more businesses want to jump on the data-driven bandwagon. Data is now seen by firms as an important asset to their organization. They can use it to improve decision-making, to update business processes, or even as a driver for innovation (data-driven innovation). For example, a data-driven innovation you might use weekly is the Discover Weekly playlist of Spotify. Based on your listening data, Spotify created a service that provides users a weekly personalized playlist that matches their music preferences.

Data, the magic solution for everything?

However, using data to innovate your business does not always lead to immediate success. Several studies have shown that 77% of the executives that have adopted data initiatives find it challenging to really benefit from data-driven innovations (Bean & Davenport, 2019). Interestingly, the barriers to data-driven innovation are often non-technological and instead caused by complications that can be contributed to humans or outdated organizational processes.

Smart maintenance
We wanted to understand how and why decision-makers use and interact with data. For several months, we followed a project of a Fieldlab in the Netherlands in which different organizations, with various capabilities and backgrounds, together worked on a project to make the maintenance of large machinery ‘smart’ by using sensors to collect data (such as vibration and voltage) on the condition of the machines. These sensors had the potential to indicate to the project team the ‘health’ of the machine. This was, however, easier said than done. Although the team had access to a large amount of existing data and collected data themselves through the sensors, making the data valuable and actionable was often a challenging process as the data, in itself, often carried much ambiguity. Understanding the data, right-of-the-mill, was therefore difficult.

By studying the team its interactions with each other and with the data, we were able to identify two practices — familiarity and distinctiveness framing — about how they went about in making sense of the data to make it actionable and valuable. First, through a familiarity frame, they tried to relate data insights to metaphors and images with simple language, thereby making it more recognizable and easier to interpret and comprehend. While this enabled the project team to make better sense of the data and thereby create a shared understanding, this also led to not everybody fully contributing and participating equally. Second, through a distinctiveness frame, they emphasized the novelty and unique characteristics of the data that could lead to innovation instead of only improving the machine maintenance. This sparked insightful discussions in the team as this invited other project participants to provide their own views on how the data could be best used for innovation. By emphasizing the unique characteristics and giving examples of how the data could lead to innovation instead of only gaining insights from it, the project team was able to make sense of the value of the data and how it could become a driver for innovation. The value of using these two practices together was in creating a shared understanding of the data and the project goal and in emphasizing how the data stimulate innovation.

Sensemaking to make data valuable
The results from this study thus show that it is not only important to focus on the technology but to also address specific attention to potential human and process barriers. Data, as this study shows, does not become actionable or valuable until people are able to make sense of it which they can accomplish by familiarity framing or distinctiveness framing.

We hope that this study provides firms with more insight in how they can work with data and engage in data-driven innovation.

Also interested in working and innovating with data in the smart maintenance industry? Check www.techport.nl

Najla Barikzai & Dennis van Kampen

About Najla Barikzai
My name is Najla and I recently finished my master’s in Digital Business and Innovation and currently started a second master’s degree in Data Science and Marketing Analytics. During my studies, I was intrigued by how valuable data can be for businesses and how difficult it can be to bridge the gap between data scientists and management. This sparked my interest in data science and I wanted to gain more knowledge to understand both worlds. I would love to work in a data-driven, innovative, and international work environment to translate data science in the context of the business strategy.

If you have any interesting opportunities, such as an internship, or want to get in touch? Then connect me on LinkedIn!

References
Bean, R., & Davenport, T. H. (2019, February 5). Companies Are Failing in Their Efforts to Become Data-Drive. Harvard Business Review. Retrieved from https://hbr.org/2019/02/companies-are-failing-in-their-efforts-to-become-data-driven