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In our previous post we mentioned Big Data as being one of the facilitators, but also benefits of circular economy and sustainable development. What is Big Data? you might ask yourself. In this post we will try to get you acquainted with the term and its uses. Our findings are backed up by scientific papers, which you can find in the reference list if you scroll to the bottom. For now, let’s start with the basics.

What is Big data?

Big data is often perceived as a high volume of data, which is true only to a certain extent. There are more underlying analytical concepts when it comes to Big data’s true purpose, but De Mauro and colleagues (2015) synthesized the definition as follows: “The information assets characterized by such a high volume, velocity and variety to require specific technology and analytical methods for its transformation into value“.

Big data is characterized by the three big V's, being volume, velocity and variety. These characteristics are linked to almost any information in the world that is considered relevant or intriguing. Moreover, the information within the scope of Big data is used for analysis, projections, real time monitoring, maintenance and much more, which brings us to our next question.

Big data is at the same time made available and further developed by digitization. Structured data such as text is now enriched with unstructured data such as images, videos, audio files and similar. Stemming from either an individual or group, big data can target and/or access a variety of stakeholders (De Mauro et al., 2015).

What is the higher value behind Big data?

Big chunks of data are not relevant if they cannot be used by specific stakeholders. For some, lots of data might be even hindering their business or progress, if it cannot be used in a matter that facilitates growth. That is why, the input created by using Big data, always needs to result with a valuable output.

Gupta et al. (2018) point out big data functionalities can be utilized to generate insights for integrating processes and sharing resources. Big data can therefore be used by both smaller and larger companies to better manage maintenance, project future results, access relevant metrics and much more. As an end user, individuals are also using Big data on a daily basis by searching any available data base online.

However, not every useful data may be easily accessible and specific chunks of data may be protected by a paywall. Tseng et al. (2018) highlight how, at least within corporate means, data should be available to optimize resource usage and create a symbiosis within the supply chain.

What makes Big data different?

Big data is distinctive through two streams. First off, the analytical method used is dependent upon data volume. Secondly, patterns generated through data mining anaytics and machine learning are not dependent on any theory.

What makes Big data sustainable?

1. Big data is mostly free, allowing for both corporate or individual users to use it either for analytical or predicting purposes.

2. Big data reduces excessive resource use or generation through sharing information.

3. Its predicting features aid in adjusting corporate or individual actions to a more sustainability oriented agenda.


Big data as a concept will continue growing in every industry. Its usability still has to be developed and tested in certain areas, but with sufficient education any work area can be made more sustainable. Adaption to a new concept will definitely be needed for a variety of stakeholders. However, the benefits of sustainability, interdisciplinarity and cost efficiency of big data will be the main pillars for further development of Industry 4.0.

Note: We discussed Big data as a positive facilitator of circular economy, while we left out potential downsides of its generation and use.

Reference list

1. De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. In AIP conference proceedings (Vol. 1644, No. 1, pp. 97-104). American Institute of Physics.

2. Gupta, S., Chen, H., Hazen, B. T., Kaur, S., & Santibañez Gonzalez, E. D. R. (2018). Circular economy and big data analytics: A stakeholder perspective. Technological Forecasting and Social Change, 144, 466–474.

3. Tseng, M.-L., Tan, R. R., Chiu, A. S. F., Chien, C.-F., & Kuo, T. C. (2018). Circular economy meets industry 4.0: Can big data drive industrial symbiosis? Resources, Conservation and Recycling, 131, 146–147.


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