SCIENTIFIC NEWS AND
INNOVATION FROM ÉTS

Big Data and Creativity


Lorena Escandon
Lorena Escandon Author profile
Lorena Escandon is a PhD candidate at ÉTS automated production engineering department in Montreal. She specializes in "Big Data" use to support creativity.

Mickaël Gardoni
Mickaël Gardoni Author profile
Mickael Gardoni is a Professor in the Automated Manufacturing Engineering Department at the ÉTS. His research interests include innovation management, knowledge management, technology transfer and Product Lifecycle Management (PLM).

Patrick Cohendet
Patrick Cohendet Author profile
Patrick Cohendet is a professor at the Department of International Business, HEC Montréal and the co-founder of the center MosaiC. His research interests include economy, knowledge management, innovation management, and technology transfer.

SUMMARY

Big Data may mean many things, but for most of us, it seems to be the prerogative of large companies. Big Data could also help you to be more creative during short ideation session. How? This first article on this subject will help you to understand how to use Big Data when you need to do creativity. As you will see, it could be really helpful!

Creativity is one of the most important traits of this generation, companies that do not encourage creativity cannot innovate, and if a company does not innovate, it will be obsolete. However, there is a latent need for companies to be creative faster. Idea generation (ideation) sessions are one of the available options companies have to generate ideas by bringing together participants from different domains or areas of expertise, and facilitate the exchange and creation of knowledge for a specific purpose. However, participants in creativity sessions usually gravitate towards known solutions [5] and popular ideas are frequently recombined [4]. To create new ideas, individuals must form new combinations of knowledge they already possess, but to generate really innovative solutions, we must find a way to motivate participants to make wild combinations [4].

We can do this by making sense of the incredible amount of information generated every day, and big data can help! Big data enables organizations to analyze their data in a way that was not possible before, by bringing together different sources of information and finding trends that are only visible with large amounts of data. By using Big Data, the information will reveal trends and connections that were previously unseen.

Big data is characterized by three criteria: volume, velocity and variety [7]. Furthermore, big data considers also the potential relationships between the data and the need for new tools to be able to exploit the data [6].

Big data in idea generation sessions

We identified that, because of its characteristics, data can be used in four moments of the collaborative idea generation process:

1. Problem / need identification

As mentioned before, big data can help companies identify trends that are only visible when analyzing large amounts of data. This is useful to visualize the gaps in a domain [9]. This option is suggested for organizations that already have information they would like to include in the session. For example, a company with patents, machinery, etc. would benefit from identifying if there are any areas of opportunity, for instance in new product development, using the already possessed know-how and resources. This could be part of the work in an ongoing team.

Figure 1. Flow of information to use big data for problem or need identification.

Figure 1. Flow of information to use big data for problem or need identification.

2. Information gathering

Information gathered from different sources can be mined and used as input for information gathering, thus increasing the external stimuli for participants [3]. For ideation sessions with limited time, it would be good to begin with a knowledge base from where participants can build upon. This option is also interesting when the participants are not familiar with each other, to provide a basis for communication. It is also useful for creators working individually when they are at a stale mate.

Figure 2. Flow of information to use big data as input.

Figure 2. Flow of information to use big data as input.

3. Idea generation (bisociation)

If used in real time to analyze data generated by the users (for example, in cases where a crowd is providing ideas in an online platform), big data can be used to identify which ideas are not being connected, but are already in the knowledge base of participants. The purpose would be to enable bisociation, to connect two frames of reference previously considered to be incompatible [8, 10]. If the organization has the tools and human resources to handle using big data during the creativity session, it could be used to analyze the contributions by participants. This would work better with large groups or crowds using an online platform.

Figure 3. Flow of information to use big data for bisociation (identifying missing connections)

Figure 3. Flow of information to use big data for bisociation (identifying missing connections)

4. Evaluation

A wealth of information is generated in ideation sessions; however, companies and organizations will usually keep a few of the concepts and not take advantage of all the ideas generated by the participants throughout the session. Big data can be an aid to find the concepts with the most interest and obtain insights from the session [1]. This option is valuable when the group was large, and there is the possibility to transfer all the data to a digital format (for example, making the transcripts, using voice recognition) or when the session was held virtually.

Figure 4. Flow of information to use big data for insight.

Figure 4. Flow of information to use big data for insight.

By harnessing data in the different phases of the idea generation sessions – for information gathering, as input, for bisociation and for evaluation -, it could be possible to see an improvement in the complexity and variety of the resulting concepts. This topic is garnering great interest in the creativity and innovation academic community, Luc de Brabandère sums the sentiment nicely: “If Big Data can help us discover, then it rests upon us to invent” [2].

Research paper

This research will be presented on March 5th, 2015 at the Fifth International Conference on Industrial Engineering and Operations Management (IEOM 2015) in Dubai, United Arab Emirates.

Escandon Quintanilla, M.L., Gardoni, M. and Cohendet, P. (2015, March) Opportunities to Exploit Big Data in Idea Generation Sessions. Abstract presented at the 5th International Conference on Industrial Engineering and Operations Management (IEOM 2015), Dubai, UAE.

It is part of an ongoing PhD project supervised by Professor Mickaël Gardoni at the Department of Automated Production Engineering at ÉTS and Professor Patrick Cohendet at HEC Montreal.

This article will be followed by another one titled : How to Use Big Data to Create? It will be published on Substance ÉTS soon.

Lorena Escandon

Author's profile

Lorena Escandon is a PhD candidate at ÉTS automated production engineering department in Montreal. She specializes in "Big Data" use to support creativity.

Program : Automated Manufacturing Engineering 

Research chair : Canada Research Chair in Biomaterial and Endovascular Implants 

Research laboratories : NUMERIX – Organizational Engineering Research Laboratory for the Digital Enterprise 

Author profile

Mickaël Gardoni

Author's profile

Mickael Gardoni is a Professor in the Automated Manufacturing Engineering Department at the ÉTS. His research interests include innovation management, knowledge management, technology transfer and Product Lifecycle Management (PLM).

Program : Automated Manufacturing Engineering  Innovation Management  Operations and Logistics Engineering 

Research laboratories : NUMERIX – Organizational Engineering Research Laboratory for the Digital Enterprise 

Author profile

Patrick Cohendet

Author's profile

Patrick Cohendet is a professor at the Department of International Business, HEC Montréal and the co-founder of the center MosaiC. His research interests include economy, knowledge management, innovation management, and technology transfer.

Author profile


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