Track 1.3 – Data Science for Innovation Challenges

Dr Filippo CHIARELLO, University of Pisa, 
PhD Paola BELINGHERI, Vienna University of Economics and Business, 
Prof. Andrea BONACCORSI, University of Pisa
Prof. Antonella MARTINI, University of Pisa

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Track's Contacts : 

filippo.chiarello[AT]destec.unipi.it
paola.Belingheri[AT]wu.ac.at

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The information field has changed dramatically over the past years, affecting the economy, technology, culture and society. However, these changes have left an even stronger mark on business systems (Jin 2015). Considering the mass of digital information produced in the past 10 years, companies have found themselves in a chaotic and constantly expanding digital universe. To innovate and stay competitive, companies must master methods and tools to prevent information overload, while gaining useful knowledge from the available data (Feng 2015).

The discipline of Data Science has emerged as a clear (although broad) field of research to solve data-related problems (Provost 2013). Data science is an interdisciplinary field that uses scientific methods to extract knowledge and insights from structured and unstructured data. It attracts researchers and encompasses methodologies from wide-ranging fields such as statistics, mathematics, information science, computer science , data analysis, machine learning and communication and is therefore an ideal tool to bridge the gap between research, industry and society (Waller 2013).

The objective of the present track is to collect works that use state-of-the-art Data Science tools and techniques to gather, transform, model and visualize data (Wickham 2014) to gain valuable information relevant for firm innovation. The scope is to use publicly available data to obtain a clearer view of which information sources contain the most untapped value and which methods and tools can be used to uncover it.

The main contributions are expected to highlight which information is relevant for different companies to build knowledge as a tool for innovation, in particular related to:

- data science for product innovation (Chiarello 2018a; Tan 2015): e.g. data-driven product development, A/B testing, patents analysis, product success evaluation, machine learning for innovation.
- data science for technology intelligence (Colladon 2018; Chiarello 2018b): e.g. brand analysis, competitors mapping, partners individuation, tools for knowledge visualization and communication.
- data science for open innovation & co-creation (Hoornaert 2017): e.g. papers mapping, open analytics, IP analysis, cloud computing.
- data science for new skill identification & mapping (Frey 2017): e.g. curricula analysis, job vacancies identification, job creation, new skills for innovation.

We expect to see contributions coming from the usual sources (e.g. open databases, patents, papers, social media) but we especially welcome contributions from less-known sources. Since Data Science is broad, we expect to showcase a wide range of methodologies such as machine learning, deep learning, natural language processing, image analysis or tools for data visualization and communication, to name a few.

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References

Jin, X., Wah, B. W., Cheng, X., & Wang, Y. (2015). Significance and challenges of big data research. Big Data Research, 2(2), 59-64.
Feng, L., Hu, Y., Li, B., Stanley, H. E., Havlin, S., & Braunstein, L. A. (2015). Competing for attention in social media under information overload conditions. PloS one, 10(7), e0126090.
Provost, F., & Fawcett, T. (2013a). Data science and its relationship to big data and data-driven decision making. Big data, 1(1), 51-59.
Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84.
Wickham, H. (2014). Tidy data. Journal of Statistical Software, 59(10), 1-23.
Tan, K. H., Zhan, Y., Ji, G., Ye, F., & Chang, C. (2015). Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph. International Journal of Production Economics, 165, 223-233.
Chiarello, F., Cimino, A., Fantoni, G., & Dell’Orletta, F. (2018). Automatic users extraction from patents. World Patent Information, 54, 28-38.
Chiarello, F., Trivelli, L., Bonaccorsi, A., & Fantoni, G. (2018). Extracting and mapping industry 4.0 technologies using wikipedia. Computers in Industry, 100, 244-257.
Colladon, A. F. (2018). The Semantic Brand Score. Journal of Business Research, 88, 150-160.
Frey, C. B., & Osborne, M. A. (2017). The future of employment: how susceptible are jobs to computerisation?. Technological forecasting and social change, 114, 254-280.
Hoornaert, S., Ballings, M., Malthouse, E. C., & Van den Poel, D. (2017). Identifying new product ideas: waiting for the wisdom of the crowd or screening ideas in real time. Journal of Product Innovation Management, 34(5), 580-597.

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