Details of nearly a million startups from all over the world are now available through publicly accessible databases; this track explores how this wealth of information can be used. Professor Jean-Michel Dalle of Sorbonne University & Agoranov discusses this.

Track: Towards a Data Science of Startups: Utilising novel data sets

What do you think are the most disruptive influences impacting developments in your track?

The recent and widespread availability of large (several 100,000 of startups over 15+ years) and easily accessible startup datasets, such as Crunchbase or Dealroom, has really allowed for renewed investigations thanks to the use of newer data-intensive techniques.

These techniques include machine learning and artificial intelligence, of course, but also a larger array of methodological approaches from data science that are commonly used in computer science or elsewhere but that we could not use before, due to the limited dataset we had access to.

There were exceptions of course and in a sense, a seminal paper on venture capital networks such as Hochberg et al. (2007) now really stands a precursor of the kind of works that are now emerging rapidly.

Hochberg, Y. V., Ljungqvist, A., & Lu, Y. (2007). Whom you know matters: Venture capital networks and investment performance. The Journal of Finance, 62(1), 251-301.

Can you describe some recent findings in this area that are of interest to you personally?

There has been a few recent attempts at building “predictive” AI models for M&As or startup fundraising that are of course very interesting and intellectually challenging since they often make use of non-interpretable AI techniques.

We are also very interested in works dedicated to in-depth analysis of startup ecosystems or when researchers manage to link startup datasets with others such as patent databases (Tarasconi & Menon, 2017).

If someone was new to this topic what would you suggest they read to get a quick overview of the issues?

For instance (and though broader than the scope or our track),

• Obschonka, M., & Audretsch, D. B. (2019). Artificial intelligence and big data in entrepreneurship: a new era has begun. Small Business Economics, 1-11.
• Tarasconi, G. and C. Menon (2017), “Matching Crunchbasewith patent data”, OECD Science, Technology and IndustryWorking Papers, 2017/07, OECD Publishing, Paris.http://dx.doi.org/10.1787/15f967fa-en

Towards a Data Science of Startups: Utilising novel data sets is one of the tracks at the 2021 R&D Management Conference being hosted by the University of Strathclyde, Glasgow, from 7 – 8th July 2021- see more information here.

Track chairs:

Professor Jean-Michel Dalle, Sorbonne University & Agoranov. Visit his LinkedIn profile here.

Professor Matthijs de Besten, Montpellier Business School. Visit his LinkedIn profile here.