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Big data mining for RIS3


Written by Christian Saublens

 

The concept

How to implement it?

Step in the RIS process

What can be expected?

A quote

References

 

The concept


Big data mining is an analytic process to explore date in search of consistent relationships between variables. The concept is becoming increasingly popular as business information management tool to guide decisions. In other words, it is the capability of extracting useful information from large datasets. Big data mining can bring an evidence-based view to the RIS3 place-based prioritization process.

 

How to implement it?


To collect as many data as possible regarding the stakeholders’ behavior, to classify them by sector activities and to assess if the historical data trends are in lign with the chosen RIS3 priorities. Hereafter is an example of data sources that can be looked at:

  • Pitchbook: venture capital deals
  • Bureau Van Dijck: mergers and acquisitions
  • OCO Consulting: foreign direct investment
  • Regional Competitiveness Scoreboard: trade flows
  • eCorda: participation in FP7

 

Step in the RIS process


  •  Step 1: Analysis of the regional context and potential for innovation
  • Step 2: Identification of priorities
  • Step 6: Integration of monitoring and evaluation mechanisms

 

What can be expected?


Sound evidence-based allows to say “no” to some priorities supported by traditional lobbyists. It also provides an in-depth view of the sectors where private investments are done. This allows to assess if the sectorial RIS3 priorities are in line with private behavior in terms of investment in export, equity and research as well as location/relocation of activities and headquarters. The big data analysis exercise can lead to three types of conclusions, i.e. (1) the RIS3 priorities are right as a lot of entrepreneurial activities occur in the chosen sectors; (2) the choice seems an overestimation as few entrepreneurial activities are backed by the big data analysis or (3) the data identy sectors not retained as a priority (hidden sections) of the RIS3.

Big data mining, together with a good segmentation of the enterprise portfolio, are necessary to assess if the priorities defined through the governance systems are backed up with evidence based on behavior of enterprises (entrepreneurial discovery process). This is illustrated by the following graph:

 

 

A quote


“The aggregation of all those regional data should be done centrally for all the regions.”  by Irma PRIEDL, Lower Austria (AGORADA 2014+, November 2014, Bilbao)

 

References


RIS3: the day after: Intelligence for economic (regional) development serving evaluation and the entrepreneurial discovery process

 

Mr Christian Saublens


 

Christian Saublens has more than 30 years of working experience in European trade organizations. Since 1992 he is the Executive Manager of EURADA, the European Association of Development Agencies, a network of 145 organisations. Christian has been involved in the organization of numerous conferences and meetings dealing with all matters related to regional development. He wrote several papers and working documents on business support schemes for SMEs. He played an important role for the dissemination in European regions of concepts such as benchmarking, business angels, investment readiness, proof of concept, clusters, open innovation, financial engineering, crowdfunding, … Several times Christian has been appointed as an expert by the European Commission and the Committee of the Regions.

christian.saublens@eurada.org

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