Data Challenge 2018


The challenge

Public markets in Europe represented over €2 billion, or nearly 19% of GDP. In fact, the efficiency of public procurement has often been questioned. The supplier selection phase, particularly regarding contract awarding criteria, has been discussed
extensively. Public procurement is defined as a contract concluded, in return for payment, between a public contractor and a private economic operator. The purpose of the contract is to satisfy needs in works, goods or services.

The objective of this challenge is to predict public procurement awarding. By participating in this challenge, you’ll contribute to further understand the supplier selection in public sector.


Data Contributors

Challenge data sets are provided by European Union open data portal.  Challenge data comes from TED (Tenders Electronic Daily). TED is the ‘Supplement to the Official Journal of the EU (“OJ S”), dedicated to European public procurement. The subset of Tenders Electronic Daily (TED) data cover most important fields from contract notices and contract award notices in CSV format.

The data come from the European Economic Area, Switzerland, and the former
Yugoslav Republic of Macedonia and cover the time period between
2009/01/01 and 2017/12/31. These data include the most important fields from the contract notice and contract award notice standard forms, such as who bought what from whom, for how much, and which procedure and award criteria were used.

Who Should Participate?

  • datascientits
  • researchers
  • students
  • startups and private sector organizations

How to Participate?

  • Step 1. Register
  • Step 2. Access to the Datalab
  • Step 3. Upload the data
  • Step 4. Build your model
  • Step 5. Submit model and results
  • Step 6. Award Winners announcement


  • Registration Deadline: May, 26, 2018
  • Challenge Launch: May, 28, 2018
  • Results and model submission: June, 18, 2018
  • Challenge awards: June, 28, 2018


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Challenge Coordinator: Olivier MAMAVI – Editor in chief – Management & Data Science –

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