The hackathon pitching session recordings are available here: https://webcast.ec.europa.eu/hackathon-pitching-20230306
For presentations of the hackathon teams, see here
Eurostat organised the fourth round of the European Big Data Hackathon from 2 to 7 March 2023 (including the presentation by the winners at the NTTS), as an in-person meeting in Brussels. The purpose of the 2023 hackathon was to foster expertise in using Big Data sources relating to financial transactions for producing innovative ideas for products and tools relevant for the EU policies.
The European Big Data Hackathon is organised by the European Commission (Eurostat) every two years and gathers teams from all over Europe to compete for the best solution to a statistical challenge. The teams develop innovative approaches, applications and data products combining official statistics and big data that can help to answer pressing EU policy and/or statistical questions.
For points relating to logistics and/or questions on hackathon procedures, please contact our functional mailbox: ESTAT-EU-BD-HACKATHON@ec.europa.eu
The Hackathon took off on 2 March 2023 with the announcement of the challenge, and closed on 6 March when the teams presented their solutions. The winners were awarded on 7 March 2023, at the first day of the NTTS 2023, where they had the opportunity to present their solutions more in detail.
Participation in the Hackathon
Twenty-four hackathon teams of National Statistical Institutes’ experts and four teams of students from EMOS network universities competed in the 2023 European Big Data Hackathon.
In the 2023 European Big Data Hackathon, the teams were challenged to design an early warning system relevant for EU policy makers. It was up to the team to choose their own policy question that they wished to answer with their application.
2023 European Big Data Hackathon
Devise a data analytics tool (with data visualisation features) that:
Based on the data analytics tool, build an early warning system for EU policy makers.
Please note that, as a first step, you should analyse the data quality, in particular its consistency over time, and might need to choose a subset of data on which to base your data analytics tool.
Please document and justify your choices.