Policy-Making and Data Economy at the city level: utopia or reality?
13:30-16:30 January 27

½ day
Beginner level

@ 1A


Despite the Data and the AI hype, a number of grey zones of exploitation and several open challenges still represent concrete obstacles in the value creation, above all in the private-public collaboration and the effective arise of a "Data Economy and Data Market” at the city level.

For instance:

  • Finding valuable data is still the main problem despite the impressive amount of digital information we produce every day and the Open Data movement.
  • Hiring professionals is hard, as well as team assembling. Furthermore, we observe a lack of people able to “translate and communicate” the data insights.
  • The gigantic ethic-of-data theme is only superficially explored.
  • Regulated systems failed to keep the pace of technology improvements (despite on the GDPR arrival).
  • The evaluation of models did not reach a universal benchmark yet. What is the best way to measure the goodness of a specific solution?
  • Bias are affecting Machine Learning and Deep Learning implementations.
  • Transition from “grid” (deterministic, functional, efficient, … ) to complex is hard, particularly for companies, organizations, and governments that have a strong legacy in infrastructures and processes.

Generally speaking: today data, ML, DL are effectively used in industrial and for-profit business cases while the data-driven evidence-based policy-making is far from reaching the initial promises.

Throughout the application of our conceptual framework canvas (namely the Data Ring - dataring.eu) we will explore different end-to-end scenarios in terms of:

  • Enabling factors
  • Best practices
  • Technical aspects
  • Threats


  • Workshop introduction (Who we are, Why we are here)
  • Our perspective on Machine Learning and AI open challenges, particularly in the field of Policy Making at the city level.
  • Interactive discussion aimed to collect ideas and common problems.
  • Data Ring Canvas explanation
  • Interactive session: attendants will be divided in groups and guided to fill the canvas using given use-cases
  • Group will presents the output of their work
  • Conclusions and Wrap-up

The main goal of the workshop is to brainstorm, analyze and define collaborative scenarios where local governments, companies and community of Data Scientists might collaborate to solve citizenship problems.


After a theoretical assessment where common language and notions will be shared, the participants will take part in a group activity aimed at “drawing" end-to-end scenarios where local governments, companies and community of Data Scientists might collaborate to generate social and economic value.

As a secondary output the participants (tech, executives and business people) will have a complete overview, and eventually more comprehensive perspectives, on data projects and will be trained to use the Data Ring tool to effetely address future data-driven projects.


The ideal group of participants we'll have both a technical background or experience in policy-making and connection with public administration to collect different perspective of the scenario we'll tackle together.


Stefania Delprete

Data scientist, TOP-IX


Christian Racca

Design Engineer, TOP-IX

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