Data for Policy

Name: Ayse Kok

Organisation: Individual Consultant (ex Nato and UN)

Country: United States of America

Stakeholder Group: Civil Society Organisation

Thematic Area: Access

Session Title: Data for Policy

Session Format: OPEN FORUM (30/45/60 MINS)

Co-organizers Name (if any):

Speakers Name: Ayse Kok

Speaker Status: confirmed

Relevance of the issue with specified session theme: Data Science is emerging as a key interdisciplinary research field to address major contemporary challenges across sectors. Particular focus on the government sector offers huge potentials to advance citizen services and collective decision-making processes. To reflect the diversity of skills and knowledge required to tackle challenges in this domain, Ayse offers an open discussion forum for all stakeholders. Five major themes constitute the basis for discussion throughout the conference: the revolution in government service provision using Big Data, data collection and its ethical implications, computational methods and technical challenges, methods of data collection and modelling, new and emerging sources of data collection, the application of data to various policy areas both new and old. This session aims to provide a space for ideas and people to mix and for a healthy discussion to take place which allows academics, policy makers and technical experts to interact with people from beyond their own disciplines. In the words of Professor Anthony Finkelstein, Chief Scientific Advisor for National Security to the UK government: Data for Policy provides a unique opportunity to have a dialogue across many disciplinary boundaries, it’s a forum in which we can ask questions and move collectively towards solutions, we can share methods. I think it’s invaluable.

Content of the session (Objective of the session): Scope: Topics covered include but are not limited to the following

Government & Policy

Digital era governance and citizen services, public demand vs. government response, using data in the policy process, open source and open data movements, policy laboratories, citizen expertise for government, public opinion and participation in democratic processes, distributed data bases and data streams, information and evidence in policy context, case studies and best practices.

Policy for Data & Management: Data collection, storage, and access; psychology/behaviour of decision; privacy, trust, public rights, free speech, ethics and law; data security/ownership/linkage; provenance, curation, expiration; private/public sector/non-profit collaboration and partnership, etc.

Data Analysis: Computational procedures for data collection, storage, and access; large-scale data processing, dealing with biased/imperfect/uncertain data, human interaction with data, statistical/computational models, technical challenges, communicating results, visualisation, etc.

Data Sources: Government administrative data, official statistics, commercial and non-profit data, user-generated web content (blogs, wikis, discussion forums, posts, chats, tweets, podcasting, pins, digital images, video, audio files, advertisements, etc.), search engine data, data gathered by connected people and devices (e.g. wearable technology, mobile devices, Internet of Things), tracking data (including GPS/geolocation data, traffic and other transport sensor data, CCTV images etc.,), satellite and aerial imagery, and other relevant data sources.

Policy/Application Domains: Security, health, cities, public administration, economy, science and innovation, finance, energy, environment, social policy areas (education, migration, etc.) and other relevant domains.

What is USP of the session: Scope: Topics covered include but are not limited to the following: Government & Policy: Digital era governance and citizen services, public demand vs. government response, using data in the policy process, open source and open data movements, policy laboratories, citizen expertise for government, public opinion and participation in democratic processes, distributed data bases and data streams, information and evidence in policy context, case studies and best practices.

Methodologies: Qualitative/quantitative/mixed methods, gaps in theory and practice, secondary data analysis, web scraping, randomised controlled trials, sentiment analysis, Bayesian approaches and graphical models, biologically inspired models, real-time and historical data processing, simulation and modeling, small area estimation, correlation & causality based models, and other relevant methods. Data Sources: Government administrative data, official statistics, commercial and non-profit data, user-generated web content (blogs, wikis, discussion forums, posts, chats, tweets, podcasting, pins, digital images, video, audio files, advertisements, etc.), search engine data, data gathered by connected people and devices (e.g. wearable technology, mobile devices, Internet of Things), tracking data (including GPS/geolocation data, traffic and other transport sensor data, CCTV images etc.,), satellite and aerial imagery, and other relevant data sources.