Data for Policy 2020: Recap of Day 1
By: D P Blog Admin
Tags: Governance, Policy, Data Science, Covid 19, Open Data

Two of the social media volunteers at Data for Policy — Itzelle Medina Perea and Gulsen Guler— share their observations from the first day of the Conference. Note that many of the papers are available, and openly accessible, on the Data for Policy Zenodo community page here.

Our opening plenary chaired by Barbara Ubaldi — the Team Lead on Digital Government and Open Data at OECD — set the scene for the conference with a discussion about the relevance of data use for public value.

Zuena Aziz’s (Prime Minister’s Office, Bangladesh) presentation emphasised how data can be used to support the most vulnerable in society, including the use of technology to track SDG goals (see Bangladesh’s SDG tracker and Open Data portal). Sir Peter Gluckmann (former Scientific Advisor, New Zealand) reflected upon issues of public trust and privacy, noting that people have more trust in data exchange between individual and private sector, compared to individual and state, and that government use of data needs to be subject to oversight which should be co-developed with citizens. Jeffrey Schlagenhauf (OECD) also addressed the urgent and critical challenge for government, business and the third sector: how to make better use of data whilst maintaining — or improving — public confidence. Finally, Jeanine Vos (GSMA) presented on ‘AI for Impact’ in relation to Covid-19, noting how data can show unexpected results during lockdown, emphasising need for up-to-date real time data.

The first session chaired by Emanuel Baldacci (European Commission) addressed Data, Governance and Policy. The full list of presentations is in the image below. Some themes that emerged from this session included:

Data is the ‘colour pallete’ through which we paint a picture of our economies.

It is not all about big data: small data and qualitative insights are also useful and valuable.

We need to improve awareness of the value of open data and develop methods that work for both urban and rural areas (smart regions, not just smart cities).

What time periods are data used to predict for? The nuances and subtleties in data models need to take into account of more data inputs within broader contexts so that the insights can be more accurate.

Efforts towards a trusted, responsible, and inclusive data-driven policymaking should be fostered and supported.

The special track on Data Governance in the Public Interest, was chaired by Silvia Mollichi (the Ada Lovelace Institute). Some observations from this session:

It is not enough to have ethical principles; the key is to ensure the use of data is always for the public good.

Contextualising data to understand their value is key.

Data sharing entails a number of benefits but the potential risks exist when ‘oversharing’ data should not be overlooked.

Cities have a growing data stewardship mandate and should experiment and lead on data governance.

The panel chaired by Bilal Gokpinar and Steve Yoo (UCL School of Management) discussed data, analytics and digital transformation in the private sector, with contributions from Jonathan Bell (Cape Ann Asset Management), Itxaso del Palacio (Notion VC) and John Lee (KPMG). This highlighted how the availability of new sources of data has provided industry with unique possibilities for in-depth research and analysis — but it is the implementation of insights deriving from data that represent a primary challenge. The ability to manipulate data is going to be more important in the years to come: there is an urgent need for training more people in the use of data, not just data scientists.

The keynote lecture by David Hand (Imperial College London) — with Innar Liiv (Tallinn University of Technology) acting as chair — concerned ‘dark data’: the types of data that we can’t see.

Dark data can enable the things we wouldn’t otherwise able to do, for good and for ill.

There are fifteen types of dark data, including data we know are missing, data we don’t know are missing, and dark data caused by self-selection.

Types of dark data are not mutually exclusive, and different types of dark data can occur together.

There are several strategies we can apply to combat dark data such as using what we know about what we don’t know, getting extra data and finding substitutes for missing values.

There are situations where dark data can be to our advantage, especially regarding confidentiality and privacy.

If you are interested in dark data and want to learn more, this Imperial College post interviewing David Hand is a good starting point, and his book is available from Princeton University Press.

Sarah Giest (University of Leiden) chaired the first session in the Special Track: “For good measure’: The challenges of quantifying complex problems for policymaking”. This focused on the idea of co-producing value by collaboration among different actors (from academia to government bodies) especially in policy making context. Some observations from the presentations:

Co-producing a research strategy can be a slow and iterative process, therefore having constant feedback loops is the key to success.

Higher quality of data will enable us to create better policies. This is especially important when linking data coming from various sources.

In order to convince policy makers to say yes to academia-government collaborations, it is important to show clearly what will be the added value of such collaborations.

Chaired by Stefaan Verhulst (the GovLab, NYU), a special track focused on re-using data to address COVID-19 and pandemics:

Data has now become one of the main ingredients of daily life as the current pandemic has showed how data can help us understand situations better.

There were four main areas of concern related to COVID-19 apps that are (or were) available on Google Play Store: transparency; issues during software development phase; security; permission requests

Most governments are facing challenges when it comes to publishing data that can benefit the public and these challenges are mostly policy-related rather than technical ones. The most prominent challenge is the lack of capacity to quickly define and identify the purpose of data collection and sharing and not promoting data discovery enough.

The final session of the day, chaired by C. Leigh Anderson (University of Washington) looked at the interplay between data quality and development policy. Some reflections on this session:

Data that are collected using different methodologies can be used in complementary ways. The case study “Satellite Modeled Community Consumption” showed how combining satellite data and survey data can help fill in the data gaps.

Before trying to fill in the data gaps, we have to make sure why those gaps are there in the first place. Data gaps can occur due to offline inequalities (i.e. no access to electricity or internet).

We need innovative means for learning among policy actors. Such means include learning from data-driven anticipatory governance, learning from local and contextual knowledge and learning from service systems.

Itzelle Medina Perea is a PhD in Information Studies at the University of Sheffield. She can be followed on Twitter @itzellemedina

Gulsen Guler is a data literacy consultant with roots in social work. She can be followed on Twitter @gulsengulerdl and on Medium Gulsen Guler

This is the blog for Data & Policy (cambridge.org/dap), a peer-reviewed open access journal published by Cambridge University Press in association with Data for Policy, exploring the interface of data science and governance. Read on for five ways to contribute to Data & Policy.