Collecting data: How can big data contribute to leaving no one behind and achieving the SDGs?
Collecting data is the first step in the data ‘life cycle’ and our first stop on the Road to Bern. This briefing note provides background points for our discussion on data collection, which will be held on 19 February 2020
The type of data impacts the way data is collected. Gathering personal health data requires data handlers to follow strict privacy protection regulations due to the sensitivity of the data. Rules for collecting weather and scientific data are somewhat less strict. All things considered, data as the new currency for innovation is an asset for sustainable development goals. What are the types of data that a data taxonomy should include? And how can this data taxonomy be used to adjust policies based on specific types of data?
The first dialogue in the ‘Road to Bern… via Geneva’ journey will address the following aspects of data collection: the technology behind data collection, the policy and standardisation elements, development aspects, and co-operation among the involved actors.
The technology behind data collection
Technically speaking, almost any device we use – from mobile phones and smartwatches to home devices – can be regarded as data collectors.
A lot of personal data is collected while we use the Internet: when searching for information on Google, communicating through e-mail, making online purchases, and using social media. Since such data is attributed (or can be re-attributed) to individuals, this type of data stands at the nexus of political controversies around privacy and data protection involving tech platforms such as Google and Facebook. How actors in Geneva use data generated by the main tech platforms? Are there examples of public-private partnership for using this type of data in health, human rights, and humanitarian activities?
International and scientific organisations have a variety of technical facilities and tools for collecting data, ranging from CERN’s experimental colliders to WMO’s weather observation stations. For instance, the WMO co-ordinates one of the most complex data networks worldwide, across its 191 member states and territories. Its Global Observing System collects data from 17 satellites, thousands of aircrafts and ships, and nearly 10 000 land-based stations, covering the oceans, land-bodies, and the atmosphere. At CERN, new advancements in data collection are being developed through sensor technologies for data collection. What other data collection technologies are being developed, and should we be concerned about them? How can organisations benefit from each others’ advancements and innovations in data collection technologies?
Policies and standards
When addressing the challenges of data collection, we also need to look at policies, standards, and regulations. For personal data, this includes privacy regulations such as the EU’s General Data Protection Regulation (whose regulatory arm extends beyond EU shores). The collection and sharing of weather and scientific data also require appropriate policies and standards.
The WMO’s core document is the WMO Resolution 40. The WHO Global Health Repository is the most extensive data commons on health indicators and statistics for all its member states (194 countries). It uses more than a 1 000 indicators, and has a network of six regional health observatories. It also compiles health data for each country in close consultation with them. What is the experience of the WMO in applying Resolution 40? Do other international organisations have policies in place on data collection, management, and sharing (resolutions, treaties, guidelines)? What are the available standards for data collection used by IOs? How can we facilitate the exchange of policies, regulations and standards on data collection? Would a repository of data collection policies be useful?
Data and their collection are in the nexus of development. Target 17.18 (SDG 17 – Partnership for the Goals) calls specifically on efforts to ‘to increase significantly the availability of high-quality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant for national context. Nevertheless, much needs to be done. As the 2019 SDG Report indicates, ‘the lack of accurate and timely data on many marginalised groups and individuals makes them “invisible” and exacerbates their vulnerability.’
The rule of thumb is that if a country is less economically developed, less data is available. Since sustainable development goals (SDGs) are the leading vehicle on our Road to Bern… via Geneva, the main question is how to improve the collection and sharing of data for developing countries. Data and SGDs are widely-discussed topics. The challenge for us will be to find new and innovative angles on data and development. Open questions include: what are the limitations of digital data collection especially among ‘bottom billion’ (people with access to networks and advanced digital facilities)? How could traditional data collection methods (e.g. interviews and surveys) be used by development actors? How to finance data collection in developing countries?
An important focus is on least developed countries which represent the 4th ‘invisible billion’ since in many of the countries there is no record of the population (WHO has set the ‘triple-billion target’: one billion more people better protected from health emergencies, and one billion more people enjoying better health and well-being.). The WHO has been pushing governing bodies (within ministries and externally), working groups, etc, to incorporate Accelerators on Data and Digital Health. What is the experience of the WHO and other organisations in getting more data from 4th or bottom billion of world population?
As already mentioned, when discussing development, data disaggregation also needs to come into play. The disaggregation of data by income, sex, age, race, ethnicity, disability, and geographic dimensions can fundamentally help to make the invisible visible (marginalised groups and minorities). What is the experience of international organisations in Geneva on data disaggregation?
More co-operation is needed among international organisations, governments, and public-private actors. Many public-private partnership initiatives focus on data. In many of them, international organisations need to access data generated by tech platforms on, for example, health, weather, migration, and nutrition. What is the experience, in particular, in dealing with disaster scenarios (such as, alerts communicated via mobile phones)? What is the current status of MoUs, agreements, and policy processes on public-private partnerships on data? What do international organisations see from the private sector in public-private partnership (e.g. access to data, data analysis expertise)?
Data collection will demand a sturdier interdisciplinary approach for collecting, managing, and sharing data. In our first dialogue, we will map these cross-cutting issue areas, such as the interplay between the WHO and the WMO on water and sanitation (SDGs 6 and 13). What is the experience of the WMO and WHO in using data for cross-cutting policy approaches? What are the other converging and cross-cutting examples that could facilitate a co-operative approach among different actors?