1. Introduction: Data to help achieve sustainable development
In September 2015, 193 heads of state and government agreed to set the world on a path to a more sustainable future by adopting the Sustainable Development Goals (SDGs) via the 2030 Agenda for Sustainable Development (“2030 Agenda”). These goals are extraordinary; they represent the greatest consensus of the global international community since the signing of the Universal Declaration of Human Rights. But they are also ambitious. To meet global emissions targets, we need rapid transitions to green energy and nuclear power. To eradicate hunger and malnutrition, we need rapid expansion of nutrition-sensitive agriculture and fundamental changes in our consumer practices. To eradicate poverty for all, we need universal access to social safety nets, healthcare, employment opportunities and effective monitoring systems to ensure the most vulnerable are not being left behind. Each goal will require considerable investment by each and every government. Each will require strong political will. And each will require new technologies, data and innovation.
This report is about the data piece of this puzzle — in other words, the ‘data revolution for sustainable development.’ To achieve the lofty ambitions laid out by the SDGs — and the associated U.N. landmark agreements of the UN Framework Convention on Climate Change’s 2015 consensus on climate change (“Paris Agreement”) and Sendai Framework for Disaster Risk Reduction 2015-2030 (“Sendai Framework”) — we need data and information systems that tell us what is happening in real time or in a timely way; enable us to understand people’s unique vulnerabilities and challenges; enable us to see how services are working and whether they are reaching those most in need; and enable us to anticipate future opportunities, shocks, risks, and trends so we can adapt accordingly. Location is also a critical element, since location data and information provide insights for decision making now and in the future. Governments and citizens alike must be able to access this rich world of data in order to plan, organize and achieve their objectives; to hold each other to account; and to catalyze change while also ensuring personal liberty, security and equality of access.
Current data systems do not fulfill these ends. Poverty and basic health data, such as that relating to child stunting, is often five or more years out of date, while birth registration is often even older. Administrative data like what children are learning, whether hospitals have enough medicine and whether people have access to transport are grossly underfunded in many parts of the world — if funded at all.
Worse still: Even when there is data available from third parties like civil society or private companies, it is often not used due to legal and institutional barriers, pre-conceptions about statistical methods and production processes, perceived quality issues or a lack of trained statisticians able to reconcile this data with official statistical records. But even if all this data were available, it would still be insufficient because it looks backwards. To respond to the immense problems of the 21st century, we should not only look to the past to learn lessons, but also look to the future to preempt challenges.
A 21st–century data system that is fit for purpose to both monitor and achieve the SDGs and the other U.N. landmark agreements should help governments to:
Plan and prepare for the future by anticipating climate change, environmental shocks and stresses, population dynamics, social challenges and changes, as well as new phenomena like mass urbanization and resilience challenges;
Manage and govern more effectively, providing policy makers with real-time or near-time information on the quality of services, the welfare of the population and the state of the environment so they can course-correct and change policies to meet changing demands;
Monitor historical progress and ensure we stay on course to meet our objectives, tracking changes over time and helping us to project where we are headed in the future.
Delivering these results requires a new approach to the development and management of data and information systems, which places data right at the heart of government. Data needs to be the bedrock on which governments plan, budget, design implementation strategies, and improve their performance. Only with a data-based and evidence-led approach to decision-making can we have any chance of meeting the wide-ranging and very ambitious SDGs by 2030.
But placing data at the heart of government does not mean only governments can and should produce and/or share data. Private companies, universities, civil society and other third-party actors will need to contribute given the scale of the challenge. These partners can offer new skills, technologies, sources of data and analytical tools to improve our knowledge and understanding of sustainable development. Innovative (often privately-owned) sources of data can also provide a useful “check and balance” on government reporting, ensuring governments are fulfilling their commitments to the SDGs and are carefully managing the data at their disposal. Governments will continue to be central to the production of statistics, but as the range of data producers expands, governments’ roles should morph from producer to coordinator of a broad data ecosystem. Statistical offices will transform themselves from information producers to knowledge builders. For example, they should be responsible for identifying useful, nongovernmental data sources that can help institutions and companies track the SDGs, and design policies and plans to achieve them. They should also assess the quality and reliability of third-party data and work to harmonize the data so it is broadly comparable with, or complementary, to official statistics, which will require capacity development.
That is not to say that partnerships are a silver bullet. Inviting more actors into the statistical production process and using new sources of data will create methodological challenges relating to sample sizes, differing methods and data interoperability, as well as raising important questions relating to data privacy, ownership and use. Poorly managed data partnerships risk exposing individual microdata (highly personal, individual data) to third parties who may not have the same developmental objectives in mind, or follow the same ethical principles official statisticians follow. These risks will have to be carefully managed by national statistical offices, as well as the executive branches of government. This is why one of several proposals of this report is that a new position within governments — the ‘Chief Data Officer,’ or CDO — be established to play a vital brokerage function: carefully sifting through alternative methods and sources of data, identifying quality partners and establishing partnership agreements with clear rules and expectations.
Notwithstanding the efforts made by the international statistical system since the adoption of the 2030 Agenda, the limitations of current national statistical systems for monitoring and achieving sustainable development are still relevant and well known. This report attempts to suggest solutions for building more effective and efficient data ecosystems at local, national and international levels. It explains the kinds of data needed to achieve the SDGs and identifies the roles and responsibilities of different actors, as well as the urgent changes needed to build architectures capable of responding to the increasing demand for high-quality, disaggregated and geo-referenced data. Table 1 lays out a Theory of Change, summarizing the key actions required.