Citizens, the Private Sector and SDG implementation: Scale success and scrap the rest

SDG implementation requires the production of more and better data at country, sub-regional and city level. The public sector alone is not up to the task and so other actors need to be brought into the game.
02/02/2016 06:37 pm ET Updated Feb 02, 2017

(This post was co-authored with Till Zbiranski, PARIS21 consultant.)

Ten days ago, while leaders from business and government were in Davos sipping green tea and debating Europe's future, threats to the global economy and the emerging 4th industrial revolution, in Silicon Valley and elsewhere young entrepreneurs were hard at work developing programs, applications, codes, tools and projects that are changing the world as we know it. The range of cutting edge digital technologies - 3-D printing, machine intelligence, the internet of things, cloud computing, big data etc. - is already affecting our lives, from leisure to work, from identity to statistics.

The large-scale change brought about by the digital revolution will not only alter economics and society but also shift the way we think about and relate to data, statistics and measurement. A data revolution that "draws on existing and new sources of data to fully integrate statistics into decision making, promotes open access to and use of data, and ensures increased support for statistical systems" is of urgent need if we are serious about SDG implementation. Without better data we continue to fly blind, unable to monitor progress and enforce accountability. But to produce more and better data for the SDGs, we need to involve citizens and the private sector. Why?

Traditional and government-led data collection such as censuses and surveys are resource-intense, take a lot of time to implement and are sensitive to scaling. And while public administrative data systems in developed countries can instantly produce many (but not all) data needed for SDG monitoring, in developing countries there is only rudimentary capacity and tools to do the same. Complicating matters, funding for official statistical systems remains shamelessly low in developing and developed countries alike, and this will not fundamentally change in the near future. In short, the SDGs put a demand on public national statistical systems that simply cannot be met with the resources on hand.

So what can be done?

One element of the answer should come from engaging more with citizens and the private sector as both data producers and users. Today nearly everyone has access to a mobile phone, which can be a tool for data collection while producing data itself. In short, data is going to be "crowdsourced" or "citizen generated". Take as an example Wikipedia, arguably one of the most successful crowdsourcing projects with 10-times as many articles as the Encyclopedia Britannica English version (indeed Britannica stopped its print issue in 2010). Wikipedia has shown that it is possible to produce high quality information through citizens.

With respect to the private sector, today most digital data is coming from new sources such as modern information and communications technologies. But modern data analyses based on people's movements, desires, networks and socio-economic and emotional states is valuable not only for commercial use but also for the public good, such as measuring the SDGs.

These new sources of data from everyday people and the private sector should be seen as complementary to, not substitutes for, traditional ones. No single source can efficiently cover the complexity of sustainable well being. Censuses provide coverage while surveys grasp the intricacies of living situations; both have clear conceptual frameworks, but associated costs are often very high. Citizen-generated data and data from the private sector often lack design frameworks, are limited in thematic scope and prone to coverage bias. But they are also relatively cheap and easy to collect.

Synergies have already been demonstrated between public and private sources, and they are both desirable and technically feasible. However, as recent public-private data sharing models show, it's not the technical but the legal and ethical questions that stand in the way of change. Privacy, propriety and confidentiality issues are tricky and new territory. The focus becomes to design a business model that harnesses the benefits while minimising risks. With that task in mind, participants at a recent OECD workshop put forward the following three insights:

- First, the public sector's practice of paying only the marginal cost of data extraction rather than market value has to be rethought. Together with reputation risks, this helps explain why private sector data owners prefer cooperating with academia and other private sector actors over the public sector.

- Second, creating trust between the public and private sectors is key to jumpstarting co-operation. What can help is 'privacy-by-design' approaches through, for instance, bringing the code/algorithm to the data (not vice versa) and conducting analyses behind the firewalls of private companies. Some institutions like Flowminder have gained a high level of trust with the private sector. Flowminder has done groundbreaking work on disaster response management using mobile phone data, including during the earthquake in Nepal last year.

- Third, the development of more data-sharing, public-private partnerships (PPPs) should be supported. National statistical offices have a strong reputation for being confidential and should further build bilateral ties with data providers. Multilateral PPPs and regional/global platform solutions will help manage the increasingly diverse landscape of citizen and private sector data providers.

SDG implementation requires the production of more and better data at country, sub-regional and city level. The public sector alone is not up to the task and so other actors need to be brought into the game. The good news is that there are several promising initiatives, projects and business operations that can already be tapped. Let's build on those, learn lessons, scale success and scrap the rest.