It’s a truism that crisis spurs innovation; one has to look no further than engineering feats during times of war, social-policy progress in financial crises, medical discoveries accelerated by pandemics, etc.
Most recently, the Covid-19 pandemic has inspired a host of innovations. Not only has it motivated pharmaceutical companies to innovate their drug development and testing processes but also education institutions to digitize their teaching practices, and governments to sharpen their data collection and analysis capabilities for effective contact tracing.
However, many of the innovations spurred by Covid-19 are less novel than one might think.
The countries that have responded most effectively to the pandemic are in fact those that have been heavily investing in digital transformation for years. Take for example Singapore and China, which over the last five years have built up deep capabilities in data analytics and digitalization.
The Singapore Smart Nation program, launched in 2014, drives adoption of digital and smart technologies across all domains, including the public sector, in order to boost economic competitiveness.
Most of China’s recent major policy initiatives rely on big-data insights, including Healthy China 2030 launched in 2016. By utilizing big data, Healthy China 2030 is working to optimize the health-care system across the whole country and provide better, more efficient and more equitable care to all of its people.
While it may seem that Singapore and China have created methods for handling the pandemic on the fly, they are actually leveraging their well-developed data architecture and digital infrastructure.
These investments have paid dividends – saving thousands of lives and billions of dollars. While the pandemic has brought renewed attention to big-data analytics, it shouldn’t take a crisis to get governments to adopt data-driven decision-making. The benefits have been clear for a long time in nearly every policy sector, from public health to urban planning to taxation.
Yet in the developing world, there are still too many people who are not counted or their needs measured, as well as huge inequalities between the information-rich and information-poor. These governments face three key challenges: data gap; skills gap; and governance gap.
The term “data gap” refers to incomplete or outdated data. This is perhaps the most fundamental problem. Without data, you cannot hope to develop evidence-based policies. For many countries this is the hardest barrier to overcome simply because collecting data is expensive.
One organization attempting to overcome this problem is Atlas AI, which applies cutting-edge artificial-intelligence algorithms to satellite images in order to understand economic activity and crop performance in Africa. The data that Atlas AI collects would traditionally be gathered through censuses and surveys – hugely costly in regions with poor infrastructure.
Another is the Lacuna Fund, which supports the creation, expansion and maintenance of training datasets in agriculture and health for use by researchers and social entrepreneurs.
The skills gap is a shortage of professionals with data-science skills. When people think about data science, they think of corporations modeling the costs and benefits of a potential investment or e-commerce platforms crunching personal consumer data to figure out which products to market to whom. Data science has been commonplace in the business world for years, but there remains huge potential to put this in the service of development challenges.
Organizations like Data.org and DataKind are trying to do just that. They connect data scientists with non-governmental organizations to create solutions that improve people’s lives and inspire the social-impact community to adopt data-driven approaches.
The governance gap is discrepancies in data-collection methods and formatting standards, as well as data protection and security measures. These can lead to a deficit in public trust in the quality and responsible use of data.
The governance gap is certainly the most challenging of the three issues. How much personal data should governments collect on their citizens? To what extent should this data be shared in order to drive solutions? How might algorithms be designed and built to minimize bias of certain groups of citizens?
To answer questions like these, we need to secure consensus on data sharing, ownership and privacy standards. The United Nations Development Program’s NextGenGov program and AVPN India’s Leadership Lab, two initiatives supported by the Rockefeller Foundation, aim to promote global conversations on issues such as bringing data and digitization into the heart of government.
We have seen a dramatic acceleration in digital transformation over the past two decades and especially in the last six months, but just because tools exist doesn’t mean they’re being deployed to solve global challenges. In fact, often areas where the most meaningful progress can be made are those where the barriers to implementation are highest.
We live in a pivotal moment when rapid advancements in data technologies offer governments the means of controlling a global pandemic. We must build on this momentum by putting newfound innovations and capabilities in the service of all development challenges.
Deepali Khanna is managing director of the Asia Regional Office of the Rockefeller Foundation.