To lift over a billion people globally out of extreme poverty. That was the objective of The Millennium Summit — a meeting of 189 members of the United Nations and other key institutional leaders — the largest in history as of 2000.
At the summit, the members agreed to develop a framework to reduce poverty and disease by half by 2015, and distribute the fruits of globalization fairly. In total, the summit was committed to achieving eight ambitious goals by 2015, collectively known as the Millennium Development Goals.
Millennium Development Goals or MDGs, as they were called, were deemed outright impossible by several experts. Take reducing poverty and hunger by half, for instance. Achieving the goal would demand scientists, policymakers, private institutions, and every other stakeholder involved to rethink agriculture systems, nutrition, and food delivery from the ground up.
And they did. Between 2000 and 2015, nearly every African nation saw stunning improvements in child nutrition. Many also improved in education — a critical indicator of progress. In Ghana, for example, stunting in children under the age of five reduced from 36% in 2003 to 19% in 2014. How did Ghana and other African nations achieve this monumental feat?
One word: data.
Lessons from Ghana
As Kofi Annan, Former Secretary-General of the United Nations has explained: “Data gaps undermine our ability to target resources, develop policies, and track accountability. Without good data, we’re flying blind. If you can’t see it, you can’t solve it.”
Policymakers and scientists relied on data gathered by the University of Washington’s Institute for Health Metrics and Evaluation, which depicted social, health, and economic conditions in high-quality indicators. Indeed, the data and statistical methods utilized were so advanced that progress could be gauged at the scale of villages.
Ghana’s example offers two groundbreaking lessons.
- First, that data can better and even save lives. We may associate data with businesses that use it to better understand their customers to maximize conversion. But 2020 threw light on the more urgent applications of data. Imagine the next pandemic, but without any data on where the infection has spread, how quickly, and how effective the proposed vaccines are.
- Second, that in a hyper-connected age of globalization, data ought to be more easily accessible to policymakers, scientists, journalists, private institutions, and even ordinary citizens. Achieving MDGs in Africa would really have been impossible if it wasn’t for institutions like the Institute for Health Metrics and Evaluation, which ensured that data was open and its insights available to everyone. Assuming we possess the infrastructure to access it, open data ensures that we all benefit from it, together, regardless of where we live.
The question is, did we learn from the two lessons? The answer, unfortunately, seems no.
‘Free the data’
Unlike the data gathered by the Institute for Health Metrics and Evaluation, the data circulating during the COVID-19 pandemic was not open. And if it was open, it was not well-funded.
In 2020, we saw very early the emergence of a multitude of websites dedicated to tracking the increase in cases, deaths, recoveries, and finally, vaccinations. Several dashboards, indeed, were quite advanced, allowing users to compare numbers across different variables, such as population size, density, and demographics. Further, most visualized the trends in neat charts to make communication more intuitive.
The data was often cited by policymakers and leaders worldwide. The problem is, the data was mostly private and behind really expensive paywalls. Nations provided other nations with only snapshots of data, fully knowing how severely it could slow down progress in tracking not just the spread of COVID-19, but subsequently, its variants. Otherwise, the data was locked behind paywalls that could cost thousands of dollars. How, then, were open dashboards developed? With the help of volunteers.
The consequences of private data go beyond health. In 2015, the UN extended the framework established in 2000 and set seventeen interlinked goals called the Sustainable Development Goals (SDGs). The seventeen goals include eradicating poverty, hunger, gender inequality, and injustice, among other ambitious initiatives. They are intended to be achieved by the year 2030. Almost seven years later, however, it is difficult to say which members have accomplished SDGs since the 231 indicators, though high-quality, are difficult to track.
Take energy, for example. Climate action is one of the most critical of the seventeen SDGs. However, before we formulate a plan of action to halt and even reverse climate change, we must first get hold of a detailed map depicting energy consumption across different regions. Such a map would detail where the energy is going (electricity, transport, etc.) and how much, across industries, applications, demographics, and other important variables.
The problem is identical. While many public organizations and volunteers contribute to data collection, most high-quality and comprehensive data is collected by the International Energy Agency, based in Paris. Even though governments fund the Agency, the data is very expensive. The consequence is that only the world’s wealthiest countries can access it and deploy effective climate action.
That said, even though underdeveloped and developing countries could access the data, there is no guarantee that the data is reliable. Especially, if the data is sourced from underdeveloped and developing countries. According to Nature, only 35% of sub-Saharan nations have updated their poverty data since 2015. After Brazil, India faced severe criticism for under-reporting COVID-19 cases by as much as half a million.
In summary, here is what COVID-19 made very clear.
- Data research is either under-funded or considered national property and is therefore sequestered, deterring collaboration to solve global problems.
- Data is closed and inaccessible, locked behind expensive paywalls, deterring less wealthy countries from accessing and using it to save and improve lives.
- A lack of funding leads to a lack of talent and adequate infrastructure, making data unreliable.
Toward a data revolution
All things considered, if we wish to accomplish MDGs and SDGs and make the world more equal, accountable, and fair, we need a data revolution.
The data revolution finds its roots in the fact that, given how connected the world is, we can’t solve problems alone and that data must be coordinated internationally. Otherwise, poverty, hunger, education, sustainability, and other social, economic, and environmental goals will just remain goals. Unaccomplished.
Here is a summary of solutions to the problems listed above.
- As data becomes central to not just political decisions, but also health, scientific, and investment, it is important that we ensure that data is transparent and accessible. To do so, we must ensure that institutions that engage in data research, public or private, are well-funded.
- A well-financed organization is also more reliable since it can invest in better technology and infrastructure to collect, store, process, and analyze data. Many organizations in underdeveloped nations still record data on paper or on a handful of computers. Better technology significantly increases efficiency and reduces the risk of data loss.
- A well-financed organization can also invest more in training and talent, making for not only more competent data monitoring systems, but also staff and authorities.
- Finally, standards and terminology ought to be clear and well-defined. Clarity and standardization ensure that data is transparent and inclusive, understood by not just subject experts, but also ordinary citizens.
Read more: Top 4 Skills That AI Won’t Replace
The value of data analytics is often seriously undervalued. The US alone saves over $2 billion annually by actively monitoring land and other geological data and using it to design smarter land-use plans and deploy more effective responses to natural disasters. A more connected and collaborative system for sharing and using global data could save billions more. But more importantly, such a system could save many more lives.
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