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Trust, Velocity and Inclusivity in Data for Sustainable Development
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Trust, Velocity and Inclusivity in Data for Sustainable Development

In January 2019, as top leaders and thinkers from around the world gathered on the beautiful and pristine mountains of Davos to discuss the future, Nielsen was invited to talk about how Project 8, a digital data collaborative created by the United Nations Foundation and The Demand Institute (jointly operated by The Conference Board and Nielsen), is helping to prepare for the world with 8 billion people and beyond. According to the World Bank, the world’s population was just over 7.5 billion in 2017.

But before we get into the specifics that showcase how data collaboratives like Project 8 are necessary to plan for future human need, it’s important to understand a little more about the actual need itself.

Planning for future human need

In the years 1716-15 BC, Joseph of Egypt predicted that Egypt was going to face famine for seven years after seven years of prosperity. And as it happens, Joseph learned that when you come up with a problem, you are also responsible for finding a solution. So, Joseph was tasked with managing the looming crisis. He determined how much food the Egyptians would need during those seven years and made arrangements to store enough food to last during the famine. In fact, enough food was stored that the Egyptians survived a major calamity.

Despite the fact that this was thousands of years ago, Joseph became AI-enabled—and was in fact very efficient, as he derived the input for his predictive model was from a dream the king had.

While we don’t have a real life Joseph among us today, we do have data and technologies that can predict future human need—but only if, and this is a big if, we can create trusted environments, new ecosystems, and new contracts between public and private sectors to bring the right data and capabilities together.

In 2017/18, Nielsen and The Demand Institute set out to help the World Food Programme (WFP) in its desire to predict famine. We started by building algorithms using WFP data and Nielsen’s data science that predicted food consumption scores, often referred to as FCS, in two African countries. FCS is a core input into the next phase of work, which the WFP has taken on to build models that will predict future famine. With those in place, we will be able to plan and ration our resources to hopefully avert hundreds of deaths due to starvation, just like Joseph did.

Public Data Isn’t Always ‘Usable’ Data

But the WFP isn’t alone in looking to end hunger. For example, in 2013, a very capable team of researchers from the Brookings Institution got together to create a comprehensive database and analytical framework of indicators and indices to measure efforts to end rural hunger across developing countries. During its work, the team identified more than 100 indicators to build a framework to measure progress on this very important topic.

To achieve this, they had planned to use mostly publicly available data. They had earmarked approximately two years to complete their work. Sounds fairly straightforward, right? Let me tell you…it was anything but straightforward.

The challenge with data, you see, is that even though it may be publicly available, it may not be frequently updated, user friendly, analytically useful, or have complete global coverage.

Lorenz Noe has spent the past few years managing the data for the Brookings project and has had several conversations with Project 8 in the process. He and his colleagues want the international community to have dramatically better data available so that anti-hunger resources can be better targeted to practical implementation priorities on the ground.  

Big Data Will Drive the Future

Looking ahead, we know that big data will drive the future of predictive analytics. That is true for sustainable development as it is for any other sector. For example, the U.N. is building a big data platform to measure sustainable development goals. Specifically, the U.N. Statistical Commission created the U.N. Global Working Group (GWG) on Big Data for official statistics in 2014 to explore the benefits and challenges of the use of new data sources and technologies for official statistics and sustainable development goal (SDG) indicators.

Upon review, the GWG found Project 8 to be a good example of a functioning data collaborative and have applied some learnings from us in their work.

As a part of this initiative, one of the U.N. task teams is working to modernize the calculation of consumer price indices (CPI), which is a key economic indicator that has an impact on people, policy and business decision-making.

Together, we are exploring the possibility of leveraging Nielsen’s knowledge to build a value-driven public private sector collaboration to modernize CPI measurement around the world.

Let’s Build New Stories Together

There are many problems and challenges ahead of us. We also have many possibilities and options to wade through as we navigate the right way forward. It’s up to us to leverage the opportunities by adopting better strategies for using data and technology.

Project 8, and other collaboratives like it, present critical insight into understanding future human need. Using AI, we can answer questions like “How much food will India need?”, “How much energy will China require?” and , “How many new housing units will Brazil require?”. Answering questions like these is critical in ensuring that we understand and meet our future needs and those of generations to come.

It’s time to make some new stories. Let’s go.