With the prevalence of mobile phones, interconnected devices, WiFi availability, GPS, etc., we now have access to more data than ever before. Not only is data experiencing an exponential pace of growth, it’s become easier than ever for companies – from large corporates to sole traders – to capitalise on this wealth of information, gaining insights on customer demographics, predicting buying trends, and even analysing user behaviour online.
In the development sector, data can be a powerful tool. For example, CGIAR (Consultative Group on International Agricultural Research) has established the Platform for Big Data in Agriculture, to democratise agricultural information and unleash greater impact on farming in developing countries. Data is also being used by development agencies to develop robust monitoring and evaluation methods, closing the feedback loop and allowing them to measure their ‘return on investment’.
In fact, United Nations Global Pulse was established in recognition of how much big data has to offer to the international development community; seeking to “accelerate discovery, development and scaled adoption of big data innovation for sustainable development and humanitarian action”. However, data has its limitations.
The quality of data that you’re working with is critical to making quality decisions. For example, Nigeria became Africa’s biggest economy overnight in 2014 – purely because it rebased its method of calculating Gross Domestic Product (GDP) to include whole industries such as information technology and telecoms that had previously been left out. Who knows what the ramifications were of government, companies, development agencies, etc. using this inaccurate data to make decisions.
Having quality data is a good start, but you need to be able to make sense of it. Governments and organisations are struggling to keep up with the rapid growth of data and there’s a shortage of data scientists and other analysts capable of interpreting it. Without the right skill-set, organisations are at risk of making decisions based on inaccurately interpreted data.
As ubiquitous as technology is nowadays, there are still people groups who are so isolated and disconnected from the rest of the world that there is limited data about them. This has been recognised through initiatives such as the Missing Maps project, which aims to map the most isolated and vulnerable parts of the world by crowdsourcing map data. However, strategic development projects rely on global and country-level data such as GDP, population figures, the number of smallholder farmers, etc. – the type of information which is far harder to access, let alone verify.
Data can be used in innovative ways to fight for better lives around the world. However, we need to remember that it shouldn’t be used in isolation to make development decisions. When looking at complex problems you need to collaborate, partner and share knowledge to drive key insights which can be supported by the data. Also, consideration should be given to those who are not captured by data – typically those who live in abject poverty. By complementing data sets with ideas, theories and case studies, a more informed understanding can be established to drive the desired impact.
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