Can big data revolutionise the way we measure results in international development?

09 January 2017

We hear a whole lot about big data these days. For many people, “big data” means a flood of data, but what exactly is it? According to UN Global Pulse, information can be defined as “big data” when the data volume can no longer be managed with normal database tools. Big data is typically characterised by the 3Vs: volume of data, variety of types of data and the velocity at which the data is processed. Big data comes in different ways and can be divided in structured data and unstructured data.


We see big data typically used in the corporate sector to optimise performance and drive results. At PwC, we help our corporate clients solve their biggest problems with the power of perspective and our use of big data is an important aspect. For instance, we’ve developed SocialMind, a social media listening and analytic app that helps companies learn what customers are saying about them and their competitors. It monitors consumer sentiment so companies can react when needed.

Many of the big data tools and techniques used in the corporate world are increasingly available in the international development sector. According to the World Bank, more than 40 percent of the world’s population has access to the internet, with new users coming online every day. Among the poorest 20 percent of households, nearly 7 out of 10 have a mobile phone. Increased use of mobile devices in developing countries has led to greater availability of digital data sources including call details and geo locations. Other sources of digital data available in developing countries include information from sensors and satellites.

But how do we use this growing trove of digital data to analyse the results from international development projects, to learn and improve the impact of the work we’re doing? Donors typically spend significant resources gathering results through extensive monitoring and evaluation programmes. Evaluators usually conduct large-scale and costly surveys amongst beneficiaries to determine the changes which can be attributed to the development interventions, mostly after the programme has been completed.

With current advances in analytics, it is now possible to do this more effectively and efficiently, by gathering existing (‘big’) data and using open source tools to analyse these large amounts of data during project implementation. Global Pulse is taking forward several interesting initiatives looking at big data and M&E in the international development space;

Case1In Uganda, the type of roof a house has, is used as a proxy-indicator for poverty by the Uganda Bureau of Statistics. Traditional thatched roofs harbour pests and disease and are high maintenance. And so as soon as someone can afford to, they will upgrade to a metal or tiled roof. At the same time, a wide variety of remote sensing image sources is available which can show the type of roofs houses have and as such can be used for measuring sustainable development.

Case2Using social media monitoring tools, public tweets were analysed to evaluate the impact of the Every Woman Every Child Movement. A taxonomy of relevant keywords was developed to identify messages related to women’s and children’s health (for instance, searching keywords such as “maternal health,” “breastfeeding,” “vaccination of children”). The resulting 14 million tweets about women’s and children’s health were then analysed to identify spikes, trends and possible connections with real life events and campaigns.

Case3Another example is illustrated through PwC’s project with the Polish State Railways. When the Polish State Railways sought a better way to monitor its investment in railway infrastructure, it turned to PwC’s Drone Powered Solutions team for help. PwC helped the client use drones and data analytics to monitor the reconstruction of the bridge, by providing visual information on construction progress that could be tracked on a smartphone.

We’ve looked at the potential in using big data to monitor and better understand outcomes in international development. However, much remains to mainstream the use of these techniques in M&E. The examples are mostly pilots and the amount of development practitioners using big data for continuously monitoring and evaluating projects is actually quite limited. In order to determine the potential of the use of big data more time is needed to research:

Advantages-icon The advantages of using big data in M&E, such as the speed of data collection, real time evaluation and adaptation.
Practical-challenges-iconPractical challenges to using big data in M&E, such as the availability of the data and methodological constraints on how to determine attribution of results.
Enabling-factors-iconEnabling factors for applying big data in M&E, such as sectoral characteristics or geographies.

In our next blogs we’ll review these points in more detail to see if big data really can revolutionise the way in which M&E is practiced.

This blog is the first of a “digital in development” collaboration series between PwC Kenya, PwC Netherlands and PwC UK. Please contact Maaike Platenburg for more information.


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