Statistical Data Analysis

Africa Pitch: A Big Data Challenge?

Asking different teams to pitch a proposal in order to win funding is a tried and tested format. But I was particularly interested in this one as the brief was so broad. The Africa Data Challenge, hosted by the Planet Earth Institute and conducted at its second #ScienceAfrica UnConference, required innovators to propose a data-driven project which would have a human impact on the continent. The winner would receive £7000 ($11000) to implement their project.


The event

The Planet Earth Institute received over 200 (1000 word or less) proposals from all over the world. These were then shortlisted to 10 contestants who had to submit a refined document. From there, five finalists were selected to pitch at the event.

At the end of the day, two sums of £7000 were awarded, to TReND in Africa for African scientific training, and the AWP Network Agropreneur Project for Nigerian farmers. I’ve listed the details of these and all the finalist projects below – some are still clearly worthy of funding.


The contestants

Developing specific scientific skills - WINNERstargif

Organisation: TReND in Africa – Sarah Hoey.

Solution: A workshop to provide a bioinformatics approach to genomics.

In practice: A bid to bring the latest research techniques to a group of 30 African post-graduate students. The aim is that this group will then spread their knowledge out across the continent.

Benefits: Hoey explained that this will “allow students to acquire new unavailable skills” it will help develop “a new generation of data scientists” and it will “propel Africa to scientific independence”.

Comments: This was a niche solution which honed into a specific branch of science. I can see why it won but didn’t feel it offered as much as some of the larger umbrella ideas.


Training Nigerian women farmers - WINNERstargif

Organisation: AWP Network Agropreneur Project - Mary Olushoga.

Solution: To use data to help women farmers improve their techniques. 

In practice: To run intensive three-day training sessions for previously ignored female farmers. This would allow female farmers to learn best practice on a 1000 square-metre demo farm and produce much-needed data on this overlooked group.

Benefits: Nearly half the people in Nigerian agriculture are women and yet they’re “left behind” from training programs. This initiative should help women move “from peasant farming to commercial farming,” said Olushoga.

Comments: This struck me as the best idea on paper and it ticked a lot of boxes. However, in practice, it seemed to be less about data than some of the other solutions. It also appeared to overlook the fact that while a lot of women are farmers, the majority of these are not single women but are part of wider families.


Collecting data on professional engineers

Organisation: World Federation of Engineering Organisations (WFEO) and Federation of African Engineering Organisations (FAEO) – Andrew Lamb.

Solution: To collate a new dataset on African engineers based on those who are members of professional bodies – this information does not currently exist.

In practice: To phone up professional organisations across the continent and produce new, country-by-country, up-to-date information on the current volume of engineers. The long-term aim is to map the future shortage in order to put in place counteractive solutions.

Benefits: “Engineering has got a problem in Africa,” said Lamb. By his calculation, based on the limited data available, there is one engineer per 100,000 people across the continent. This would be the equivalent of 83 engineers in London. He wants to provide an accurate picture of the current numbers in order to provide the necessary skills to train the engineers needed to build much-needed African infrastructure.

Comment: I really liked this solution. The lack of specific data is one of the real challenges across Africa and this provided a clear, systematic answer to solve a need.


Setting a curriculum for Data Scientists worldwide in Kenya

Organisation: Research Data Alliance and CODATA - Dr Andrew Harrison.

Solution: To run workshops to set the Data Science curriculum across East Africa.

In practice: Data Science is only just being seen as a separate academic discipline. There is still some confusion as to how this will work in practice. Someone needs to sit down and work out the logistics.

Benefits: There is such an urgent need for Data Scientists that, like footballers, they hold a premium. Jomo Kenyatta University of Agriculture and Technology (JKUAT) already has an excellent reputation for Data Science and is supported by IBM which holds a lot of the continent’s data. As Harrison put it, “if everyone goes to JKUAT [to ask for help with Data Science teaching] maybe they will become the Manchester United of the future”.

Comment: This struck me as a very smart way to put Africa on the scientific map. It would firmly place an important scientific discipline in Kenya. But above all it would provide an excellent PR job for the continent. Sometimes African solutions are too local – this would benefit the region and the globe – which would ultimately help Africa more. I hope it gets funding elsewhere.


Providing a process to help local problems in Uganda

Organisation: Makerere University, Uganda - Ernest Mwebaze.

Solution: Student projects and workshops to help solve local problems.

In practice: To run workshops based on 10 teams of four students. These would be competitively selected and provide an equal balance of girls and boys. They would focus on using data to solve problems in the region.

Benefits: 40 students would put a concerted focus into providing data solutions for ongoing problems. This would include entering bigger competitions for more funding and would act as a conduit to funnel change.

Comment: There is an ongoing need to find solutions to a range of societal issues. What I liked about this was that rather than focus on one specific problem, it looked to put a framework in place to enable students to a tackle variety of challenges. Small-scale solutions are very limited but by organising a process to facilitate wider initiatives, it could help tap into the bigger picture. I did wonder if an equal mix of girls and boys was a bit too much like positive discrimination though – I imagine it would be far easier for a girl to get into one of those workshops than a boy.

What does this show about the challenge of data in Africa?

The challenge of data in Africa is enormous. It spans the sheer lack of available data compared to elsewhere, the issue of which sources are reliable, and the race for data ownership. At present IBM is probably doing the most systematic job of gathering and processing data across the continent. Yet beyond the work of this behemoth there are a wide range of small-scale data driven projects taking place on the local level. The Data Challenge event reflected many of these wider issues.

Ultimately, it threw up the question: to solve the big data conundrum in Africa, do you need to go small-scale and hyper-local, or big picture and overarching? This event seemed to conclude the former. My view is it needs the latter.


Kathryn Cave is Editor at IDG Connect


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