The Data Revolution is now with us here. It now takes high-level skills to analyse and develop new solutions to many of the problems we face.
Unfortunately for Africa, we are acting as though nothing has changed in the recent past.
The continent is still weak on capacity to utilise emerging tools and methods of the emerging data sources for research and policy decisions.
Neither university programmes nor the research activity in Africa reflects the advent of Big Data. Yet the world has moved on to real-time generated data for research and making critical policy decisions.
Sometimes, decisions are made at the highest level to adopt new technologies but are rarely followed through.
For example, when African heads of state and government at the African Union (AU) Summit in January 2010 endorsed South Africa’s bid to host the Square Kilometre Array (SKA) project, many people thought that the continent’s scientific destiny had been shaped.
AU decisions are almost never cascaded to institutions that are supposed to take action.
More than six years down the road, Africa is still weak on capacity to utilise this multi radio telescope that promised to facilitate innovations, skills development and commercial potential emerging as a result of this new project in Africa.
There were other eight partner states around the African continent that were to build a network of radio telescopes, contributing to the project that would provide scientific communities with the world’s most advanced radio astronomy array.
These were Kenya, Ghana, Botswana, Madagascar, Mauritius, Mozambique, Zambia and Namibia.
SKA is the world’s largest radio telescope that is being built in Australia and South Africa to support scientific research across the world.
The project is expected to produce massive amount of data that will require high-level skills to analyse, manage the emergent large data sets and develop new products.
Some of the benefits expected out of this scientific experiment include use of sustainable energy sources and development of energy-efficient processing.
It is no longer a question of what Big Data can do or where it will come from. As Joris Toonders noted, “Data in the 21st Century is like Oil in the 18th Century: an immensely, untapped valuable asset.”
There are multiple sources of these data to deal with virtually every problem we face.
For example, we know that anti-corruption agencies in Africa would be more effective if they embraced Big Data and effectively used it in their investigations. That, however, is not happening.
No one has a clear explanation for the slow uptake but it is likely that there is no data analytic capacity or there is a strategy in place devised by these same agencies to fool the public that something is happening.
The key performance indicators for such agencies should never be how many people they have pushed to the courts (because some are dupes of circumstances) but rather how many people cannot account for their sudden wealth and in addition to prosecution, let the public deal with the rest.
In virtually all major corruption cases in Africa, there is an accompanying trail of data that can be obtained from multiple sources of data sets.
Even without sophisticated data analytics, a casual look at any real-time generated data like mobile money has a story to tell.
The police vetting process demonstrated that mobile money data alone was sufficient to prove culpability in corruption to the extent that many officers were not able to explain sources of their newfound wealth.
In healthcare, a recent collaborative cancer study by the Kenya Medical Research Institute (Kemri), Britain’s Queen Mary University and Oxford University analysed data from Nairobi hospitals going back 20 years and came to striking conclusions linking the occurrence of cancer in Nairobi to the tribal origin of afflicted people.
This was a significant step forward in the fight against cancer.
Next steps will perhaps look at common practices or genetic formations that could be causing cancer.
To build capacity for the 21st Century, we must build collaborative networks like in the case of health analytics that I just mentioned.
There must be fundamental mindset change to get the government, academia and private sector to work together in building capacity in this evolving critical enabler for innovative solutions. Above all, we all must become change agents.
Mahatma Gandhi once said, “If I have the belief that I can do it, I shall surely acquire the capacity to do it even if I may not have it at the beginning.”
I believe we can acquire the capacity to do it in Big Data.
The writer is an associate professor at the University of Nairobi’s School of Business.