Time is nigh for a data revolution. To understand why, I have to take you back in history. Between 1760 and 1840, a major transition in human history took place.
It was the era of the industrial revolution, when the world moved from hand-production methods to machines.
Other forms of manufacturing, such as chemical and iron production, started and were used to improve efficiency in water power, leading to new technologies in steam power and more efficient engines and machine tools.
Most of this industrialisation took place in Europe and North America. Africa lost the opportunity then and we may be on the verge of losing yet another opportunity.
The world is about to experience another transition, with an impact that is equivalent to the industrial revolution. This is the Data Revolution.
We are beginning to change the way we make decisions, from macro to micro approaches that have the potential to address some of humanities greatest challenges, ranging from climate to achieving the UN-declared post-2015 sustainable development goals.
Experts say that a critical enabler for realising this potential is the exchange of data assets in new, collaborative and innovative ways.
In particular, sharing data and data models currently held by the private sector holds transformative opportunities. Linking these assets with those of public, personal and academic origin, and making them widely accessible for social innovation, would greatly increase the richness of the space of data use for social good.
Although the African Union has adopted the Data Revolution Consensus, there is urgent need to begin creating partnerships with the entire data ecosystem – private, public, civil society, individuals, research institutions, and multilateral agencies.
We also need to build the capacity of handling data in ways that can drive innovative solutions by leveraging on modern tools to gather, analyse and visualise these data from all manner of sources like never before.
The objectives of the transition is to obtain as accurate data as possible and move away from estimates that don’t measure or give proper interpretation of what data is today.
For example, several countries are using mobile network big data for informing transportation and urban planning. Lisa Amini, Eric Bouillet, and Francesco Calabrese in their 2011 research paper ‘‘Challenges and Results in City-Scale Sensing,’’ found that road congestion is proving to be an increasing problem for countries experiencing rapid growth.
They sought to create analytics, optimisations and systems for sustainable intelligent urban environmental systems.
Data is needed to identify the choke points and prioritise additions and enhancements. A data-centric approach to transportation management based on sensor data is already a reality in many developed economies, with transportation systems being fed with a multitude of sensor data such as CCTV, integrated public transport card readers as well as GPS data, from phones as well as public and private transport.
Decisions based on all these sources of data are more robust and possibly more precise compared to traditional data gathered through questionnaires mostly in developing countries that discriminate against those who are outside of the statistical net.
New geological survey technologies reveal new underground resources. New geospatial data is being created through programmes called National Spatial Data Infrastructure (NSDI) that enable access and exploitation of data.
It is sad but true to say that most of our geo-data is in foreign hands and we seem not to be in a hurry to understand and acquire the technical capacity to use new technologies to exploit our mineral resources.
It is because of such ignorance that Kenyans woke up one day to the huge surprise that they also had fossil fuels within their boundaries.
We have underground rivers, aquifers and more resources that we do not know about. One thing that is common throughout, however, is the fact that there are many poor people sitting and dying on such resources.
Many may think we have no resources to build capacity and exploit such resources but that is only true if we ignore global collaborations.
In Kenya, for example, we signed a collaborative arrangement with IBM to exploit big data and use it to solve many problems, including transportation. We need to nurture such relationships and build a reputation to take advantage of others.
It is the availability of data that is making it possible to predict some disasters.
For example, Fiji has been hit by a number of cyclones but because of predictions on when they would land, coupled with good communication, only six deaths were recorded when one of the most powerful cyclones, Pam, blasted through Vanuatu. In the Philippines, the people failed to heed warnings and Typhoon Haiyan ended up killing more than 10,000 people.
The writer is an associate professor at the University of Nairobi’s Business School.