Nothing illustrates the incredible power of data-driven decision-making more than the recent COVID-19 pandemic. Lamenting the disparities in genomic sequencing data from high versus lower-middle income countries, Professor Happi, from the African Centre of Excellence for Genomics of Infectious Diseases in Nigeria, has talked about the pressure they experienced early in the pandemic to speed up delivering results from sequencing undertaken at the Centre.

He tells the story of delivering results to the head of the Rwanda Biomedical Centre in the middle of the night and how, upon receiving data revealing that 93% of the genomes sequenced showed the presence of the Delta variant, lockdown was declared in Rwanda that very night. A story that demonstrates not only the value of being able to make decisions based on data analysis, but also the power of collaborating around data.

But not only governments rely on data to make decisions. Some of the biggest companies in the world today have built their business models entirely on that. Think no further than Google and Facebook.

Data-driven-decision-making (DDDM) is the practice of making better decisions based on insights, facts and metrics derived from data analysis. This practice has recently been made possible by the availability of massive amounts of data. Most of this data is directly received from consumers and often in almost real-time. All thanks to the use of apps and smartphones.

Now more than any other time in history, businesses and governments have access to troves of data, helping them understand consumer and citizen behaviour. This means that feedback on specific interventions is directly accessible and measurable.

For example, a business changes the price of a product. In almost real-time, the impact can be measured – Who is most affected? Where are they located? What age they are? How often do they generally buy from us? What impact is this going to have on our sales?

The information that previously required multiple market studies, focus groups, and a certain level of risk is not only more immediately available, but now businesses have the option of simulating market responses and consumer behaviour before the price change is even put into effect.

For governments, data analysis can enhance internal efficiency and operations for nearly any type of service; especially with multiple priorities competing for limited resources. Data analysis has the potential to inform many policy and government spending decisions. Examples include:

• Mobility data could inform infrastructure roll-out and which roads need the most urgent repairs.
• Prescription and medical test information could help identify the onsets of epidemics before they spread.
• Education data can be used to improve students’ examination and help track their individual progress
• Data can be used in smart cities to increase public participation, promote economic growth, improve energy efficiency, reduce greenhouse gas emissions, improve security and emergency services, and help to manage waste and pollution. (“Big Data in the Public Sector : Applications and Benefits”)

One of the great benefits of data-driven decision-making is its power to save money. By using data, the government can pinpoint the problem and spend resources only where it is needed or can have an impact.

Let us take a simple example: Assume the government is concerned about malnutrition amongst children younger than 12, but it only has a limited amount of funding to spend on a feeding project targeting such children. Where to spend the money? By looking at symptoms of malnutrition, for example absenteeism from schools, it can start to build a picture of the districts and sectors most in need. However, to do so, it must have accurate data on daily school attendance from schools across the country. Building such a database can therefore save the country billions of francs because it will assist not only the Ministry of Education, but many other ministries besides.

What this example illustrates, is that data gathered for one purpose, for example sound education management, can also serve to improve public health policy. The only requirement is that the data is of excellent quality and accessible to those who need it. That requires a sound data governance framework.

Data analytics and its derivative technologies such as Machine Learning and Artificial Intelligence have real potential to help address some of the challenges that our society faces. But we need to recognise and start communicating the value of leveraging the data being created, collected, and analysed. This will lead to a process of self-evaluation for agencies and businesses – which in turn (and we hope) will lead to skills development, strategic planning that take data into account, infrastructure planning and putting in place the necessary rules and procedures on data creation, access, confidentiality, security, and commercialisation.

About the authors

Hennie Bester is the programme director at Cenfri and an experienced public policy strategist working towards an inclusive digital Africa. Fiacre N. Mushimire is the Digital Transformation Policy Lead at Cenfri with over 15 years’ experience in emerging technologies policy and their regulatory environment. For more information on the programme, you can contact comms@cenfri.org 

This article was originally published by New Times Rwanda.