The existence of tremendous amounts of data has led to an increased interest in analyzing it to extract insightful knowledge and information for decision making. This is why data is currently regarded as the gold of the 21st Century. When obtaining more value from gold, it is minted into coins or fashioned into jewelry, when obtaining more value from data, it is collected, manipulated, and analyzed. This whole process of collection, manipulation, and analysis of data is the goal of data science.

Data science in simple terms is an interdisciplinary field that deals with processes and systems extracting insights and knowledge from large datasets. This means that as data volumes grow, data science continues to become relevant. The field is an integral part of the culture and economy of the global world because its findings and results can be applied to almost any sector (such as travel, insurance, healthcare, and education), and in any company in any industry. In business, data science has already helped drive good decision making resulting in revenue opportunities, cost savings, and more efficient operations for companies. In agriculture, data science is the central point of precision agriculture and has resulted in increased efficiency and greater productivity with the adoption of scientific approaches to planting, fertilizing, and harvesting.

In recent years, the East African region has experienced massive transformations evidenced by new FinTech platforms, digitization, and other forms of digital financial services like mobile money. The key driver behind this shift has been the use of data and analytics for innovation and decision-making. If data-based innovation is adopted in government, it can be beneficial in improving the way basic services are delivered to the masses in the following areas;

  1. Fighting crime
  2. Improving Education
  3. Fighting Poverty
  4. Preventing diseases/health-related research
  5. Improving Public transport

Fighting Crime

Data science can be used to compare and integrate data from various police or security agencies for predicting and identifying threats to a country. It can also be used in detecting fraud in financial transactions and in linking repeat offenders to particular crimes. US-based firm PredPol uses historical crime data on things like the type of crime, location, and time of crime which they combine with other social-economic data to predict where and when specific crimes are likely to occur in the next 12 hours. In addition to this, law enforcement agencies get access to the Automated License Plate Recognition that helps them identify cars owned by people with outstanding warranties.

In 2013 during the famous Boston Marathon bombing, data science was used to analyze over 480,000 images which were reduced down to a few based on available descriptions of the suspect.

Improving Education

Data Science can help governments understand more about educational needs on a local level in order to ensure that the youth of the nation are getting the best possible education in order to serve the country in the future. An example of this is the US Branch of Education that is utilizing learning analytics and data mining frameworks to understand how online students study to improve their overall learning experience by eliminating fatigue.

In addition to this, a data science research methodology is becoming even more important in an educational context. More specifically, this field urgently requires more studies, especially related to outcome measurement and prediction and linking these to specific interventions.

Fighting Poverty

With data science, governments can develop tools that discover more effective and innovative ideas on how poverty can be reduced. Data science makes it easy to pinpoint areas with the greatest need and how these needs can be met. In Kenya, researchers are using meteorological data to build prediction models that forecast weather patterns. This is helping farmers plan agricultural activities accordingly and improve their crop yields which reduces hunger.

Researchers at the University of Washington used cell phone metadata generated by texts and calls to infer patterns about people’s living styles and economic activities which they used to construct an estimate of wealth distribution in the region. With this information, they can be able to make decisions about social welfare allocation and pinpoint key priority areas when distributing humanitarian aid.

Preventing diseases

With data science, governments can use data from previous epidemics, weather patterns, and human travel patterns to predict likely origins of future outbreaks, patterns of spread for creation of containment plans, or even establish vulnerable regions that can be targeted for prevention programs.

Scientists from Johns Hopkins University, the University of California San Francisco, and IBM created prediction systems for dengue fever and malaria. In the world of cancer treatment, data science is being used in predicting synergism of cancer drug combinations which provides many benefits such as higher efficacy and lower toxicity.

The data science solutions can also reshape the medicine industry, uncover new insights, and turn brave ideas into reality. The possibilities for integrating data science and healthcare are expanding as the amount of data is growing faster each day, and the technologies are constantly improving.

Improving public transport, reducing congestion and air pollution

With data science, governments can improve transportation, reduce congestion and eliminate traffic during peak hours by using predictive analytics to determine which areas and routes are most likely to be active.

After deploying sensors within cities in areas with oil refineries, chemical plants, natural gas wells, and other industrial sites, the government can use data science to analyze and detect changes in air quality such as detecting harmful pollutants the moment leaks occur and deploying maintenance crews to handle repairs.

Although we are greatly enthusiastic about data science and its growing capabilities, the field still faces concerns such as data privacy, information misuse, and its reliance on complex tools that are difficult to understand. Aside from this, the field also faces two key barriers to its adoption which are; the lack of trust users have for data science applications which are caused by a lack of understanding of the inner workings of data science algorithms; and the challenge of operationalization where a model performing very well in the lab fails to meet its expectations when deployed in larger environments.

However, data science adoption can do more good than harm if used in ways that do not cause harm or undermine public trust. In Kenya, a device that gathers and transmits data on a vehicle’s location, speed, the tonnage of garbage loaded, and the drivers’ behavior on the road in real-time is being used to monitor garbage trucks to increase collection. In Uganda, Pulse Lab Kampala is using machine learning techniques to analyze content obtained from public radio discussions which they use to inform programs to achieve the SDGs. Ultimately, all these new techniques are here to stay and present numerous opportunities for us to utilize data to make more accurate, objective, and efficient decisions.

By Arthur Kakande, Communications Lead at Pollicy