Building a data skills foundation is necessary to achieve meaningful digitalisation
Building a data skills foundation is necessary to achieve meaningful digitalisation15 July, 2022 •
One of the reasons so much emphasis is placed on digitalisation is that people hope digital technology will solve common, but hereto unresolved, challenges. While these digital solutions can be exciting, unless executed properly, there is always the risk of widening the digital divide. To achieve meaningful and sustainable digitalisation that tangibly improves livelihoods we need to be rigorous about examining the evidence (collecting and analysing data) to determine the outcomes of digitalisation initiatives and then be courageous and creative in adjusting strategy and policy. For this to be effective, you need both a commitment to data-informed policymaking at the top levels of government and the private sector and a trusted data foundation. But before we can start thinking about data rigour and governance frameworks, we must ensure that the necessary practical data skills are identified and fostered.
Bolstering digitalisation in Rwanda
Cenfri is working with the Mastercard Foundation and the Government of Rwanda on the Rwanda Economy Digitalisation Programme. Rwanda has an ambitious digitalisation strategy and has invested considerably in the success of its implementation. This is evidenced by a number of activities such as the distribution of smartphones to connect Rwandans, declarations on meaningful connectivity by a host of institutions, the cashless acceleration campaign and investment in digital skills.
One of the programmes is commitments to data-driven policymaking and the Mastercard Foundation’s youth employment objectives has been placing eight data science interns in key government institutions. These interns work closely with data scientists from government institutions on the foundations of data-driven policymaking and will participate in a Cenfri data science community of practice that will enable peer-to-peer learning for data scientists and those in digitalisation programmes across sectors.
We interviewed two of these data science interns, Patrick Manzi and Mireille Kirezi, to understand what they are learning as they start applying their skills. Mireille holds a Masters’ degree in Electrical and Computer Engineering with a major in Data Science. They were both born and raised in Kicukiro, Kigali.
When discussing the shift from using their data skills in an academic environment to a public sector one, the interns told us that they were surprised by how data programmes are set up independently by different institutions. “I was surprised at the dissimilarity of the data stored by different government entities. Some datasets have different numbers for the same thing,” said Patrick. These differences could be because different entities do not share their most up-to-date data, meaning that there is misalignment or duplicated effort in collecting the data. Mireille noted another inconsistency, “We receive data from different institutions and sometimes those datasets are all in different formats. We then need to process them to a standard format to create merged data sets for a more powerful analysis to be done.” The document formatting is a common barrier to data analysis since there is no standard format for recording either dates or names in Rwanda. This creates difficulties for data analysis across different datasets that record dates and names differently. However, the interns have been able to share the different processes they use to clean and visualise data.
The potential of data to solve common challenges
A trusted data foundation and the application of data and other multidisciplinary skills that enable the interpretation of data insights for the local context, can have far reaching positive effects on a community. The data science interns are already excited about how they will be able to use their skills in the future to solve problems that are close to home and make an impact in their respective fields. Mireille sees how government administration services could use data to reduce wait times and improve service delivery, “In my community I have noticed a huge number of people go to different administrative offices to ask questions about services. They spend so much of their time waiting. If there were chatbots to support them instead, these services would move much more quickly.” Patrick thinks data can be used to tackle a problem we are all familiar with, “Using machine learning techniques, we could use data analysis to better assign traffic officers to deal with traffic jams and traffic-light outages.”
Shared opportunities for problem-solving
Data science is fairly new in Rwanda and the need to upskill is growing as the push for digital transformation continues. Most people currently working in the data science field have backgrounds in fields such as software engineering, electrical engineering and computer science and had to pursue their data science at a post-graduate (master’s degree) level. Because of the difficulty in accessing data education, we are undertaking data capacity building in Rwanda. The Rwanda Economy Digitalisation Programme, Cenfri and its partners are offering some short courses with the Rwanda Management Institute in addition to the community of practice, which is designed to give data scientists an opportunity to share insights and techniques they can apply in their line of work.
For Rwanda to achieve its digitalisation agenda, it will need to build a trusted data foundation and invest in digital and data skills. To fully unlock the potential of digital technology to resolve persistent challenges, these data science skills would need to be complemented by creative input from a range of specialists across different sectors of the economy. This creates opportunities for young people in other fields to partner with data specialists in developing solutions to everyday issues they experience in their communities.
If you would like to be learn more about our capacity development initiatives, please contact Edwin Byusa.