Training improves ability to unlock value from data

Training improves ability to unlock value from data

13 October, 2022    

Capacity-development is a vital part of the Rwanda Economy Digitalisation programme and beyond the hosting of training courses, the aim is to cultivate a hub of data professionals in  the Rwandan private and public sectors.

These data professionals who interact with, and analyse, data can champion data-driven decision-making and evidence-informed policymaking in Rwanda. Two such champions are Violette Mahoro and Juste Nyirimana; both past participants in The Foundation of Data-Driven Analysis for Policy Decisions short course, which Cenfri offers in conjunction with 71point4 and the Rwanda Management Institute.

The participants come to the training with varying levels of knowledge and different expertise (such as SQL and R) and the work is designed to take advantage of this. The training is practical and interactive and provides an environment where participants can learn from the instructor and each other.

Violette and Juste shared some highlights of their participation in the training course. These included:

  • An improved understanding of the value of project documentation. Violette and Juste were already working as data scientists when they joined the training course and one of their biggest takeaways from the training was the value and importance of documenting the code and processes, they use in projects. Not documenting code, processes and learnings meant that each time they started a new project they would have to start from scratch; now they can build on their previous work. Documentation also helps future collaborators understand how the project was built and makes it easier for them to maintain and update.
  • The benefits of a holistic project view. Another critical lesson they learned from the training is the need to understand the underlying logic or business aspect and not just the technical component of projects. Taking a holistic view allows them to make better decisions around what kind of data is needed for the project. Understanding how data is captured and cleaned is important to every data scientist but in most educational institutions the focus is on using data to make predictions. This focus makes it hard to ensure you are collecting the right data and to help the institution make good policy decisions. Since the training, Juste has switched his focus from building models to securing the right data for the questions they’re asking. For example, when it comes to understanding how financial services providers make decisions, he realised that sometimes the data is not cleaned properly, or the data used is missing key information and consequently the models that are built are unreliable or biased.
  • The potential of analysing data to understand impact. Violette used to think data science was mostly about descriptive statistics, but the training has made her realise that it is more about analysing and understanding data. Now she ensures that the data used is clean and complements the projects they are running. For example, in her work with the Economic Recovery Fund, which was established by the Government of Rwanda to support businesses that were hit by COVID-19, she now uses the data to understand the impact the fund had on those businesses rather than just the monetary value of the support. Having previously focused on system administration, using data to understand how decisions are impacting the lives of people has added a new dimension to her career.

The two data scientists have gone on to act as peer facilitators in subsequent courses and we spoke to them about their experiences and reflections on training facilitation. These include:

  • The incentive to prepare ahead of the training sessions and master new content. It is one thing to understand a concept and it is another to be able to teach and train others. Training requires patience, versatility in your methods, and a lot of preparation on content as the two are reluctant to suggest they don’t know the answers to questions. Despite this, due to different backgrounds and levels of understanding among the training cohort, they have had to rely on each other and other facilitators to satisfy all participants.

A sense of personal satisfaction. One of Violette’s highlights was introducing other training participants to functions in programming languages that are built into the software they were already using. Juste is now committed to being more “T-Shaped” in terms of his skillset. T-shaped skills is a reference to people who have deep, specialist expertise in a particular area coupled with broader knowledge that enables them to collaborate with others in adjacent areas. He is now focused on expanding his skills and is adding more system administration work and data engineering.

An ability to spot gaps in data practices in the work environment. Violette now has more insight into the challenges financial institutions experience in accurately capturing and cleaning data (and the implications if this is not done properly). She recognises the need to work with technical teams to ensure their data capturing processes are optimised. Juste’s experience as a trainer made him realise the importance of training not just the data scientists, but executive teams so that they can understand the importance of data and how it can be used to make decisions.


Read more about our capacity-building for data-driven decision-making here.

The Rwanda Economy Digitalisation Programme is implemented by Cenfri in partnership with the Rwandan Ministry of ICT & Innovation and financial support from the Mastercard Foundation.

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