Building the right data talent starts when a person is still in school preparing to dive deeper into the data science profession. This is well grasped by Cenfri, which is implementing the Rwanda Economy Development Programme funded by the Mastercard Foundation, as well as the African Centre of Excellence in Data Science (ACEDS), which is located in the University of Rwanda-College of Business and Economics (UR-CBE). On January 25, they organised a public lecture on the topic Building the right data talent pipeline at UR-CBE. It was attended by different data science practitioners as well as data science students. Ekow Duker, a tech start-up co-founder highlighted that there is a high demand for data science professionals, suggesting demand increased threefold in 2021. Duker urged the students in the audience to develop a commercial mindset and learn the underlying business to better understand the issues for which a data solution is required. The next step is to partner with the business and not to be an 'order taker', he continued. Be technically proficient by knowing the underlying math, building a project repository, testing yourself in competitions, and by being part of the wider data community. Hanjo Odendaal warned, It is not all about big data, administrative social science data can also be particularly valuable. He encouraged the audience not to fixate on machine learning and advised people to learn SQL so they can work with large databases. Olivia Rutayisire, a data scientist for Cenfri and an alumna of ACEDS declared that she is using the knowledge she has acquired from school (including data mining, data manipulation, Python, as well as machine learning) in her work, although she is aware that the data science domain requires one to keep learning. She urged organisations to share data on trust, declaring that the number one goal of data scientists is to help them make data-driven decisions instead of basing decisions on gut feelings. Rutayisire called those who are passionate about data science to pursue it because it is a golden field considering today's world, adding that they must be willing to help institutions to get the right data and then analyze it by finding insights that are going to impact those institutions. Dr Ignace Kabano, Head of Training at ACEDS, found the session very useful and insightful, given that it bridges the need of the industry through the education and training that are offered at the university. He asserted that at the ACEDS, they train students to become data scientists who can not only develop models but also develop solutions to the challenges that industries are facing. Data scientists can't only be studying theories; we want them to come up with solutions, especially when you consider the data revolution and data engineering perspective. We want them to be working on a specific project in the industry and come up with a solution to the challenges observed, he said. “Through these talks”, he continued, “we will be connecting practitioners with data science students so that they can acquire additional coaching”. The ACEDS enrolls 40 data science students across Africa every year. Gaudence Uwimana is a PhD candidate at the center and the Head of Statistical Analysis Division at Rwanda Revenue Authority (RRA). She is aware that with the new era and digitisation, data scientists are dealing with big data and that a good data scientist has to also acquire coding skills through IT or data engineering. She said that when it comes to big data, some people don't have the right management tools, which is a challenge. As an example, she explained. I was a statistician dealing with MSs Excel, but the program could only handle one million rows. But when it came to the Electronic Billing Machine (EBM), we ended up having a record that could go beyond three million a month and, as someone in charge, I didn't have a tool. I felt like it was the time for me to get into the new era and get new technology to sustain my position at RRA. Uwimana noted that the challenge is still there for many other institutions including banks who deal with several transactions. If you are not into big data and also data science, you will have the data, but you will not be able to use it, she noted.