Zindi, a Cape Town-based crowdfunding start-up has been building a database of data scientists across Africa. The start-up now has 12,000 registered users on its platform that uses Artificial intelligence (AI) and machine learning to solve real-world problems for companies and individuals.
In 2021, a group of data scientists led by Zindi used machine learning to improve air quality monitoring in Kampala. While another group assisted Zimnat, a Zimbabwean insurance company, in predicting customer behaviour, particularly in terms of who was likely to leave and what interventions could persuade them to stay. Zimnat was able to keep its consumers by providing customized services to people who would have left otherwise.
These are some of the data-driven solutions that Zindi offers to businesses, NGOs, and government agencies. Zindi announces these challenges and invites its data scientist community to compete in problem-solving competitions. Data scientists who participate, submit their solutions, and the winner receives a monetary award.
The competition hosts get to use the best solution to meet the issue they were given–like in AirQo’s air quality monitoring project, which sought solutions for forecasting air pollution across Uganda and helping Zimnat in reducing its losses.
Celina Lee, co-founder, and CEO of Zindi stated that the AirQo now has a dashboard where the public can examine air quality and forecasts. One of the interesting aspects of this project was that AirQo hired two of the winners from the challenge to help with the implementation of the project. The other co-founder of Zindi is Megan Yates from South Africa and Ekow Duker from Ghana.
Lee also said that AirQo raised funding from Google, based on the solution that they built. They now have replicated this solution in the other parts of African countries.
The competition was organized in partnership with the Digital Air Quality East Africa (DAQ EA) project of the University of Birmingham and the AirQo project from Makerere University, Kampala. Among other notable private and public organizations that have tapped Zindi include Microsoft, IBM, Liquid Telecom and UNICEF, and the government of South Africa.
Given how the crowd-funded start-up has evolved since its introduction, Lee is pleased with what Zindi has accomplished so far and optimistic about the community’s future. The platform is now offering options and increasing competition against traditionally pricey consulting firms operating across Africa.
From the beginning of 2020, Zindi’s user base has tripled, reaching 33,000 data scientists from 45 countries across Africa. It also gave $300,000 in prize money to data scientists.
This number is expected to rise as it prepares to hold the fourth inter-university UmojaHack Africa challenge in March 2023, in which college students will compete for various solutions.
Zindi is using the inter-university competition to expose students to realistic data science experiences and to tackle real-world problems with AI. The platform attracted roughly 2,000 kids during last year’s event, which was held virtually due to the COVID pandemic.
Lee said students get to develop their first machine learning models, and it opens up all kinds of avenues for their careers and education. She also said that Zindi has created a career platform to shorten the road from learning to earning. By posting job openings on the talent placement platform, businesses can access their talent pool.
After recognizing a knowledge gap and the need for training, the crowd-solving platform plans to incorporate a learning component that would provide training material to aspiring data scientists. Furthermore, according to Lee, the majority of Zindi’s users are university students in need of learning experience and better skills to tackle real-world challenges.
The crowd-solving platform is also planning to introduce a learning component that will provide training material to budding data scientists. This will come after recognizing a knowledge gap and the need for training. In order to implement this plan, the platform would need a $1 million seed funding.
Furthermore, Lee said that the majority of Zindi’s users are university students in need of learning experience and better skills to tackle real-world challenges.
Lee said that for them it is all about the community and creating more value for all of their data scientists. She also said that they will introduce more learning content as a lot of their data scientists are university students or very early in their careers. And they are just looking for a chance to learn and build their skills in this field.
Shakti, a San Francisco-based venture capital firm, led the seed round, which also included Launch Africa, Founders Factory Africa, and FIVE35.
According to Lee, all of these activities are aimed at developing a strong data science community in Africa and for the continent, with the goal of reaching one million users in the near future. This, she said, will be accomplished through providing early career data scientists with training opportunities and building a robust community that supports collaboration and mentorship. Lee said that they want to make data science a career that every young person should get interested in pursuing.
In 2012, Zindi was created as a subsidiary of Ixio Analytics, which is a data science consulting company also based in Cape Town. Ixio was founded by Megan Yates, a South African who has been working in data science.
The Ixio saw there was a growing pool of data scientists in Cape Town and across Africa looking for opportunities to grow and apply their abilities at the same time, which led to the creation of Zindi.
Companies and other organizations in Africa were in a position where they were generating large volumes of data but were unsure how to proceed and capitalize on the true value of the asset they were creating.
How does Zindi work?
They first identify and characterize a problem, after which they assist in the creation of a data collection that will be used to construct an AI or machine learning solution. Then they publish it on their platform and make it available to the public. Zindi currently has over 8 000 data scientists enrolled on the platform, with over 100 new registrations added each week.
Data scientists would create an account on Zindi and then register for the competition, agreeing to the terms and conditions and gaining access to the data, which they could then use to create machine learning models.
Why is Cape Town the best base for Zindi?
Cape Town is an excellent location for a tech or AI firm, the city has a thriving start-up ecosystem, and the skill pool in Cape Town has been invaluable to Zindi. Because of its proximity to the University of Cape Town (UCT) and Stellenbosch University, the city has a strong talent pipeline.
Is using AI and machine learning for the benefit of society important to Zindi?
The applications of AI have been driven by corporations that are looking to use AI to make profits but what Zindi is excited to figure out is how to translate those same technologies, those same approaches, for social impact.
While Zindi is a for-profit company, the company does have a social mission and is passionate about solving problems for companies and other organizations which will also have a positive impact on society.
What about Zindi’s presence in the rest of Africa?
While Zindi does not have offices in other countries, they do have a presence across the continent through its network of ambassadors. Users are now concentrated in South Africa, Nigeria, and Kenya, but Tanzania, Uganda, Tunisia, and Senegal are rapidly gaining ground. However, they have data scientists in their network from almost every African country.
AI become more relevant after COVID pandemic
The pandemic has reshaped the way people work, play, and live. It has deepened their relationship with technology as they depend on digital mediums for almost everything, including work, education, leisure, social interactions, everyday transactions, and more. A key takeaway from this accelerated pace of digital adoption is that there has been a fundamental shift in consumer behaviour, leading to the instant gratification mindset.
A study by PricewaterhouseCoopers (PwC) indicated that 52% of organizations accelerated their AI adoption initiatives due to the pandemic. Business leaders are beginning to understand the power of artificial intelligence and machine learning technologies that can help optimize the business, improve decision-making, and better understand their customers. Here’s how AI is making an impact across industries-
1. Prevents fraudulent transactions
Fraud management is complex and can vary across industries. A PwC survey showed that several business leaders globally found an increasing threat from external perpetrators in 2022. Industries like banking, insurance, and healthcare face the most risk from fraud. Artificial intelligence models prove to be very effective for fraud detection. AI/ML models can not only detect fraud but can predict it by analyzing fraud trends and preventing it immediately. For instance, financial services that are always at risk of fraud can apply advanced artificial intelligence algorithms to detect good and bad loan applications.
2. Improves customer journey
With the help of AI/ML tools, business leaders can create a seamless end-to-end customer experience. Machine learning and predictive analytics can provide detailed insights into customers’ behaviors, trends, and issues and enable businesses to personalize the customer experience. The ability to easily extract and analyze customer data can help companies create the right personas, match offerings to customers’ needs, and share customized communication.
3. Enables intelligent decision-making across processes
With the complexity and dynamics of modern-day businesses, organizations require the capability to make the best decisions in the shortest time in a risk-conscious, adaptable, and personalized manner. Artificial intelligence can analyze massive amounts of data in minutes and extract rich and meaningful information. As a result, an increasing number of organizations are introducing AI and data analytics into their workflows to help them make better data-driven decisions.
4. Automates content-heavy processes and harness content intelligence
Apart from enabling intelligent decision-making, AI/ML also brings content automation and content intelligence to the table. AI technologies like natural language processing (NLP) and machine learning can be used for document classification and text classification to make it easier to search, manage, filter content, and semantically understand text. Image, audio, or video processing tasks with artificial intelligence capabilities can also help in detection and classification. For instance, businesses can use AI-enabled image processing for face recognition in public places or even recognize patterns and objects in images and videos.
The way forward
Low code-based AI/ML platforms make AI/ML accessible to every enterprise and can process raw data into real-time insights in no time to solve various business use cases. Scalable artificial intelligence can enable enterprises to stay prepared for sudden shifts. This requires collaboration across different business users such as domain experts, business analysts, data engineers, and IT teams. While one set of users would be responsible for prepared data to create AI/ML algorithms, other users can experiment with the models and deploy them in production to ensure that organizations have the latest insights for smart decision making.