The African Data Story
Those who need data don’t have it. Those who have it don’t use it. How can better quality information help to catalyze evidence-based decisions in Tanzania?
Four years ago, a friend of mine introduced me to GitHub, a website for developers to collaborate on software projects. He also helped me to learn GeoJson, a computer file format that makes it easy to create and develop visual maps. Armed with these skills, I created my first data-driven mapping project, Afya Map, which maps health facilities across Tanzania.

The tool works well, but unfortunately it lacked enough accurate geo-located data (that is, information that has coordinates so it can be placed on a map) to help finalize the project. Undeterred, my colleagues and I at the Buni Innovation Hub continued to explore other interactive data visualizations using open-source software, which we used to develop a few other platforms including Tanzania Census Map and Kilimo Info.
For all of these data-driven tools, we used publicly available data, but that data simply wasn’t enough. The data had gaps, inconsistencies, and errors. It wasn’t long before we realized that the technology solution was rarely the problem; tools will continue to be released, mapping languages developed, and technologies improved. The question is: Where is the accurate and usable data for those who need it?
This, I’ve since learned, is a common challenge, not only in Tanzania but across the continent (and elsewhere as well). In many cases (but certainly not all!), the African data story is one where those who need information often don’t have it, and those who have it often don’t use it.
The African data story is one where those who need information often don’t have it, and those who have it often don’t use it.
Those who need don’t have it.
Even if techies, innovators, entrepreneurs, specialists, journalists, and data activists have the will and the skills to use data, they might not have the data they need to produce meaningful decision-making tools. Products such as Shule Wiki (a schools performance analysis portal) and Habari Mazao (markets prices portal for agro products) are powered by brilliant teams and technologies, but issues with the data are what prevented them from scaling and creating their intended impact.

To be clear, these challenges are not about data availability. The raw information about market prices, data about health facilities and even school performance are all technically available online. Rather, the issues were either that the data accuracy or data accessibility. Why is that the case?
Those who have it don’t use it
One of the reasons for this imbalance between supply and use of data is that those who own the data are good at investing resources to collect data, but not as good at fully exploiting it. Sometimes, they don’t see the opportunities to use the data to genuinely inform decisions, so much as a tool to monitor, track, or report progress.
This leads to a culture where data is viewed within an organization as something to be collected and reported, rather than a tool to drive critical reflection, innovation, and community problem-solving. Without this level of interrogation, the data is too often filled with gaps, errors, and inconsistencies that render it difficult to use in tools — like Afya Map and others I mentioned above — to inform meaningful decisions.
Data is viewed within an organization as something to be collected and reported, rather than a tool to drive critical reflection, innovation, community problem-solving
Providing resources for those who need data, and for those who have it.
Fast forwarding four years later, I’m now involved in Data Zetu, which aims to empower communities to make better, more evidence-based decisions that improve their lives. After visiting communities in our Listening Campaigns in Temeke (see earlier post), we’ve collected loads of information from the community, including a deep-dive into challenges and problems facing everyday Tanzanians and their local leaders. As we reflect on this vast dataset, and as we get ready to develop tools to address some of these challenges using data, I find myself wondering: Are we, too, going to fall victim to the African (Tanzanian) data story?

This time, it’s going to be different. For example, we’ll work with local leaders to reflect together on the data surfaced from the Listening Campaigns, building skills on data interpretation to promote meaningful use. This will help to ensure that “those who need data” — decision-makers in local communities and organizations — can use the information to inform their priorities about investments, advocacy, and more.
And for our own part, as part of the camp of “those who have data”, we at Data Zetu are charged with using these deep insights to inform our own activities moving forward, such as with which community-based organizations we’ll work that are themselves tackling some of the challenges raised during the Listening Campaign.
As a result, our contribution will be to invert the African data story. We aspire for a data ecosystem where “those who need it, have it,” and where “those who have it, use it.” This way, people, organizations, and leaders will be better equipped to address community challenges, and tools like Afya Map, Shule Wiki, and Habari Mazao will provide more meaningful information to drive evidence-based decisions in Tanzania.
We hope you’ll join us as we rewrite the African data story.
