On November 5, 2010, Ghanaians woke up to discover they were now the proud citizens of a middle-income country.

Overnight, they, and their country, had transformed from being poor to this new status. So, what changed to bring about this good news? The answer is that the government’s Statistical Service had revised the country’s GDP estimates. That revision, known as rebasing, showed that Ghana’s GDP had been underestimated by up to 60%, amounting to around US$13 billion in previously unreported economic activity. Plenty of Ghanaians surely found themselves wondering how that could even be possible?

Shortly afterwards, Nigeria’s Bureau of National Statistics updated its own base year figures, releasing new data which accurately allowed it to show that the country’s estimate was now 89% higher than previously known. This moved Nigeria’s economy from being Africa’s second largest after South Africa to number one at the time.

These two extraordinary examples highlight how important accurate data and statistics are to our understanding of key trends and challenges facing African economies. Prior to its rebasing, data on Nigeria’s GDP had understated the true value of the country’s ICT, telecommunications and services sectors as engines of national development. This seems to have led to decisionmakers to prioritise policies that disproportionately favoured more historical growth drivers such as the oil industry.

Africa’s national governments have long struggled with dealing with the challenge of collecting accurate data to use for informed policymaking. But that is far from the only challenge they face where numerical accounting of what is going on in their countries is needed. In most instances, data scarcity is the most common issue. While many governments manage to generate a certain amount of consistent data through household surveys, most cannot afford to conduct them regularly. Whatever data they collect is further complicated by its being quickly rendered obsolete or outdated by swiftly changing on-the-ground realities. Collecting population data at scale is a complicated, high cost, and time-consuming logistical feat that many countries across the continent simply cannot afford. As cities across the continent become the key sites in which most
of its pressing challenges will need to be dealt with, it’s increasingly clear that the data deficits it faces is existential. It is crucial that it be confronted head-on as the type of essential evidence-based policymaking the continent’s cities need is premised on having a full accounting of not just the scale and size of economic activity, but as granular as possible an understanding of how people in the continent’s cities live.

According to UN Habitat, just under two-thirds of Africa’s urban population lives in informal settlements or slums. Add onto that the fact that data on life in and on these ubiquitous informal settlements is limited, and you start getting the picture of the challenge facing policymakers and communities. How can you adequately design policies and long-term plans for what you do not truly understand?

To unlock Africa’s urban potential, cities must begin by making sufficient investment in one critical robust data system. Not just its collection, but also in the tools and expertise necessary for analysis, deployment into the areas where it’s needed and sharing. Additionally, the data needs to be accurate, inclusive, and actionable. Data for its own sake serves no purpose.

Specifically, the data systems need to enable policymakers and all the relevant actors across our urban systems to do four key things. They should:

1. Respond to the realities of low-income populations that are often overlooked in traditional data sets.
2. Break down data silos between sectors and departments.
3. Build partnerships with private sector data holders.
4. Anticipate future urban population, economic, housing and other trends.

Across Africa, lawmakers have a unique opportunity to establish policies, regulations, and interventions that anticipate rapid urban growth and avoid the high costs of retrofitting infrastructure in informal settlements—which can cost up to three times as much as new infrastructure development, and comes with significant financial, political, and social impacts. Geo-spatial data can be used to anticipate growth and pre-emptively respond. The Ethiopian Urban Expansion Initiative, a partnership between New York University and Ethiopia’s Ministry of Urban Development and Construction is a good example of this. Focussed on four cities—Adama, Bahir Dar, Hawassa, and Mekele—the project used geo-spatial and population data to forecast and plan for the growth of these cities up to 2040.

Beyond merely supporting more evidence-based policymaking, innovative uses of new technologies can also help cities use their data systems for improved resource mobilisation. This is essential.

African cities collect far less property taxes than their counterparts in other regions. Property tax revenue in sub-Saharan Africa averages less than 0.5% of GDP, compared to over 2% in high-income countries.

When integrated into a coherent policy and reform process with political backing, digital innovation could be a game-changer for African cities that want to increase revenue. For instance, the availability of satellite images and aerial orthophotographs taken by drones has transformed how land and property records are tracked and compiled.

Digital data management systems can link geospatial data such as GPS coordinates to ownership information, particularly useful in places without a functioning address system. These technological advances allowed the city of Freetown, Sierra Leone to double the number of registered properties in its jurisdiction in just one year. This has quintupled the city’s property tax revenue prospects, further bolstered by the introduction of a more progressive tax structure that has seen the top 20% of properties triple their contribution while the bottom 20% has more than halved. The city of Kampala, Uganda has had similar success, geotagging at least 300,000 properties and adding them to the tax register since 2014.

But, before getting seduced by the potential of effective data systems, let’s go back to basics. What are the key building blocks of an effective and inclusive citywide data ecosystem?

For a start, there must be clear data standards—common formats and definitions that enable sharing across departments, which can be easily integrated with other data sources. Data scientists are said to spend a quarter of their time “cleaning data” due to a lack of clear standards.

There must also be clear protocols that govern the types of data collected, how it is stored, and used. And where data is concerned, it goes without saying that trust is vital. This has to be earned, and cities aiming to win over a broad range of stakeholders and citizens need to actively invest in building this trust. Clearly articulated policies around the collection, use, sharing, retention, and disposal of any data collected are a good starting point towards building trust.

Lastly, cities need to put in place strategies to attract, train and retain cohorts of data science and systems engineering experts. With these in place, cities can develop products and services that lead to tangible impact and develop policy that is rooted in reliable evidence.

How do cities get there?

COVID-19 served as a catalyst for public-private data-sharing partnerships across Africa. To deal with the pandemic, many governments leveraged private sector data such as call detail records (CDR) held by mobile operators to monitor movement patterns and track the spread of the disease. This is an encouraging development.

It’s well known that most cities across the continent lack data on mobility and usage patterns of the informal transportation networks that are the bedrock of life. Call detail records are increasingly being used by urban transport authorities to understand these dynamic systems. In Senegal, Orange’s big data team Flux Vision partnered with the transport authority Conseil Exécutif des Transports Urbains de Dakar (CETUD) and other local businesses to use CDR data to understand movement patterns in peri-urban areas, track changes to mobility during sudden crisis events such as floods, or during holiday periods, in order to plan and implement new bus routes.

Cities across Asia have shown how data from ride-hailing companies can also be leveraged to support long-term planning. In the Philippines and Malaysia, the ride-hailing company Grab has partnered with municipal and national governments and the World Bank Open Transport Partnership to make use of OpenTraffic, the company’s open data visualisation tool, which uses anonymised driver GPS data, to understand traffic congestion and make data-driven decisions on transport planning and infrastructure investment.

However, the business and funding models to support such partnerships in a manner that ensures both financial sustainability and accountability aren’t as clear cut. Governments need to put in place the legal, data, and policy frameworks to enable these collaborations that also clearly address any concerns over privacy and potential misuse. Another pressing question is regarding the wisdom of handing over vast troves of personal data to corporations, who have proven to be unreliable custodians of sensitive information in the past.

Initiatives such as the Global Partnership for Sustainable Development Data, the Digital Impact Alliance, the International Growth Centre’s Cities that Work Initiative, the International Centre for Tax & Development, and the GSMA’s AI for Impact’s programme aim to support policymaking in overcoming these barriers and provide platforms to share learnings across contexts.

The stakes of Africa’s urban future are too high to plan in the dark. Cities are under pressure to become engines of
economic productivity and social mobility, deliver housing, jobs, and access to essential services, while also building climate resilience.

Planning with inadequate or outdated data means that decisions may not align with the current and future needs of cities and their residents. Investing in inclusive data systems is not a technocratic luxury—it is a moral and practical imperative. It determines who is seen, who is served, and whose future is planned for.

With AI emerging as the next great transformative technology, now is the time to set strong, contextual and
inclusive data foundations for Africa. These efforts need to be continent-wide.

Jumping onto the AI bandwagon without establishing first principles on data for the continent will only reinforce
existing blind spots and inequalities rather than adequately addressing them.