Why Digital Data Should Matter In Our Emerging Economies
You will hear and read it these days with all sorts of qualificatives: Smart, Raw, Big.
Data, that is what I am talking about: what does data mean?
Behind “data”, hear "every action we perform online". Everything we do leaves a mark and forms for each of us a digital footprint. Well assembled, data provides insights about who we are, what we do, how we act and why. Well analysed, data provides precious insights. Well leveraged, data can lead to the (almost) perfect design.
With Data, we can understand people and develop a human-focused approach to development. In the digital world, we call it Human-Centered Design. A design that brings real solutions to actual problems. When applied to the offline world, it requires even more solid and reliable data. For this reason, data matters even more to our emerging economies, where we strive to do the best for all.
2 questions from there:
- What kind of data do we make available?
- How can we best leverage for the wellbeing of the people?
Let’s use an example for clarity purpose: Nancy, living in Kumba, South West Cameroon.
Nancy, 35, lives in a village 30-minute away from Kumba. She is a widow, and has 2 children: an 8-year-old and a 14-year-old. She sells produce everyday in the main market in town, earning 50,000 a month.
For the sake of simplicity, we’ll gross all her other monthly expenses to XAF 35,000. She then has a saving capacity of XAF 15,000.
The kind of data we need: the case of Nancy
Nancy owns a feature phone. She sometimes uses apps such as WhatsApp or Facebook. She relies on USSD codes for her mobile services:
- Topping up her airtime account
- Subscribing to phone and data plans
- Sending airtime to her children.
Below are some data we can collect from Nancy by “watching” her phone:
- When she tops up her account, how much and how often
- How long her calls last on average, and how often she calls
- How many test messages she sends on average a day, a week, a month, etc.
- How long she commutes everyday, the departure and arrival points
- The speed she moves at during the commute (indicating whether she’s waking or riding a bike)
- Any movement she does during the day, when and how often.
After only 3 months of data points, we get an empirical picture Nancy’s habits. Thus, we can leverage this data to design the best solution to Nancy's problem.
Nancy needs funding to grow her produce business. Some MFI will consider an application from her, but how will they assess it in a strongly factual way?
This is where data comes in. A data-driven MFI would use it to bring economic intelligence to Nancy's application. It will have relevant data about her lifestyle and her economic situation. From there, it could design a tailored loan programmes for Nancy-like economic agents.
Applying this method would result in a data-driven policy, designed from and for humans. Now, extend this approach to all sectors of the economy... What would be the result, in your opinion?
But there are some drawbacks
Indeed, with the need and use of data comes a tension: security vs privacy. Nobody can have 100% of both. To grow, we need to make some trade-offs. But let’s save that point for another day... Shall we?
This article was originally written for my Alma Mater alumni association's newsletter.