As technical infrastructure and telecommunication capabilities continue to improve, a wealth of data is being created in sub-Saharan Africa. The region is considered the world’s fastest-growing when it comes to mobile, thanks to mobile phones and internet becoming more affordable. It’s estimated that 87% of the region’s population will have access to mobile broadband by 2025 (currently that number is about 40%).
Data, data, data
The more connected we become, the more data we create. Traditionally, this data has been used to devise campaigns and target new customers, but more recently, it’s helping to create the actual products and services that will make it to market. Take Amazon, for example. It’s recently launched its own physical store, Amazon 4-Star, stocked with products scoring four stars or more in customer reviews online. In other words, a direct reflection of what the data tells them their consumers love most – and want more of.
Obviously, established brands with access to large amounts of their own data are most likely to be able to take advantage of the design from data concept. But how are brands faring in SSA? What factors do they need to consider in order to bring big data to life?
Diversity
While it’s certainly something to be celebrated, SSA’s ethnic diversity poses a challenge, too. Any given person in the region might hold several ethnic identities that are used interchangeably, and that may also shift over time. To better inform decisions and help shape products and services that can quickly adapt to changing needs and expectations, diversity needs to be a key consideration when it comes to gathering and better understanding data in SSA.
Context
The mechanics of how to get to the treasure troves of Big Data, rules-based automation, AI and machine learning are important, but true value will come from adaptability, sensitivity to the nuances of each market and a deeper understanding of their drivers. In other words, people-based marketing will be hugely beneficial to help leverage better value from contextualised data… data alone will have little to no value without context.
Introducing longitudinal measurement (panel- or cohort-based) to the mix of data will be particularly useful in helping to contextualise data and make communication and content more relevant and personal – the best approach to targeting real people. This also enhances our ability to discover “sleeper effects” or connections between different shifts over a period of time that might otherwise not be linked.
Data management
As the data lakes in SSA continue to grow, the strategic imperative is to operationalise data across a business as a whole and understand how data will be used to make better decisions and take action (at speed).
As we ingest massive amounts of data, the relationship with other data sets (e.g. in a data lake) can become fuzzy or dark. It is essential to reveal insights on the data sets themselves and to unravel the relationships between the data sets. This is where technology comes into play. Automation tools can help manage big data more effectively, and provide more opportunities for (real) people to get creative with solutions, ranging from deductive reasoning with the largest possible audience to more inductive reasoning with smaller, more focused segments and real-time streaming prescriptive analytics, by building and injecting models further up the data stream.
Sound complicated? It can be. But managing and integrating data, breaking down the silos of information and swiftly converting data into meaningful (and relevant) business analytics, is how we turn insights into action. What’s the point of it all, otherwise?
