Due to the hype surrounding Big Data, there is a temptation for economic planners in developing countries to over-depend on quantitative data (numbers) at the expense of qualitative data (stories). There are many valid reasons why economic planners should not ignore local intuitions that offer a better interpretation of what is beyond the data (numbers). Working on the forefront of informal agricultural markets has made it clear to eMKambo that what is measurable is sometimes not the most valuable. Local intuitions and emotions can be more valuable than numbers. There is a hidden story behind each commodity price.
Unfortunately, in a world that is becoming seriously data-driven, qualitative data is being seen as anecdotal, unreliable, small and insignificant. As if that is not enough, researchers who specialize in qualitative data are being swept aside by the big data revolution which is more interested in quantitative evidence that can be manipulated by machines. Yet there is no doubt that qualitative data is better at revealing people’s emotions, intuitions, experiences and worldviews. Although qualitative evidence is the sticky stuff that is difficult to quantify, it is good at generating incredible depth of meanings and values.
With the rise of Big Data, it seems development organizations and private companies are also now valuing quantitative more than qualitative data. For instance, the introduction of drones in agriculture is expected to generate a lot of real time data on what is actually happening in farming areas. Investments in technologies such as drones are threatening the future of qualitative data which cannot be captured by drones. One of the reasons why organizations over-rely on numbers (quantitative data) is that qualitative data is not easy to measure. Yet, what is not easy to measure can represent a community’s competitive intelligence and advantage. Most quantitative data gathering and processing methods strip information of its context, meaning and stories so that it can be standardized and clustered.
Integrating numbers and stories
A complete picture of the reality on the ground in most African communities can only emerge from integrating quantitative and qualitative evidence. For agricultural value chain actors to form a complete picture of the agriculture market, they need both numbers and stories because each of them produce different types of insights at varying scales and depths. While numbers can reveal quantified data points, stories can reveal the social context accounting for those data points.
On the other hand, more numbers do not necessarily translate to more insights. Several organizations have huge data bases which are difficult to convert into meaningful insights. When organizations value quantitative results more than qualitative results, they reinforce an assumption that statistically normalized and standardized data is more useful and objective than qualitative data. Yet it is more meaningful to balance the quantity (number of farmers or volumes of commodities) with the quality of agricultural insights and commodities.
The importance of not losing people’s experiences
When organizations start making decisions based on numbers only, they end up losing people’s stories and actual experiences. Without human decision-making, farmers and other value chain actors lose their capacity to reflect on the morality of their actions and that is not ideal in a changing climate. Qualitative data generates emotions and inspiration that enable communities to know what they need to know. That is why collecting and analyzing stories produces rich insights more than can be generated through quantitative surveys. Through stories, communities stumble on surprises that can inspire innovation and imagination. You cannot say the same about a string of numbers.
Numbers alone do not respond to the emotions of everyday life such as trust, vulnerability, fear, lust, security, love and intimacy. It is hard to use numbers to represent the strength of an individual farmer’s affiliation to his crops or animals and how such affiliation changes over time. On the other hand, qualitative data approaches reach deep into people’s hearts. That is why a relationship between a farming community and a contract company’s brand becomes more emotional than rational. When policy makers want to build stronger ties with agricultural value chain actors, they should embrace both numbers and authentic stories. That is because stories contain emotions which cannot be found in a normalized data set.
Why we need to balance a data-centric approach with a human-centred approach
Solving global challenges such as climate change, malnutrition and poverty certainly requires a health combination of numbers and stories. Data-driven approaches are already asking many questions that can only be answered through stories (qualitative data). One such intriguing question is: What does the behavior of farmers and traders tell us about the role of big data (numbers) and qualitative data (stories) in agricultural markets and farming communities? The process of answering this question can show why quantitative data alone is not able to reveal all the influences of socio-economic development. Quantitative data alone will not tell you what consumers really want but stories that resonate with consumers can shed more light.
Many communities in developing countries are already experiencing uncertainty, unpredictability and information load as a result of digital technology and related influences. In order to keep up with uncertainties such as climate change and poverty, they need both numbers and stories. Big data is not going to replace traditional forms of intuitive learning and making sense of the world. Although quantitative data may suggest necessary changes in a particular community, qualitative researchers are going to remain critical in surfacing the impact and context of those changes from a socio-cultural perspective. However, since stories have their own limitations, quantitative data is an important avenue for enabling communities to experiment with alternative ways of thinking about learning and knowledge sharing in the 21st century.
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