The uncertainty of not knowing what lies ahead is disarming in many ways because it renders us powerless. One way to navigate that ambiguity is to use historical data to forecast the future. The predictions help create some certainty about where the future is headed and, in some way, make things less obscure. Various modelers, for example, had anticipated future COVID-19 infection cases, death, and recovery rates. BMC public health projected that Kenya would reach 1,000 confirmed cases by 14th April 2020 (33 days after the first confirmed case) and 4,000 cases by 21st April 2020 (40 days after the first confirmed case). The Ministry of Health numbers were similar, projecting “1000, 5,000 and 10,000 cases of Coronavirus by early, mid, and late April 2020, respectively ceteris paribus[1]”[2]. In hindsight and according to WHO data, on 14th April, we were at 218 confirmed cases, and by 21st April, we were at 291 confirmed cases.[3]
Other interesting examples of data forecasts include those made at the beginning of the year. GDP was expected to grow to 6% following consistent growth in the last five years, which placed Kenya among one of the fastest-growing economies in Sub-Saharan Africa. The expected growth was attributed to continued government spending of 25% of GDP on infrastructural ‘development’ projects, high remittances of 2.9% of GDP, a solid consumer base which accounts for about 80% of GDP, and increased foreign direct investments due to ease of doing business and high macro-economic stability. The continued extraction of oil from Turkana County and other exports, trade, and anticipated higher investments in the big four sectors - health, housing, manufacturing, and agriculture were also expected to boost the economy significantly. The investments would result in savings on health care, increased access to housing, and enhanced food supply (also propelled by expected favorable weather), which would then bring down the cost of food and reduce the cost of living hence lower inflation rates of about 5%.
Unfortunately, the forecasts have had the opposite results, primarily due to the unforeseen effects of locusts and COVID-19 (events that were completely out of anyone’s know or control). Between April and July 2020, the economy was projected to decline to 1% as government investments “slowed down significantly, and civil works were delayed by global supply disruptions and the limited supply of labor associated with COVID-19”[4]. Remittances declined by 20% between January to April 2020[5]. FDI was projected to contract by 25 to 40 percent in 2020, and the adjusted inflation was projected to increase to 6.5%. Labour force participation declined from 75% to 57% due to job losses meaning that the consumer base had considerably shrunk. Export revenue was expected to reduce by a minimum of 25% (USD 1.5bn), while trade with China was expected to decline by about 40%. The COVID -19 pandemic coincided with the start of the maize planting season, the primary staple food. Following a sharp decline in maize production in 2019 due to early-season drought, the unresolved locust pandemic as well as floods in 2020, food stocks are likely to decline, and food prices considerably increase thus a high cost of living.
The point this article makes is not that forecasts are bad but how they are presented and in turn consumed is a big problem. Modelers present forecasts as future facts instead of indicative possible future trajectories that could happen. When the future does not pan out as predicted, people mistrust data or downplay its importance and, therefore, do not use it to plan or prepare for various eventualities. Forecasts are also inadequate by themselves because they emanate from the fact that data projections are linear. They map historical and current data to show future patterns – which is not necessarily how life works. Past trends can indeed continue in the future but not always. Data analysts try to incorporate assumptions into forecasts to bring out other variations of the trends - hence terminologies like the base case, low case, or high case scenarios. However, these data patterns usually consist of stand-alone parameters which, when interpreted by themselves to discuss multifaceted societal issues, will obviously give a false picture of the whole. This means that the interconnectedness of stand-alone parameters has to be explored together because that is how life works hence an analysis of multi-sectoral patterns, their relationship to each other, and the ensuing implications of those multiple relationships.
There is also a dire need to unpack the reasons why a trend moves a certain way and uncover other factors that would affect the course of that trend in a different way from the current norm. Understanding these push and pull factors altering patterns enables people to get a more accurate picture of how the future might unfold, which is seldom incorporated in predictive data. Tracking the evolution of these underlying aspects altering data patterns, some of which are not quantifiable but qualitative in nature, is particularly painstaking but important because it points to where the change is likely to happen in a more precise fashion. This way of processing data removes the blind spots that keep people locked inside some obsolete assumptions of what the future looks like and which blocks them from seeing emerging realities that eventually become the main cause of their strategic failures.
Ultimately, data and trends are only meaningful when they are innovatively processed, and important insights are extracted to enrich people’s understanding of the issue. However, the value of data, trends, and patterns is in the analyzed and leveraged information to enable evidence-based strategic decision-making, organizational agility to manage risks, and positioning for resilience.
[1] Latin word meaning all things remaining constant
[2] Ministry of Health website:
[3] This fact is not meant to belittle the gravity of COVID-19 in any way
[4] World Bank. (2020). Turbulent Times for Growth in Kenya: Policy Options during the COVID-19 Pandemic. Kenya economic update, 21
[5] From 259,392.71 in January to 208,217.73 USD thousand in April 2020
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