Futurology belongs to the past. We are in the era in which data prevails and we must take advantage of all the insights obtained through analyses.
In the current economic context, it is clear that reality runs at a voracious pace if we consider the ability of organizations to plan their next steps in an appropriate and timely manner.
Faced with the gigantic market competitiveness, it is therefore more challenging for organizations to face the future - in particular, if we consider the visible evolutions such as the widespread use of devices based on the Internet of Things or Artificial Intelligence. At the same time, this is a clear sign that organizations should bet on the design of scenarios that allow the anticipation of various future hypotheses, whether they represent opportunities or risks.
One of the best-known cases of organizations working in this way is Shell, which, since the 1970s, has pursued this scenario-forwarding exercise, something that has been leveraged by the organization itself, but also by governments, researchers and other companies.
However, the anticipation of scenarios puts us in two different situations: on one hand, the mere hypothesis of not having any kind of anticipation on the part of the organizations; on the other hand, the carrying out of uninformed projections or based on inaccurate or inaccurate data. In the case of the first idea, the consequences are evident: big difficulties in perceiving where the market is headed, where the organization should position itself, the areas to which the investments should be channeled, or what changes in customers' needs, for example.
There are cases of organizations on the market that have done this work in the sense of perceiving where the future was going, but which have failed in this objective, either because they made a mistake in the process, did not have the data necessary to understand the object of study, or because they did not know how to interpret.
We can refer to the case of the Detroit automobile industry in the 1980s, known for having the management teams of three of its major manufacturers outlining a set of scenarios that included the so-called "Long Live Detroit", which the US auto industry would thrive in the years ahead. However, due to the calculation error in the forecast of the fluctuation of oil prices and consumer options, they ended up facing a deep crisis in the face of the growth of the new Japanese competitors.
Having this need in mind, it becomes relevant to realize how we can operationalize this type of exercises. It may be interesting, for example, to start the approach with three distinct scenarios: a base scenario (essentially the one that corresponds to the average of what has been the reality in recent years), the best and the worst scenario possible.
In this context, the approach should include essential elements such as identification of the main driving forces (society, economy, technology, environment, politics), identification of the most critical uncertainties (two should be chosen from the previous list), development of a range of plausible scenarios (forming a matrix or a spectrum where the various scenarios will be inserted), and the discussion of the respective implications of each scenario.
In this context, it is important not to forget also the definition of process governance, which should have in mind the inclusion of boards members and executive directors of organizations, for example. This option is not only due to the weight they have in the structure of the organization, but also to the contributions they will make throughout the process, taking advantage of their equidistance in relation to the more operational side of the work developed by the organization.
However, as mentioned above in the case of processes of anticipation of scenarios that were unsuccessful, there is no point in having an excellent process of internal debate if the assumptions used are wrong or depart from unfounded concepts or ideas. And this is where data analysis takes on the role of protagonist.
Through the use of Analytics and Big Data tools and the analysis of relevant data for the different scenarios we are considering, we can proceed to a very assertive and accurate predictive analysis. All this counting, logically, with the appropriate variables of the equation that insert the various scenarios in the spectrum of possibilities (from worst to best scenario possible).
Thus, it is imperative to work on these scenarios, starting with reliable data sets so that the forecasts can be as realistic as possible and not just projections based on unfounded prejudices and "feelings" of more optimistic or pessimistic decision-makers.
For this reason, analysis based on the actual data of the organization itself, the market, competitors, consumers, or changes at political and societal level is essential if we are to put on the table different themes such as planning and budgeting, or risk and compliance management. It is important to realize that futurology belongs to the past. We are in the era in which data prevails and we must take advantage of all that we can obtain from these analyses.
Opinion article published in Jornal Económico – march, 2019