OPINION. Anticipating, predicting, figuring out what the future has in store…, predictive analytics has always fascinated us human beings. By Alain Conrard, CEO of the Prodware Group
“To govern is to anticipate,” as the saying goes. The etymology of “govern” derived from the Greek kybernan (to steer a ship) and from the Latin gubernare (rudder of a ship) refers to that of a rudder. The terms “govern », « rudder », « government » or « governor » all stem from the instrument used to steer a ship. The term used metaphorically describes the running of a state or the “governance » of a corporation.
Another word derived directly from kubernêtikê is cybernetics, a theory elaborated by Norbert Wiener in the United States in the late 40s. He laid the theoretical foundations for the multidisciplinary field of cybernetics, the study of controlling the flow of information in systems with feedback loops, be they biological, political, cognitive, social and so on. The theory is credited as being the first of many direct developments to come, that for a large part, have structured our collective imaginary and the technical-scientific reality of our contemporary world. There is no cognitive science, neurobiology, neuroscience, artificial intelligence, internet, IoT, IT, communication to speak of, no automation or robotics without the conceptual approach of this “comprehensive theory of control and communication as it relates to living things and machines.” Cybernetics has even contributed to the philosophy of ecology. In fact, the widespread use of the “cyber” prefix in modern-day language is a testament to the defining role of Cybernetics.
To govern therefore means steering the rudder in order to control, communicate and drive. In other words anticipating the potential turbulences to secure a safe passage, so as not to be caught off guard when the storm hits. Every business leader has got to get his “sea legs” in a way because the market today, just like the high seas, has become so volatile and rough. It is probably no coincidence that we say “captains of industry.”
The power of anticipation
Anticipating, predicting, trying to figure out what the future holds…, predictive analytics has always fascinated mankind. From Pythia to fortune tellers right through to shamans, astrologists, psychics, tarot or palm readers, we have always wanted to know what tomorrow is made of. Science fiction has largely drawn upon this phenomenon that spurs the imagination: The Minority Report, a movie imagined by Philip K. Dick, where a specialized police department called “Precrime” apprehends criminals based on foreknowledge is a perfect example of this phenomenon. Predictive algorithms are getting us very close to making this ancient dream come true.
Among the new breakthroughs in innovation are the numerous anticipation-ready capabilities (and therefore of the modelling of the complex evolution process in time) which are undoubtedly the most fascinating.
In the business world, intuition has always been a trusted and natural instinct relied on as a decision-making tool, especially for business leaders who know their respective market inside out. However, when considering the recent advances and performance levels of predictive solutions, relying on instinct is more or less akin to steering and driving one’s business in heavy fog, or making important financial projections for the company based on the only hope that the outcome turns out as expected and keeping one’s fingers crossed to avert bad luck.
The history of information used to make informed decisions and projections has been linear for a very long time despite whatever crises. And this linear pattern of information allowed for an appreciation of the future based on statistical variations rather easy to anticipate. This type of historical data just does not cut it anymore. We cannot afford to only rely on the past (and on certain hand-me down practices) to figure out what to do in the future. There is an urgent need to take into account the range of complexities of the different contexts.
Moreover, today’s market shifts and disruptions are so unheard of that experience alone, once something we could count on, is now drowned by instability gone fully systemic, and is, at the end of the day, on its way to becoming useless. In other words, in order to be able to predict, you need to factor in a much more significant array of parameters than « before » – the « before » designating a very recent period. It is also important to process these parameters with greater insight in order to highlight the different existing correlations, often difficult to perceive, without resorting to the new business-decision making tools and solutions available today (Business Intelligence).
It is about both appreciating more clearly and more vastly, and seeing farther beyond and far more accurately. In short, it is about leveraging new technologies to determine what goals can reasonably be set and what means are needed to achieve these goals.
Predictive analytics is one way to cope with uncertainty, which reigns supreme, in a time where nothing is sure and especially where nothing is stable anymore. Trying to anticipate is even made more challenging because of the numerous and growing interactions between the many different economic players within a globalized market (suppliers, raw materials, sub-contractors, supply chain, distributors, logistics, politics, and so on…). Hence, any management decision today, has to factor in a level of complexity never encountered before.
Today, more than ever, one of the major challenges a company has to face is to figure out, as precisely as possible, what the expectations of its potential target market are (both in the B-to-C and B-to-B segments) with regard to the products it currently manufactures or will be manufacturing in the future or those products it intends to upgrade. Anticipating this way will help in planning and adjusting the production rates accordingly, and furthermore these decisions will have an important impact on logistics and the distribution process. In fact, being able to predict customer demand will map out the entire economic cycle of the business and in the end, the profit margin. Today, anticipating customer demand ranks, in large part, as the top business driver or even what enables a business to stay in the game.
Deciding when decision-making is next to impossible
The global pandemic has unleashed an economic tornado like never before. The Covid-19 crisis has brought such consequential parameters to the surface that they could seriously call into question most of our proven analytical methodologies. The intensity, breadth and brutality of the crisis has brought into view unknowns with multiple variables that demand we appreciate data sets in many different ways in light of the new complex and random evolutionary models. This crisis does not only do away with the regular behaviour patterns usually used for predictive modeling of future trends but underscores the many vulnerable areas of the global economic system. The situation we are going through is a textbook case of the value and pertinence of economic and social predictive algorithms.
With the insecurity fueled by the pandemic all over the world, business moving forward is far from being a walk in the park. This very troubled period is marked by an extremely volatile economy. Predicting customer demand has therefore become a necessity while also being harder than ever to do. Although predicting demand is not something new per se, it has reached unprecedented performance levels with the advent of new technologies (essentially Artificial Intelligence, Machine Learning, Big Data, the Internet of Things) and the growing computational power of computers. Predictive algorithms help stay the course amid an ever-fluctuating bubble where there are no patterns to speak of, where the tracking history can no longer be used as a reliable source to draw up statistics. They help make near-foolproof predictions of future potential scenarios with a newly acquired and developing power of clairvoyance feeding into the decision-making process without which efficient and informed decisions could be made.
Reducing complexity by resorting to complexity
These new tools using multi-sector metrics based on artificial intelligence and deep modelling approaches provide multiple future scenarios and alternatives. Then, after comparing the different scenarios we can map out the different B, C, D etc. action plans. Thus, the updating of these alternative future scenarios greatly secures investments by planning supply, production rates and logistics in line with the actual and factual situational factors.
By making sure the future is not just a mere linear continuation of the present and by elaborating complex scenarios that may appear to be counter intuitive at times but are much more sensible, these Business Intelligence-ready predictive algorithms help make truly informed decisions. Moreover, you can generate weekly or monthly predictive analytics dashboards for more visibility on the future, leaving little room for uncertainty. The technological complexity of these tools brilliantly addresses the psychological and analytical complexity of decision-making.
Predictive decision-making algorithms are new ways of embracing and rethinking the future, setting the stage for what to do in the present. Because these algorithms can potentially unearth new profitable growth opportunities, they are working their way to becoming authentic business co-pilots for any business today willing to secure and ensure its growth strategy and future.
In some way, they are both a compass and a rudder that help safely navigate the rough seas, flagging and anticipating the turbulent weather ahead. With what innovation brings thanks to predictive algorithms, we can now say, “that to anticipate is to succeed.”
(*) By Alain Conrard, author of, “Taking the Plunge! A Different Take on Innovation » published by Cent Mille Milliards, in September 2020, and CEO of the Prodware Group and President of the Commission on Digital Strategies of the METI, a professional guild of French mid-market companies.
Article initially published in La Tribune