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The concept of predictive analytics has two aspects attached to the word “predictive” in a business scenario. One is to give us the probability of an event occurring based on past data within the confines of situations that are current today or to some extent with changes in the situation based on the historical variations of the various parameters that define the situation to arrive at results. The second aspect is to provide business with an insight as to what can actually happen if the paradigm of business complete changed.
Let us look at some examples:
The price of fuel – for a long time the automobile industry made and continues to make cars based on various market drivers which were more of an outcome of the spending ability of the buying population and to some extent fashion paradigms that revolved around price, technology, power, size , comfort to name a few. Even the electric cars that were on the drawing board for some time were being positioned as environment friendly with price tags that only the residents of Hollywood could afford. Except for Toyota that launched the first hybrid car that focused on fuel economy most others got it wrong. It will be naïve of us to not acknowledge the fact that the fuel price has been steadily increasing over the past 50 years but more so the population of the world has been growing at an even faster pace. Add to this a modest growth in GDP of the developing nations and all of a sudden we are faced with a situation that demand will definitely increase for fuel and so will the corresponding price. Could the automobile industry not have forecasted this?
The Financial meltdown – This was a pretty straight forward outcome to my knowledge that could have been averted specially by an industry that has been using analytics long before it became a hot topic. Without spending too much space on this topic, as it has been discussed and debated all around the world, the point is if predictive analytics would’ve have been applied in the right futuristic context couldn’t it have helped the government avert such a large financial disaster and save billions of tax payers’ dollars. What we need to look at is the applicability of predictive analytics to business at a transactional level and at a strategic level. From the two examples quoted above it is very clear that had predictive analytics been used at a strategic level we could have averted these mishaps. To make predictive analytics more pervasive and truly enhancing for business and society at large there has to be a very clear understanding of what we mean by future at different levels of the organisation.
I wonder if anybody in the air-conditioning industry is building a predictive model that will look at the effect on the industry in the next 25 years with respect to global warming, changes in weather patterns, cost of power, increase in pollution etc. I am sure there is! It is time for us to understand what predicting the future really means.