Businesses have long relied on data mining to elicit patterns and forecast future demand and supply trends. Improvements in computing hardware, specifically storage and compute capacity, have significantly enhanced the ability to store and analyze mountains of data in ever shrinking time-frames. Nevertheless, the reality is that data growth is outpacing storage capacity by a factor of two and computing power is still very much bounded by Moore's Law, doubling only every 18 months.

Faced with this data explosion, businesses are exploring means to develop human brain-like capabilities in their decision systems (including BI and Analytics) to make sense of the data storm, in other words business events, in real-time and respond pro-actively rather than re-actively. It is more like having a little bit of the right information just a little bit before hand than having all of the right information after the fact. To appreciate this thought better let's first understand the workings of the human brain.

Neuroscience research has revealed that the human brain is predictive in nature and that talent is nothing more than exceptional predictive ability. The cerebral-cortex, part of the human brain responsible for cognition, thought, language etc., comprises of five layers. The lowest layer in the hierarchy is responsible for sensory perception i.e. discrete, detail-oriented tasks whereas each of the above layers increasingly focused on assembling higher-order conceptual models. Information flows both up and down the layered memory hierarchy. This allows the conceptual mental-models to be refined over-time through experience and repetition. Secondly, and more importantly, the top-layers are able to prime the lower layers to anticipate certain events based on the existing mental-models thereby giving the brain a predictive ability. In a way the human brain develops a "memory of the future", some sort of an anticipatory thinking which let's it predict based on occurrence of events in real-time. A higher order of predictive ability stems from being able to recognize the lack of certain events. For instance, it is one thing to recognize the beats in a music track and another to detect beats that were missed, which involves a higher order predictive ability.

Existing decision systems analyze historical data to identify patterns and use statistical forecasting techniques to drive planning. They are similar to the human-brain in that they employ business rules very much like mental-models to chunk and classify information. However unlike the human brain existing decision systems are unable to evolve these rules automatically (AI still best suited for highly specific tasks) and  predict the future based on real-time business events. Mistake me not,  existing decision systems remain vital to driving long-term and broader business planning. For instance, a telco will still rely on BI and Analytics software to plan promotions and optimize inventory but tap into business events enabled predictive insight to identify specifically which customers are likely to churn and engage with them pro-actively.

In the next post, i will depict the technology components that enable businesses to harness real-time events and drive predictive decision making.