It’s all around us. The buzzwords of technology. The elite bunch of myriad technical standards often confuses us, much as a tough early-morning newspaper crossword puzzle does! Using technology has become an indispensable way of catering to the umpteen real-life challenges that are thrown at us. Data Analytics or Business Analytics is the most used (or overused?) buzzword for that matter.
For me, analytics is the single most primitive game changer that evolved over the past so many years. Other recent technologies such as Big Data, Machine Learning, Artificial Intelligence or any Gartner Hype Cycle technology are just manifestations of the core idea of Analytics. What I mean is, Analytics has been here all along, we have just begun realizing the core advantages it provides. Put differently, Human brains have been capable of fairly complex data analytics since time immemorial. (Copernicus, Galileo? They observed vast amounts of celestial data and analyzed it to gather insights).
Most experts in the field of Analytics define information capture/processing as a precursor to enable the use of Analytics. While constructing an end-to-end management framework of developing insights, I observed several steps naturally form critical parts as highlighted below.
If the flow-chart looks complex, please allow me to simplify:
The model constructed above can be applied to any real-life example that requires analytics in some way or the other. Let’s take an extremely simple case. You are driving a car on an expressway. The steps in the above model that have been labeled from A to F fits straight-in as follows:
(A)- You are driving with your family on the highway at 50kmph. You periodically observe the odometer or speedometer. (capturing data)
(B)- You enter the smoothest section of the expressway. You bless NHAI (!) and decide to speed up to 100-120kmph. Parallelly, you also observe the recurring speed warning signs on the highway. The signs ask you to limit speed to 50 kmph and be cautious of the accident prone area up ahead. (processing information)
(C)- You process the knowledge correctly and you quickly recollect reading about the several major accidents that occurred in that particular section of the highway. You know the consequences of over-speeding. (knowledge inception)
(D) & (E)- You instantly realize the ill-effects of over-speeding and decide to slow down. (engineering ‘wisdom’ and delivering insights)
(F)- You slow down to 50 kmph and keep it under constant check. (actionable decision making)
The amazing part is when all of the above six steps are processed by our brain in probably a fraction of a microsecond. We never have to exert pressure to think or evaluate in such a case. Our brain does this for us – subconsciously.
I believe the model can be used and is definitely in use across organizations where critical decisions are made by the upper ranks of management. It’s a fairly intuitive process that is now often aided by analytical tools. Many a time decisions are taken by just looking at data.
Organisations extensively use data analytics to process, analyze information and discover trends and patterns. These patterns identified may be restricted to the particular business problem at hand and we may have to revisit the “wisdom” stage to redefine insights (what our biases say, what our business clients expect and what we can deliver) and act accordingly. The dotted lines in the above workflow highlight the re-visits required.
Our subconscious mind is extremely creative and is constantly processing external stimuli. The stimuli are then transferred to the brain’s decision making nodes. An AI bot, on the other hand, might capture data, sense it, and take decisions but it will always lack the innate human touch or edge.
To conclude, let me put forward this outrageous concept:
Merge powerful analytics with our creative subconscious mind and it will create an entirely different spectrum of understanding. Disruptive technologies are the norm and this will surely carve a way to the future. It’s indeed a promising area of research and maybe in another 20 or 30 years, we as future business managers might venture into this space where we can leverage analytical tools to heighten our subconscious capabilities to make rational business decisions.
Human brain functions in most unimaginable ways and the deeply complex brain architecture will only complicate things further. It’s up to our pioneering scientists to upend the existing neuroscience research and come up with a disruptive cognitive technology that can drive rational decision making.
Imagine a critical business decision that can make or break an organization. Consider the time and effort expended in taking the decisions – some of which may end up as futile and some successful. Now, what if we use analytics to take the decision rationally in a split-second or, more realistically, in a few minutes? A decision that required sifting through vast amounts of data. And it’s so quick that the end decision still feels absurdly intuitive!
Can a standalone bot software be designed using deep learning? Can the bot simulate sub-conscious AI threads which imitate human intelligence and then integrate with business analytics to make critical decisions? Something on the lines of the Blue Brain Project undertaken by HUJI neuroscientist Prof. Idan Segev.
This is indeed the greatest intellectual challenge of the 21st century.
A humongous undertaking of biological and neurological studies along with complex machine learning models will be required for this study. If it works, it may well bring about a paradigm shift in the way all enterprises analyze business problems to arrive at the best possible solution.
Is this concept sheer lunacy or miraculously achievable in the years to come, thus setting the stage for the future of cognitive management rationale?
Only time will tell.
Authored by Ashwin Nair, PGPM(MBA) 2018, Great Lakes Institute of Management, Chennai.
