Behavioral Analytics and what Lies ahead

There are many definitions of data analytics you can find but a threadbare one would be — that which helps to predict and course-correct organizational strategy using meaningful data. This is something we all understand about data analytics. Also, we have many applications and facets to what data analytics can do for an organization to improve its profitability.

Very recently I went through a course in Data Science & Decision Making sponsored by Couresera/PWC and the crux of that course was to understand what Data Analytics is all about and what future possibilities this technique would solve.

Here is a picture that describes an application in different domains and how deeply Data Analytics in conjunction with AI can be used. It is but all-pervasive in almost all areas we are exposed to. Starting from medicare, shopping, travel, and others.

Source: Analytics HR

Everything starts with a problem, just like the way we have it in life and ends with a solution. But what is fascinating about analytics is the way it merges with future solutions for a problem that does not exist or which is unknown. It’s a paradox but it is true. Our machines are learning and anticipating faster than the human brain. Machines can envision the pitfalls that exist in the future. So to an extent, we can say Data Analytics as something that’s ring-fencing AI / Machine Learning in order to derive meaningful problems(Yes !)and possibly the solution too. It’s a metric that can be measured, applied and enhanced because it involves numbers.

While there are obvious use cases that can be solved using Data Analytics as shown in the diagram, the trouble in my view is the one which involves human behaviors — how it impacts the decisions they make and how those actions can be mapped to find tangible use cases for organizations. What drives impulse actions vis-a-vis the measured one? What is the noise in human actions?

How could we ever rely on human thoughts when they have the “Experience Brain” and “Remembering Brain” as mentioned by Daniel Kahneman in his book “Thinking, Fast and Slow” which apparently won the Nobel Memorial Prize in Economic Sciences. We seem to have 2 minds in one! “System 1” is fast, instinctive and emotional; “System 2” is slower, more deliberative, and more logical. Thus the area of decision making influenced by our thought process is vast and way too many factors to consider so as to make a precise judgment in data analytics. Source: Wikipedia

In a seminal work done by Gauri Bansal around human psychology on Data Analytics and how it influences our action, she has articulated this very well in a blog that she has authored.

I quote: “ Artificial Intelligence in relation to Data Analytics has set a benchmark in every other industry and is now all set for behavioral science. The thought process of a human determines his/her actions and the collected data are used for further analysis. Psychology is present everywhere, it is not just in medical terms but also in our day to day lives. What does a consumer buys at a particular shop is also something that clearly determines human behavior? The talking patterns, the shopping patterns, the sleeping patterns, everything is related to behavioral science. How does a brain perform in different situations also require research to be done? And, with big data, this field of science has only enhanced.”

It only goes on to prove that human psychology is an important parameter to consider and the most difficult one to analyze. Of course, NLP and advanced analytics do help to map some of the decision trees. But there seem to be a gap in understanding what data is relevant for consideration and what noise to leave especially when human thoughts are involved. Should we then look up to behavioral economics and its science for qualitative data? Needless to say, the industry is coping with challenges to quantifying the real benefits of analyzing and implementing solutions around human behavior.

Very recently I read a book called “Being Mortal by Atul Gawande” about medicalizing aging, frailty, and death. The book goes on to give insights about human psychology and how people gladly give up any medical intervention/surgery to accept the end of life care support in a supportive environment. “Being Mortal” not only gave evidence but materially proved that, death can be delayed with care and is influenced deeply by human desire towards the love and care they want from their families even at the cost of succumbing to the disease. This is astonishing and even more so when death is facing them and surgery could possibly extend their life. Gawande calls for a change in the way that medical professionals treat patients who are approaching their ends. He recommends that instead of focusing on survival, practitioners should work to improve the quality of life and enable well-being. Gawande shares personal stories of his patients and his own relatives’ experiences, the realities of old age which involve broken hips and dementia, overwhelmed families and expensive geriatric care, loneliness, and loss of independence. This insight revolutionized the entire geriatric industry and today we see retirement home/hospice care all around the world. Had we just looked at data(ex: surgeries and death rate) and analyzed based on what we find in medical records overlooking human behaviors, we surely would not have revolutionized the end of life care support which has changed the very nature of how we look at life and beyond!

This and many more use cases that impact society and touches human life calls for how we look at Data Analytics and how we can revolutionize AI/Machine Learning to complement the data. This will have a profound impact on future generations to come.

It is not anymore about profiteering and capitalism which this technology should aid but look at possibilities to influence society in a positive way and revolutionize the economic charter of a nation.

As an American psychologist aptly said

And it is worth re-reading this quote to understand what challenges lie ahead with AI and Data Analytics to derive meaning out of motley of thoughts and the ripples it creates!



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