Analytics is a technology facilitating business, psychology, living standard, etc. But how does it work and what makes it so strong?
The answer to above question also explains one more controversy: Why are big business giants so interested in tracking everyone and collecting their data, so much so that they are ready to risk their credibility for that?
Data Analytics is completely dependent on existing data. It uses Big Data, Data Mining, and Machine Learning to develop AI suiting the situation. Here, we need to under- stand the scope and strength of AI and is it worth for businesses to invest a huge share of their profit into it?
Analytics in simple words is making sense out of a piece of information. Data mining is taking away maximum inferences possible. Machine learning is coming to a conclusion using all processed information. And Big Data is our raw product to be processed for it. The level at which data is being collected currently, it’s not only possible to know the business trends, forecast profit but also to get deeper into customers’ mind. Predict their behaviour with accuracy and understand the philosophy leading to it.
Now it’s high time to get into functioning behind AI because this is the next big boom. Artificial Intelligence isn’t some Rocket Science but just a logical structure built using Big Data. Technical skills required for this is getting minimal with updated tools to churn out meaningful data but what is now required is on how to interpret it. This is where B-schools come into the picture. Each and every concept studied in Kotler and other great theories now find its way for near to perfect implementation through AI.
Traditionally, people used intuition and their own inter- personal skill to deal with a client, and this played a major role in deciding the course of business. Decision making relied on how powerful human brain was to interpret information on competitors, market and consumer. But now we have a machine to do it for us. Why is it better? Because it can easily analyse millions and millions of row without any bias, easily take into picture numerous factors and process them simultaneously. In the end, it gives a fast result with higher accuracy.
And the most important tool used for this: Statistics. Statistical concept defines on how to interpret data but hu- man error had always been the concern. But with Ma- chine doing the work, statistics is now being leveraged to another level.
The scope of AI is not just restricted to analyse simple business but it goes beyond in identifying gaps in supply and demand, competence or even a hidden problem. The right decision would give the companies an upper hand in the market and increase profit multiple folds. Unlike earlier times where the solution could be validated only after real-time implementation, now you can test multiple solutions simultaneously on sample data and predict the outcome of each with higher accuracy.
Not just limited to business, AI can now be used to predict the upcoming trends, human sentiments, mortality, climate change – basically anything and everything. The scope of it is limited to one’s imagination. The more we explore and swim through data, play around it and make friends with it, the better we get at predictive modelling.
So if one is inquisitive – the kind who would rather open up their television than read its structure in the book, this is the field they are looking for! Because here, the more questions you ask and more you tear through the data.
By Saumya Khaitan | MBA FT (2015-17)| Amazon (Program Manager)Author Saumya Khaitan | MBA FT (2015-17)| Amazon (Program Manager)