Predictive Modeling and Big Data Analysis

With the inception of E-commerce and the abundance of E-commerce website, one thing is exponentially growing and that is data. This data is all about people; what they like what they don’t like and by leveraging this data letting the user know what he or she might like. Having said that, lets delve into how important is Big Data Analysis and Predictive analysis is for E Commerce. Here we will be talking about impact of Big Data Analysis and Predictive modelling on E Commerce, and how and entire industry is thriving upon the huge data that has been generated by us.

Whereas the results of these analysis have huge advantages and impact on the daily routine of the user but these also have some disadvantages also associated with them.

Once you have clicked on a particular website for browsing some products or buying something let’s consider booking a flight. You are surfing from one device for particular destination. Once you start searching depending on the destination you were searching and time you have spent on it no doubt you will find various offers for the same one any of the social media platform you log in.

But there is a huge drawback as well as for consumer which is known as dynamic pricing. Once you have searched for that flight on a particular device you will see a significant amount of increase in the price if you are searching for same flight after some time. But if you try same thing with different device you will find lower rates than compared to yours.

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So this information collected can be channelized as benefit of the companies. If a consumer wants to use for their own benefit they can manipulate in such way like searching the flight from one device and booking from other one so they can get it cheaper. Secondly the price of flights are automatically hiked during holiday season this is also an example of dynamic pricing as the prices increase as the traffic of the booking tickets increases. The website based on the destination also gives you the hotel offers as well. The other thing which has to be taken in consideration is Group Influence. When a consumer enters an online shopping site, they are not only met with information created with the purpose of informing about the products’ properties, but they also see content created by other customers or even friends.

Here for the reference let’s consider AliBaba which is well known merchandise website. There is a reference in one of the case study regarding one fact that AliBaba itself buys a specific product a lot of time so that its demand goes high and its algorithm starts showing it on the first page of its site. So when a consumer goes on website they will see these products regardless of the quality of the product this will be highly rated products. Because the reviews are both generated and biased which influences the consumer. One of the other things which influences the buyers rating. One product which is good for one cannot be as good for other and the individual choices also influences the customer’s preferences.

Coming to the second part of the question of whether predictive modelling and Big Data Analysis is just an inevitable part of our society’s technology today or should it be restricted, I would like to start the answer of this question by saying; If we see the current landscape of our life or the society’s technology adoption over the years, I think predictive modelling and Big data analysis is an inevitable part, but as per Albert Einstein “The difference in between stupidity and genius is that genius has its limit”. So if we over do this then we will start making new problems for ourselves like privacy and if privacy is breached then we can be influenced at our subconscious level, and at some point of time every other decision of our life will be based upon some or the other predictive modelling or some kind of big data analysis. So we should learn to rein the data horse at its perfect time so that we will not lose our very own decision making skills. Because only an emotional human being does not need a tonnes of data behind to make decision, an emotional human can make perfect decision at the very moment when he encountered with it but an emotionless machine driven by predictive modelling and big data analysis can’t do this.

This is one of the major downside of the data being used. The concern over whether governments are illegally collecting big data about their citizens reminds both organisations and individuals to consider the delicate balance between the benefits that big data analytics bring, and the ethical and privacy risks they pose. Individuals are not without responsibility by offering their personal data for free Internet services. Yet organisations should initiate an internal debate on the limitations of big data analytics and guidelines to avoid public embarrassment, mistrust and liability. Even though people are expected to know what they are doing, and there may be no legal issues after consumers consent to providing information, there is reputational risk to companies if consumers feel their trust and confidence was breached. What consumers trust you to do (or not do) doesn’t necessarily equal what is legally allowed to do.

This issue needs to be handled with great sensitivity as when the data privacy is to be handled it has to be done very carefully and taken in consideration that no ethics or rules are broken. Similarly as this inevitable part of the system we have to try to keep managing the data but with some restrictions. There some rules which needs to be followed in order to use this data and manage it in better way. As a result of this ethical debate, information leaders should, together with their marketing and legal departments, develop a code of conduct for big data analytics. This code of conduct should contain the list of principles that describe what the company finds appropriate and inappropriate, a process that describes the ethical checks and balances when conducting big data analytics, legal implications, whether the intended use of the data matches how it is actually being used, and if the organisation would be comfortable if the results of it became public.

Craig Mundie once said “Data are becoming the new raw material of business.”, and these data are being used for all the predictive and big data analysis in every step of our life staring from predicting the weather for tomorrow or even to influence you to buy a jacket for the weather you are going to face tomorrow. Whereas from a company’s point of view all these seems perfect and all these seems user friendly and enhancing the customers experience but from a customer’s perspective it’s a violation of the basic right of privacy and it also hampering the decision making skills of a user. No doubt there are also advantages from the customer’s point of view, but when there is a risk towards the privacy then at that same time it outweighs all the other advantages. The predictive modelling and the Big data analysis is like double edged sword, which has to be wielded by the best samurais as in this case best data scientist, and if that is not the case then Predictive Modelling and Big Data analysis is the dark knight we deserve but not the thing we need right now.