Data Science and the Negative Buzz

Just a few years ago the internet was crowded with write ups probing and praising the incredible applicability of big data and data science in different industries. The trend became so strong that one could spot tech savvy people talking about big data analytics and related stuff everywhere. That is what you call a buzz. It literally sets the tone for the debates and discussions. And it can be greatly helpful to any industry as you can realize.

However, in recent times I have been coming across a fair amount of content that is brutally skeptical about the effectiveness and future of data science. We can call this phenomenon a negative buzz- it can be as powerful as a buzz but of course in a depreciative manner. This does not occur right out of the blue, there must have been some crucial reasons which can lead people to believe that investing in big data analytics or data science is a lost cause. Let us take a look at the possible reasons that may have helped these ideas surface.

Going with the wind

Trends and buzzwords look nice on paper and on the social media, once you follow them blindly in a business, you are half as likely to fail. Yes, you cannot go for a fifty-fifty chance when you are investing a fortune for it. What I mean to say is that a lot of enterprise owners were led to believe that once they implement big data analytics it will instantly affect their ROI positively. Nothing could be farther from the truth.

  • Data analytics helps you understand the market better.
  • Helps you make insightful decisions regarding product optimizations
  • It reduces risk
  • It cannot raise profit on its own.

Once people start to grasp these simple truths there will be less confusion regarding the true scope of data analytics and data science. Blindly investing in data science projects without having a well thought out plan in place can often turn against the company – it does not address the right problems and also drains a great amount of cash. This in turn gives the whole development a bad name.

Skill gap is still a major issue

According to a survey earlier this year, 78% of the participating enterprisers desired to integrate analytics into their systems but the serious dearth of analytics professionals all around the world keeps this possibility at bay. There are currently a lot of private institutes as well as government run bodies that are offering data science courses but the number of professionals with the desired skills is still below the requirement.

Going by the current market status one might infer that the need of the hour is a strong batch of data science professionals with R training, or awareness of Python libraries, and some visualizations skills. A professional with fundamental data science skills along with in depth knowledge of R, Python or such other languages is going to rule the job market. The skill gap is really killing the enthusiasm in a lot of enterprises.

 Absence of a defining authority

Data science industry works quite arbitrarily in comparison to a lot of other technology oriented industries. There is no standard set of tools or rules. The whole thing is in incessant flux, you cannot really stay put for too long.

This kind of flexibility is hard to achieve. It requires regular up skilling of the employees and creating a mutable education system.

The fact that matters

It is not beyond all doubts that the developments in data science and analytics came with the promise of changing the world for better very fast. It is also true that all the theories flying in, claiming the death of big data and the failure of data science, are sternly debatable if not outrageous.

Let alone all the SMVs which are depending on data analytics to compete with the industry giants and all the NGOs which are trying to implement big data to find out the needy around the world, big data has actually taken scientific development ahead of us by several decades. Healthcare analytics is a real, accessible thing because of it; genetic engineering can skip years of testing with the help of data science.

These phenomena are not disappearing; they are rather announcing their indispensability in every walk of our lives.

 

 

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