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Data Analytics is the means of extracting raw data, analysing it and drawing conclusions based on it and identifying patterns. These data are computed by specialists known as data scientists. Right from the start, computers and data were synonymous with each other. It is not surprising that computer technology would not have advanced without the means of using database of information. The Relational Database aided in generating reports on data contents. However, this did not involve huge data and only provided with little or no business intelligence. Data Warehouses that predated the modern analytics was itself a kind of data analytics system but even those were not capable of efficiently handling huge chunks of data. Over the years, the volume, variety and velocity of data grew exponentially as internet and computer technology developed at an enormous speed in general. This was the starting point when data analytics as we know it, initially came into play. 
 
The growth of data has resulted in the demand for systems and expertise that rely heavily on bigger computational systems and high level data staticians for manipulating data queries and translating results. The prime objective is commerce and driven by business. It is unfortunately not yet used as tool that for studying the data for improving the social imbalance, providing help to the needy, etc. Instead big companies have even assigned tasks to automated machines to analyse huge volumes of data. Data mathematicians and statisticians study our desire, movements and spending power. A computer program could speed through thousands of applications, sort out in seconds and marketed as fair and objective. This raises some serious questions. Have you ever wondered why the disparity of data analysis has led to affecting job market for potential candidates, created questionable credit records, prevented many from getting an insurance, propelled injustice, increased the divide between the rich and the poor even further, led to a surge in targeted online advertising, production of dubious track records in the education and political arenas? These are due to choices made by fallible human beings. To manage our lives, we left everything to the mercy of these data companies that run software system that consists of human misunderstanding, bias and prejudice. Erroneous mathematical models now micromanage the economy. They are unaccounted and unquestioned for and they operate at a scale to store, target and optimise millions of people. By confusing their findings with the on-ground reality, most of them create deadly feedback loops.  This could have a severe impact to the society on the whole.
 
In her  book- 'Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy', Cathy O'Neil talks about the flawed methods of predictive models to run our institutions, deploy our resources and manage our lives. She says the choices we make about which data to pay attention to and which to leave out is fundamentally important from a moral standpoint. Failing to pay attention to this would result in compromising on our responsibility. We have to bring fairness and accountability to the age of data. This can only be done by regulating the data. Although, one could argue  that it depends on the way we use technology, day by day we are also getting lured into exposing more and more information about ourselves. This should be challenged and a law should be enforced to challenge this. Only then can we truly take the first step to use data analysis for the benefit of mankind. 
 


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