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Wednesday, November 25, 2020

Hurdles in Data science industry


“Data is the new oil” – Clive Humby


In the current world scenario, the word ‘data’ has got a huge valuation. Not only is it the plural form of datum, but also it is one of the most valuable assets that someone can have. Starting from the current date and time at a place to the stock market numbers, everything is data. Due to this huge availability of data and as there are huge number of sources from where we get the data, we sometimes term it as Big Data. But the data that is available and surrounding us, is very distorted and haphazard. These data need to be analyzed and oriented in a manner that is easily understandable to the common public. Thus, converting the raw data to information.

               


The information that we get from the various collected observations need to be further analyzed and converted into a form that can be used to predict the future trend or perform certain operations or help in solving real world problems. Data Science is that segment of Machine Learning, in which the data fed into the machine is analyzed and operated and performed upon various calculations to figure out the trend in the growth or fall of a specific dataset. Data Science mainly uses skills of statistics, mathematics and computer science to visualize the data in a way that identifies the trend in the rise and fall of the values and helps in predicting the outcome. It helps organizations to identify regions in which the products sell the most, to investigate the diseases that impact the most in which parts of the area, to identify the products that bring the most profit to a firm etc.


So far, we see that data science happens to be a very beneficial field in the current generation, and implementing it would help the firms to grow in a better and faster way. But as any other technology, this boon also has some hurdles that need to be dealt with before implementing in the various sectors. One of the primary obstacles in this field is the lack of people and infrastructure needed to maintain this technology. The implementation of data science requires in depth knowledge about computers and the languages and software needed for the purpose. Furthermore, it also requires people to have a knowledge regarding how the businesses and services in the various sectors function. Training the people for this information is in itself a big task.