These days business houses are stockpiling a huge amount of data which is often considered as the precious asset for the companies. It is surprising to know that more than 90% of the data which is available nowadays has been generated in the last two years. In earlier days, due to the scant knowledge, the companies didn't know how to extract meaningful and relevant information from this stored data. But the advent of data analytics has successfully bridged the gap between the company and this unpolished data.
So, it can be concluded that the data analytics has completely changed the vision of the companies and by using the comprehensive business analytics, the companies can make the right decisions which will help them to surpass their contenders. Hence, the organizations are accentuating on data analysis which is extracted from raw data by specialized computer programs and are cultivating their employees regarding how to accustom and publicize the information that they are getting from these organized data.
Since the importance of data analytics is burgeoning day by day, hence the companies are appointing the sagacious professionals who will provide the company with the wider insights of the structured data. A data scientist will be responsible for designing and implementing various processes and different layouts for the intricate and large-scale datasets that are basically used for modeling, data mining, and various research purposes.
What are the core responsibilities of a data scientist? Why they became an integral part of every business?
Need to take care of those data which affect the organization most: A data scientist's core job is to identify the most relevant data which will help the organization to make the right decisions so that they can proliferate their business and growth. A data scientist usually dives into the pool of data and with his expertise and knowledge, he used to find all the imperative information and ignores other irrelevant data so that the company can take the apt decisions quickly. Suppose a company deals with mobile phones, then they should try to find out who is using their phones currently? How can they find more users like them? Only a pedantic data scientist can answer these questions and hence, the companies are employing more data scientists into their core team.
Need to present data in such a way that anyone can understand it: Though a data scientist should be well-equipped with all the technical and machine languages like R, Python, etc., he should present the data in a facile and simpler way so that even a layman can understand the insight from the data. A data scientist should never show a regression analysis or a plot from R because only a few people have adequate knowledge regarding these. Rather he should present the data in a storytelling way which consists of simple slides and visuals instead of numbers. Visualizing and communicating data are equally important, especially for the nascent companies who are making the data-driven decisions for the first time or the companies where these professionals are viewed as people who help others making data-oriented decisions. In this way, everyone in a company should understand which portions or departments of the company need further improvement.
Help in the promotions and other marketing strategies: A data scientist will also work coherently with the marketing team and helps the company to conduct the fruitful campaigns and promotions which will certainly enhance the sales and profit. If a mobile company has an idea who is their most engaged customers, then a data scientist will help the company to see what campaigns those members liked the most or what made them get involved so closely with the brand? By evaluating all these questions, a company can design their promotional campaigns and other marketing strategies in such a way which will help them in enhancing their customer base and visibility.
It would be an arduous job to describe what are the prime job roles of a data scientist within a few words. Apart from having a proficient knowledge of technical, a data scientist should know how to create directives from the data, and how to present the data in a storytelling way. Nowadays, along with the marketing and production or service teams, these professionals are also the pillars of the company for its growth. Hence, the companies are commissioning more data scientists into its team so that it can go beyond its competitors.