These days business houses are stockpiling a massive amount of data which is often considered a precious asset for companies. It is surprising to know that more than 90% of the available data has been generated in the last two years.
In earlier days, due to 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 data analytics has completely changed the companies’ vision, and by using comprehensive business analytics, the companies can make the right decisions that will help them surpass their contenders.
Hence, the organizations are accentuating on data analysis extracted from raw data by specialized computer programs and are cultivating their employees regarding how to accustom and publicize the information they are getting from these organized data.
Since the importance of data analytics is burgeoning day by day, the companies are appointing sagacious professionals who will provide the company with more comprehensive insights into the structured data. A data scientist will be responsible for designing and implementing various processes and layouts for the complex and large-scale datasets used for modelling, data mining, and different research purposes.
What are the core responsibilities of a data scientist? Why have they become 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 that will help the organization make the right decisions to increase it is business and growth. A data scientist usually dives into the pool of data. With his expertise and knowledge, he used to find all the imperative information and ignore other irrelevant data to 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 facile and more superficial. Even a non-specialist 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. Instead, he should present the data in a storytelling way that consists of simple slides and visuals instead of numbers.
Visualizing and communicating data are equally important, especially for the budding companies making data-driven decisions for the first time or the companies where these professionals are viewed as people who help others make data-oriented decisions. In this way, everyone in a company should understand which portions or departments need further improvement.
Help in the promotions and other marketing strategies
A data scientist will also work coherently with the marketing team and help the company conduct fruitful campaigns and promotions, which will certainly enhance sales and profit.
If a mobile company has an idea who their most engaged customers are, then a data scientist will help the company 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 to help them enhance its customer base and visibility.
It would be an arduous job to describe 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 present the data in a storytelling way.
Nowadays, along with the marketing and production or service teams, these professionals are also the company’s pillars for its growth. Hence, the companies are commissioning more data scientists into their crew to go beyond their competitors.