What is Data science? And why is it among the most trending fields?

In the olden days In pre-modern times, businesses, to boost their sales, used to dispatch their salesmen to areas where “they thought” the probability of finding a customer was high. There was always the question of “they thought”. The salesmen were not backed up by data and information as to which house, or which street should be a high priority. The salesman, having undergone no filtration and sorting out of potential buyers, set out, going from one house to another, in hopes of selling his product. This way the salesman had to encounter many such people who had no interest in the product, some who had little interest and others who had interest but could not afford to buy owing to their financial strains. Had the salesman known before time that some of his targeted people had zero interest in his product, he would have never reached out to those people, thus, saving time and energy. He could have utilised this time and energy later on, in going to an altogether different place to sell his product. But, unfortunately, in pre-modern times, there wasn’t any platform that could provide them with such accurate and precise information. Arrival of the Internet With the arrival of the internet and most importantly, the social media, big chunks of data made its way to company servers. People gave, and are still giving the social media companies important information, also called their digital footprints. As more and more people started using social media, bigger amount of data appeared. This data was a game-changer. Experts began to realize that this data could be used to achieve heights of marketing that were unimaginable hitherto. This data on users, would tell lots about them. It would tell a company what product is the user interested in, which brand does he prefer to wear, what time is he most probable to buy products, from obvious details to the very minutest online behaviour of the user. When you know what a user is searching on google, you can easily guess what product he might be interested in or looking for in a certain time frame, and this information, in turn would help you guess which ad would he most likely fall prey to. This data on users is what companies nowadays are looking for to enhance impact of their marketing advertisement campaigns. With the transition from traditional salesman-styled marketing to modern marketing approach, the role of salesmen has been reduced to zero, and its task has been taken over by social media and internet companies like Facebook, twitter, etc. which propose their data of users to the companies and allow them to display ads on their platforms. These ads that companies display, is not displayed randomly to any one on Facebook (for instance), it is rather displayed to only those who are more likely to be affected by the ad. This likeliness, again, is something that data of users can help determine, whether a person is a football lover or cricket lover depends upon his Facebook usage data. Let’s understand through an example As an example, let us imagine that 100 people are to be approached by a company for advertisement of a product. On the one hand we suppose the company uses the conventional method I-e dispatching salesmen to 100 random people in a town. On the other hand, the company undertakes a modern way and uses Facebook to display advertisement to 100 people. In the conventional method, we don’t know how many of the 100 people are likely to be interested in our product because we don’t have any data on them except their location. Therefore, there is an element of risk involved in this method. It might happen that all of the 100 people would be interested in the product, equally probable is the possibility that none of them shows interest in the product (and in this case, the expense of this advertisement campaign overweighs the output) Conversely, the modern approach reduces the element of risk to a bare minimum. Because Facebook has enormous amount of data and insights on every user, it would make sure that all the hundred out of hundred people selected for ads are those who are genuinely interested in the said product. And UP goes the chances of them ending up buying the product. This way the modern way of advertising, whose backbone is ‘data’, would naturally be the choice of the company. And nowadays, as companies are crossing Billion-dollar net worth figures, their marketing budgets have also plunged up to millions of dollars. That is why, with so much money at stake, no company would want to go with a model that is shrouded with risks. Role of Data science In all what has been discussed until now, one thing seems to be missing and that is: Where does Data Science fit into all this? Well, the most important role in the transition from traditional advertisement to modern advertisement is the role of Data scientists. How do the companies know that such and such product would entice so and so person? How do they group people into different categories with only their behavioral data available to them? This is all a blessing of data science. Data science, with the application of programming and statistics manipulate and polish the raw data into such a form which is makes decision making very easy and almost risk free. Example of a restaurant For example, let us suppose there is a fast-food restaurant. How would a data scientist, or, data science in general be of help to this restaurant? A data scientist would first check on company’s sales records and find out those places where these restaurants sales are higher than other places. He would visualise the data I-e make different graphs and charts that would show that in some specific places the restaurants business is growing while in others its plummeting. This data would compel the restaurant owners to reconsider their marketing approach