Disruptive Business Applications of Data Science

Data is the new Oil. This has been the discussion for the last couple of years after the story published by Economist in 2017, ‘The world’s most valuable resource is no longer oil, but data’. Around 1.7 megabytes of data is generated every second for nearly every human on the planet, and thus there is a need to process and analyse this massive volume of data. Hence you would see many applications of Data Science in your lives daily.

Let us understand what you mean by Data Science.



Data Science, at its heart, is extracting insights from data, which is growing at a tremendous pace. According to an HBR article, ‘Data Scientist’ is the most sought-after job in the 21st century. With this in mind, there are three areas one needs to get a good hold on for being a good data scientist:

Let us look at some of the popular applications of Data Science:

  1. Recommendation System

Amazon, the largest e-commerce chain, uses data to decide which products it should recommend to customers. It uses data science algorithms based on factors, such as historical purchases of a customer and other users who bought the same products or gave similar reviews. According to McKinsey, 35 percent of Amazon’s consumer purchases were due to the company’s recommendation system.

  1. Pricing Analytics

Uber operates on a dynamic pricing model based on data related to the start and end of the trip, location, time of the day, trip duration, events, holidays, demand and supply of drivers, etc. This dynamic pricing is decided using complex machine learning algorithms.

  1. Consumer Insights

Due to the pandemic, the eCommerce industry has witnessed tremendous growth, driving businesses towards an omnichannel strategy to offer a seamless consumer experience. For instance, consumers can buy online and return offline and vice versa. With the rise in online shopping, a massive volume of cookie data is generated. Additionally, social media data (Facebook, Twitter, Instagram) helps understand consumer sentiments. By marrying these datasets with customer historical purchases and demographics, businesses can derive Consumer Insights.

These insights enable companies to define their target segments and decide their marketing strategies. One of the most important areas where this is extremely useful is New Product Development, where companies are trying to see new opportunities for products that are not available for their brand. These insights are also critical for driving engagement, and optimising customer journeys and customer retention.

  1. Effective forecasting

During the pandemic, many businesses witnessed fluctuations in their predicted demand forecasts. While for some companies, the demand suddenly dropped, which led to colossal inventory costs, whereas, for others, demand spiked, leading to stockouts. This drove businesses towards the adoption of data science for forecasting.

  1. Fraud Detection

During 2015-2020, credit card fraud witnessed an increase of 161.7%, which led banks to adopt stringent measures to stop these frauds. One such measure is banks using location data in addition to customer profiles to detect fraudulent transactions. For instance, if a user has done a transaction in Delhi and within 2 hours the transaction is reflected from Sydney, the system flags a transaction under the fraud category and takes the next steps based on the predefined system using data science.

  1. Location Analytics

Nowadays, companies are using the location data of mobile users’ for deciding the location of new stores. By merging location data with consumer demographics, companies can understand the best location to open a new store.

~ Kapil Mahajan, Data Science Leader


U.S. Bureau of Labor Statistics predicts about 11.5 million data science jobs will be created by 2026. Upskill yourself in this ever-growing domain with our data science programmes in collaboration with renowned universities.

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