How data science is disrupting every business

The era of data science akadata driven analytics, innovation and decision making has arrived and is disrupting businesses across all sectors.

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The era of data driven analytics, innovation and decision making has arrived and is disrupting businesses across all sectors. The convergence of high volume data, sophisticated algorithms and vast computational power and storage has led to the greatest technological flux across industries.


A McKinsey Global Institute analysis reported that six disruptive data driven models and capabilities positioned certain industries ahead of others.

Among them, data driven discovery is the key archetype of disruption for the life sciences, pharma, material sciences and technology domains; Orthogonal data is the major cause of disruption in the insurance, healthcare and human resources domains; Radical personalization based disruptions are key for retail, media and education domains; Massive data integration capabilities are responsible for wide-ranging disruptions in the banking and public sectors; Hyperscale, real-time matching based disruptions are significant in transportation and logistics and automotives; Enhanced decision making is the key disruptive element in Smart Cities, healthcare and insurance industries.

Here are some of the most powerful examples of the data driven disruptions that are bringing fascinating changes to the industrial landscape:



 Data propelled discoveries are the key disruptors in the drug discovery space. Overcoming numerous challenges in data availability, data privacy, data diversity and data quality, the Healthcare and Pharma industries are now harnessing the benefits of data led discoveries. Atomwise, San Francisco, holds the first patent for a deep learning technology directed towards structure-based small molecule drug discovery.

It uses a combination of vast amounts of data and a convolutional neural network for drug discovery. Its AtomNet platform can screen 16 billion chemical compounds for potential hits in less than two days, expediting a process that would normally take months or years. One of their chief accomplishments is that their AI algorithms can identify potential drugs that target hard-to-reach parts of the body such as the central nervous system, with a hit rate that is 10,000 times better than wet-lab experiments.


Material Sciences

 Data driven approaches are also increasingly disrupting the Material Sciences industry. These are instrumental in accelerating the timelines for R&D by saving resources, as compared to earlier time-consuming and labour intensive practices.

The US-based startup - Kebotix, developed a self-driving laboratory solution for new materials exploration. They leveraged on their big data management, data science based decision making, robotics and an efficient interface to streamline research progression. Canadian startup Matelligence provides data driven tools for materials discovery, successfully lowering the number of experiments and speeding screening.



 The influx of new forms of data (uncorrelated or orthogonal data) has played a major disruptive role in the Insurance and Healthcare industries. In the Insurance domain, using a wider variety of information helps in better understanding and managing individual risks. Examples of orthogonal data include behaviour data from sensors obtained through opt-in customer engagement programs or telematics data obtained from embedded sensors in cars or smartphones. These are analysed for assessing driving behaviours, obtaining risk predictions, preventing subsequent losses, rewarding low risk drivers with discounts. Bajaj Allianz Life is applying predictive analytics across the customer lifecycle using several types and sources of data. They use upsell propensity models and recommendation engines to help in selling products as per the life goals of the customers. Their data analytics based fraud identification helps in minimizing losses. They have also built a robust forecasting competency that helps with the planning and targets.



 The disruptive effects of data enabled personalization are evident in the Retail industry. The retail space is undergoing a vast transformation as a result of radical personalization and is sailing ahead with positive customer reviews. Shoppers Stop, the leading fashion retailer in India, offers one of the best personalized and engaging in-store experiences for customers. They plan to use the anonymized aggregate data  of their 4.6 million First Citizen loyalty program members to provide personalized in-store promotions.

They have piloted the Connected Mobile Experience (CMX) capabilities along with Cisco Wireless Solution for improved, personalized experiences for shoppers. Myntra, a fashion e-commerce company, is disrupting the old fashion industry practices by applying machine learning to their data that comprises variables like location, weather patterns and what other consumers with similar parameters are buying.

During the COVID pandemic, they utilized the Microsoft Azure platform to derive actionable insights on changed consumer preferences and prioritized their products accordingly. They introduced the ‘Work from Home Edit’ on their app and focused on categories like masks, comfort wear and loungewear to cater to the specific needs of the vast population under lockdown.


These were some of the most exciting and trending cases that highlighted the disruptive role of data science across different businesses. As more organizations begin to walk on the data lit path, the supply of data scientists is one of the key areas that needs immediate attention.

It is to the great advantage of students and professionals, not only in the information technology space, but also in other domains such as Healthcare, Pharma, HR, retail, finance, to attain training in data science and benefit from the big opportunities that it has to offer in building a world-class career.

  • Surobhi Lahiri , Principal Consultant, TalentSprint
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