How data science is disrupting every business
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.
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.
