DECEMBER 2019CIOAPPLICATIONSEUROPE.COM8IN MYOPINIONn the past eight years, new data technologies have enabled organisations around the globe to store vast amounts of business data. Every day, humankind creates 2.5 quintillion (10^18) bytes of data, and this rate will only continue to accelerate. Ninety per cent of all data that currently exists was created in the last 24 months. Organisations have gained access to platforms that allow them to utilize this vast amount of data for analytics, insight generation, and the development of new digital solutions. This renders an opportunity to affect existing markets and change business models. Sensor technology and IoT devices serve as an interface between real-world applications, data generation, and new smart digital solutions. These are rapidly growing, with global IoT markets currently doubling in volume every four years.The increasing connectivity of the real and digital world, coupled with the new ability to run advanced analytics on big data have a significant impact on the development, application, and utilisation of new analytical methods and approaches such as artificial intelligence and deep neural networks. This change has enabled the scientific method to be employed for business decision making on a broad scale for the first time, thereby creating a significant opportunity for innovation and disruption across industries.Such technological and analytical transformations, however, require organizations to adapt, innovate, and embrace change in order to build crucial IP, secure business opportunities, disrupt markets, and develop competitive advantage. As organisations looks forward to tackle these new challenges, new structures for business analytics and digital transformation have emerged that demands new roles, responsibilities and processes. Three key, complementary analytics functions together satisfy an organisation's need for data analytics and digital innovation: the traditional business analytics approach (I), advanced analytics capabilities (II), and quant-level data science (III).Traditional business analytics capabilities (I) continue to gain relevance in the digital age. While data continues to grow and reporting gains complexity, smart investments into talent and data infrastructure are required to keep up with new digital developments.New advanced analytics capabilities (II) and centers of excellence complement the traditional approach with more complex insight generation and ad hoc analysis to leverage information hidden deep within this data. High-quality scientific talent with strong academic backgrounds from STEM fields such as theoretical physics and applied mathematics are able to create significant business value in quant-level data science teams (III). Their well-established academic analytical skill sets have been sharpened by exposure to the rigorous scientific method and experience in advanced mathematics, probability theory, advanced statistics, statistical inference, coding, computer science, and development of scientific models. For the first time, this new type of talent is enabled by the availability of rich data to contribute to large-scale business decision making.These quant-level data science teams develop new digital solutions such as AI-driven recommendation systems for high-impact business cases, which can be of significant strategic value. DR. PHILIPP DIESINGER, HEAD OF GLOBAL DATA SCIENCE, BOEHRINGER INGELHEIMI Business Analytics in the Age of IoT, Big Data, and Artificial Intelligence
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