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Boehringer Ingelheim
Business Analytics in the Age of IoT, Big Data, and Artificial Intelligence
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. Such capabilities deliver decision support systems that operate with mathematical precision, something that was impossible a few years ago.
Ideally, these types of analytical function complement each other and operate on shared data infrastructure. Each function, however, presents its own unique challenges such as utilizing data in different ways, requiring different types of talent, and leveraging different methods or analytical tools.
Crucial for all functions is a need for strong collaboration between relevant business stakeholders to enable knowledge transfer in both directions. It is the imperative data scientists who can develop an in-depth understanding of relevant business areas. While at the same time, business stakeholders must discover what AI systems can and cannot solve.
A healthy data landscape will benefit all three types of analytical functions but in different ways. While data serves as a repository for information and insight generation, as in the case of (I) and (II), it directly drives the performance of innovative digital solutions and AI-systems in (III). The more the data available and higher the quality of that data, the more accurate will be the predictive power of a recommender system that utilises it. Internal development of such smart solutions is becoming an increasing necessity to secure business opportunities, boost competitiveness, and build IP. New legislative frameworks and regulations can render opportunities to adopt competitive positions as well in the future.
Within the blink of an eye, AI-solutions can utilise data that would have taken human a decade to merely read through or understand. In such cases where data is readily available, it is important to understand through practical experience what AI systems can and cannot do. As Andrew Ng said in 2016: “Anything that a typical human can do with one second of thought can probably be automated with AI now or soon.”
Pre-existing organisational structures, processes, talent, or other factors, including strategic consideration, might render more feasible approaches to corporate analytics than other methods. However, each organisation will have different demands and ultimately will have to find the solution that best satisfies their specific requirements.
