The economy has become increasingly more digitized. Digital giants like Microsoft, Google, and Apple have changed the way the economy looks compared to 20 years ago. This has made access to data and the ability to harness it as a resource a key competitive advantage that is deeply valued by companies. Competing in this landscape requires adapting to new technologies, rethinking your customers’ needs and expectations, and utilizing the power of data and artificial intelligence.
During this webinar, WSB’s Daniel Bauer explores how digital transformation and data analytics have changed the business world today.
What do we mean by “digital?”
The term digital encompasses digital technology, data, and analytics. The relationship between the three is cyclical. For example, data can be used by companies to do things like create new apps or software that can then be used to harness additional data from customers to learn more about them and give companies a more detailed understanding of their customers. The digital transformation is the broad consequence of these mutually connected, rapid advances in data availability and technology.
What can this “digital” combination deliver for companies?
Digital technology is pervasive. Twenty years ago a company’s main goal was to have a successful product, but today it’s also about providing a positive digital experience. For example, Starbucks has become very successful by creating a customer experience to order, customize, and pay via an app in just minutes. These new services have allowed for automation and innovation to perform tasks that might have taken longer and been less efficient without the use of technology.
What is artificial intelligence and machine learning?
Over time, computers have become extremely good at mimicking tasks that were historically seen as requiring human intelligence. This kind of simulation is referred to as artificial intelligence (AI). The objective of AI is to enable computers to make decisions, solve problems, and understand human communication. Machine learning is one branch of AI that focuses on methods of creating computer algorithms that automatically improve through exposure to data and experience. Because of machine learning, computers can increasingly complete a range of complex tasks that used to be nearly impossible to program.
Daniel Bauer is an associate professor of risk and insurance and the Hickman-Larson Chair in Actuarial Science in the Department of Risk and Insurance at the Wisconsin School of Business. Bauer specializes in the development of models for the valuation and risk management of insurance products and insurance-linked securities. His research is published in leading journals in actuarial science, economics, finance, management, and statistics, and he serves on the editorial boards of several journals in actuarial science and risk management.