(US and Canada) Yingying Kang, Director of Data Science, Assurant, speaks with Jason Masker, Field Chief Technology Officer, Stratascale – an SHI Company, about the essentiality of deep learning and building models, using machine learning for running processes efficiently, and data science’s role in digital transformation.

Kang states that deep learning is a broad category and an important element of data science. It exposes quality data for people to use, transform or process to drive insights and build predictive models.

The massive amount of data acquired every moment requires multiple teams to process and make it visible and explainable, says Kang. She adds that machine learning, deep learning and artificial intelligence are technologies that connect data, shape insights, and bring out solutions. The goal, she says, is to derive value from data. 

Kang then emphasizes the crucial data quality aspect and the importance of an analytical framework to summarize the data-related information from a different dimension. This helps decision makers understand the business process and customers’ needs, she states. These insights must be converted into practices that result in better operations and elevated customer experience.

She further states that a business can develop sustainably with a good learning environment and a data-driven process. Although any machine learning algorithm can do it, the challenge lies in changing business processes and customer demands. This challenge can be overcome by incorporating a deep learning strategy because it builds a deep learning environment that tracks change and helps businesses make the right decisions.

Kang believes in putting optimum effort toward enhancing operational processes to ensure data scientists get clean data to deliver insights that build the business. She adds that the goal of operations is to have a connected data layer with good quality, governance, resilience, and usability.

She refers to AI and ML as “key drivers” of digital transformation and data science,

Kang affirms that using AI and machine learning technologies expedite the customer service experience by ensuring faster problem–solving. Citing repairing a broken phone as an example, she notes how it previously required longer processing time versus now, when customers are allowed to submit their problems through multiple channels, thus shortening the experience.

She urges organizations to ensure that their decision-makers have access to the correct information for the right decision-making. Secondly, optimum technology is required to help adjudicators drive businesses, she concludes.