VIDEO | American Family Insurance Chief Engineering & Data Officer: Data Is A Gift That Must Be Respected

(US and Canada) Brad Burke, Chief Engineering and Data Officer, American Family Insurance, speaks with Michael C. Fillios, Founder & CEO, IT Ally, about gathering and analyzing data for optimizing marketing decisions, data privacy, how SMBs grow with technology, and how the intersection of data engineering and software engineering advances data science.

Burke recalls being involved in marketing tech start-ups when the big data revolution was underway. He says the usage of analytics demanded leveling up to unlock masses of data. Machine learning and data science came in, followed by cloud migration.

Next, he states that respecting the consumer is fundamental. With the internet becoming more social and people posting unstructured content, they worked on various platforms to understand dos and don'ts with data, says Burke. Classifiers had to be put in place to understand millions of conversations happening in a day.

From a technology perspective, says Burke, it boils down to understanding how to scale a massive data pipeline and organize the records in real-time. He affirms that analytics is the primary use case for collecting and organizing information at a massive scale.

Speaking about data privacy, Burke mentions that the past processes of collecting information were not ideal. He maintains that data is the gift of a consumer and must be respected.

He highlights that it is the time for small and medium-sized businesses to shine with the available technologies. Cloud is amazingly transformative, he continues, but the company needs to be customer-centric and think of it holistically. He further notes that understanding the security posture and having a good security partner go a long way in the journey.

Burke emphasizes how data engineering becomes like software engineering with cloud capabilities. With the current technologies, one can be more iterative with data, and the developmental capabilities look more like software engineering. He recalls developing SAP software in the ‘90s when they could not afford to make a mistake with a $1 million warehouse.

He stresses the need to have data engineering and software engineering capabilities to activate data science. Burke believes that these domains must work in concert to create a product.