Mining the data of one of the largest life insurance companies in the United States to identify meaningful trends is no small feat.
From charts, digital data and PDFs, to "alternative" data, such as recorded voice conversations between New York Life policy owners and the company's service team, data continues to grow exponentially while becoming more diversified.
New York Life's data engineering team has taken a two-pronged approach to tackling the challenges of organizing and extracting valuable insights from the company's data to better serve policy owners and manage risk.
First, the team has created a "data hub," a consolidated library of data assets, refined to an analyzable format. Second, the team has deployed cutting edge machine learning solutions that enable the company to easily access and analyze the data.
"By combining internal and external expertise, we can unlock insights hidden within our data, and make them more accessible to departments across the company"- Jeremy Bucchi, head of New York Life's data engineering team.
One example of a machine learning technology helping to turn these strategies into reality came from the start-up community. Working through New York Life Ventures, the company's corporate venture capital arm, the data team onboarded Trifacta, a San Francisco-based company that provides a platform for exploring and preparing data for analysis. New York Life Ventures also made a venture capital investment in the start-up. "By combining internal and external expertise, we can unlock insights hidden within our data, and make them more accessible to departments across the company," explains Jeremy Bucchi, head of New York Life's data engineering team. He adds, "By allowing our businesses to access and process data more easily, they can accelerate and improve decision-making when it comes to important business, risk management and policy owner-related challenges."