The Lazada Data Science team has paper lunches together every Friday. Usually, we discuss about new papers we read, new ideas and implementations we tried, etc. This past Friday, we invited some special guests from Aviva to discuss about our journeys in cultivating a data-driven culture and building data science into the organization.
Some can transition successfully from traditional actuarial statistics to customer-based data science—but most fail
One of the guests, who is in charge of technology, mentioned something interesting.
Aviva is very strong in what most would consider traditional aspects of data science involving risk, actuarial statistics, etc (it’s over 300 years old, and is the second oldest institution in England after the Bank of England—of course it’s good!).
However, as it tries to build new capabilities around understanding the customer better and customer analytics, Aviva found that not many of its data scientists could transition successfully.
Why? What distinguishes those who transition successfully from those who do not?
Being always on the lookout for features that distinguish a top performer from the rest, this piqued my interest. What was it that those people who transitioned successfully had/did, I asked.