'As Long As The Data Is Used To Service The Customer, Deep Analytics Is A Good Thing'

Interview with Douglas Daly, Senior Manager of Data Science at Capital One


Douglas Daly is a Senior Manager of Data Science at Capital One. During his time at the bank, he invented a system of customer behavior analytics to track business service performance which led to a dramatic reduction in failure detection time from hours to a few minutes. For this effort, he was awarded a Business Technology Innovation award in December 2014.

Prior to working at Capital One, Douglas was an engineer and program manager specializing in signal processing and system modeling and simulation. He has an MS in Electrical Engineering from UW, an MS in Applied Statistics from RIT, and an MBA from UCLA.

We spoke to him at the Customer Analytics Innovation Summit, which took place on June 16-17. 

How did you get started in customer analytics?

I joined Capital One to leverage my analytics expertise to business problems. When I learned our leadership wanted to measure business activity in real-time, I proposed a way to leverage sudden changes in customer behavior to detect when our infrastructure isn’t serving its customers. I then built the thing myself!

Are there any particular developments that will make it more challenging for analysts to derive insight from data?

The toughest part is getting the data sources together with the right context. Anybody can run a great machine learning tool, only the expert can turn correlations into actionable stories and insights.

Many people think deep customer analytics is unethical, should companies be allowed to know so much about a customer's habits and browsing journey. Where do you think the boundaries are?

As long as the data is used to service the customer, deep analytics is a good thing. However, great care must be taken to ensure the insights are secured and contacts with the customer do not result in embarrassment or worse.

Customer Analytics is no longer a nice to have, but a necessity. What do you see as being a game changer for customer analytics over the next year? Where can you see the technology going?

Machine learning tools are already commoditized; over the next year, most people will realize it. Then will come a golden age for analytics where we will show our value by bridging the gap from correlations to stories.

You can hear more from leading analytics innovators in the banking industry at the Big Data & Analytics for Banking Summit, taking place in Melbourne this July 12-13.


Image courtesy of Northfoto/Shutterstock.com

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