Progressive is one of the largest providers of car insurance in the United States. The company uses data in all areas of the company, from quotes and claims, to analyzing driver behavior.
In video 1, Brian Durkin, Innovation Strategist, shares how Progressive chose Tableau over traditional tools, because it allows for rapid testing on large quantities of data stored in Excel, Microsoft SQL Server, Oracle, and Hortonworks Hadoop. With Tableau’s native data connectors, the data science team can bring all of this data into one view.
In video 2, Brian talks about Progressive’s Snapshot® program, which allows drivers to save money through safe driving behavior. The data science team uses Tableau to analyze Snapshot data along with external sources—helping Progressive understand how different conditions affect driving patterns.
Exploring big data in Hadoop
Tableau: How would you describe Progressive?
Brian Durkin, Innovation Strategist: We're a data-driven company. So all of our leaders, our senior leaders, and all our analysts, and everybody in the company, really, they want to use data to inform their decisions. And so we've been, traditionally, a very hands-on type of company where people are using the Desktop version.
Tableau: Who views Tableau dashboards at Progressive?
Brian: Tableau is used pretty much across the board within Progressive. Our customers are everybody from our individual internal analysts up through our senior leaders that use the reports, and they use Tableau itself in order to analyze the data. So it's pretty widespread across the company.
Tableau: How exactly do you work with data? And what types of sources of data do you have?
Brian: Our marketing department will bring in data, we'll compare that against data from our policy servicing department. We'll also look at claims, we'll look at quotes, we'll look at all these different areas to mash up data. We use Tableau to do a lot of that.
The technology used to store these things is also pretty diverse. So there's a lot of Excel, there's a lot of SQL Server, there's Oracle, a lot of DB2, and then all the new big data stuff. We store a lot of data in [Hortonworks] Hadoop. And Tableau, with all the connectors that it has, allows us to bring this data together and look at it in new different ways.
Tableau: What’s something Tableau makes possible or easier?
Brian: Sometimes when you're using Hadoop to do data exploration, your datasets can be so extremely large that you need new tools in order to really analyze that data effectively. So we use Tableau and do subset sampling to do data exploration. A lot of times, we'll use it to analyze the aggregate data. But it provides us a way to rapidly test data and look for new things.
With some of our traditional tools, it takes a little bit of work to set up your data analyses and figure out really what you're looking for there. Tableau allows us to rapidly iterate over that and do this rapid hypothesis testing to look for new things. And that's important when you're dealing with these really large datasets. You need to be able to change your views and the way you're analyzing it pretty quickly. Tableau allows us to do that.
Tableau: What’s the main value of using Tableau?
Brian: I think it's somewhat hard to quantify the savings or the value that Tableau brings. A lot of times there's things we couldn't even do previously, using our legacy tool set. Tableau allows us to do these things now. And so comparing what you couldn't do at all to what is possible now, it's huge. It's night and day.
Tableau: How has Tableau changed the way you analyze data?
Brian: Being able to have the technology to analyze data, to fiddle with the data, to experiment with it, to play with it and just look at it in different ways has been amazing. A lot of our traditional tools don't allow us to do this rapid experimentation and exploration. Tableau's a great tool that allows you to do that, just to see what you have and analyze it, and figure out what you can do with it.