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Diego Ferreira

Driven Analytics Uses DataStax Enterprise to Solve Automotive Problems

By Diego FerreiraApril 28, 2017

This post is one in a series of quick-hit interviews with companies using DataStax Enterprise (DSE) for key parts of their business. In this interview, we talked with Jake Elliott, co-founder, CTO and VP Product Development at Driven Analytics.

DataStax: Hello Jake, thanks a lot for your time today. Can you tell us about Driven Analytics and your role with the company?

Driven Analytics: Of course, we’re glad to have the opportunity to get our story out there. I am one of the original co-founders and currently serve as CTO and VP Product Development. My teams focus on developing and maintaining the technology that makes Driven Analytics what it is. At our heart though, we like to say we are an automotive company using technology to solve automotive problems, rather than a technology company solving technology problems in the automotive space. Our original founder and CEO first came up with the idea for Driven Analytics while owning and operating a small used car dealership in Oklahoma City. He had the hardest time purchasing clean used vehicles at an auction, usually finding some hidden mechanical problem when he got the vehicle back to his shop. He realized that having some data collection device on a vehicle from the time of original sale could allow a “CarFax on steroids” to be developed which could accurately predict mechanical issues based on how the car was driven. However, over the last two years, hundreds of car owner interactions and months spent sitting inside car dealerships, we’ve pivoted away from that original idea and towards what we now have as our flagship product, called Maintain™. Maintain™ is a customer retention platform specifically designed for automotive service centers and focuses on building a relationship between the car owner and the service provider through providing knowledge parity and transparency of information. By using our app, car owners know exactly what maintenance their vehicle needs, when it needs it, and how much it will cost at the service provider.

DataStax: What differentiates Driven Analytics from other mobile applications that offer real-time information to car owners?

Driven Analytics: We’ve taken a different spin on the “Connected Car” than most. The actual data from a connected vehicle is typically the main value proposition when you look at other products in the space. Where did I park? What does this trouble light mean? Did my teen driver speed on their way to school? These are all valuable bits of data in and of themselves, but they really rely on the customer to aggregate these data points and formulate some type of information on their own. Maintain™ focuses on using all the various bits of data that can be collected and facilitating a relationship between car owners and service providers. We take data, transform it into information and then take it one step further to actual knowledge.

DataStax: Did you use a different technology before you started using DataStax Enterprise (DSE)?

Driven Analytics: As the original architect of our solution, and having nearly eight years of experience at the time in SQL-based database development, our original implementation was very “small data” oriented. Only after making our first full-time hire, a PH. D candidate data scientist, did I really understand the benefits of big data platforms and the value that DSE could bring to any company, let alone one in our space.

DataStax: Why did you decide to use DSE? What kind of data is stored in DSE?

Driven Analytics: Being a start up, we had a severe resource constraint when it came to technical folks who could put time and effort into scaling hardware, responding to downtime incidents, etc. Implement a system like DataStax which added support on top of products like Cassandra database and Spark which focus on both ease of scalability and reliability made a lot of sense. We currently store all of our internally generated data from vehicle information and customer profiles to user statistics and telematics data.

DataStax: How would you sum up the benefits you’ve achieved with DSE?

Driven Analytics: With the aid of DSE, we’ve been able to focus our technology efforts on building better and more valuable products with our small team instead of addressing infrastructure issues on a daily basis.

DataStax: What caused you to use DSE over open source Apache Cassandra™?

Driven Analytics: The support, documentation, training materials, and additional tools all played a role in our decision.

DataStax: What features from the DSE stack are you currently using?

Driven Analytics: We are currently utilizing Cassandra DB and Spark to a lesser extent. Our main outcomes have been very high uptime and extremely performant systems.

DataStax: What advice would you give to other startups that are thinking about using Apache Cassandra and DSE for the first time in their solutions?

Driven Analytics: Most people getting into “big data” these days are still folks who were originally trained in the school of SQL. Twenty or thirty years ago, disk was expensive so we get things like Third Normal Form that drive data de-duplication. This obviously isn’t the case now. Disk is cheap! Our limitation now is processing power with respect to the amount of data that we are requiring our systems to deal with. Cassandra provides an awesome platform to address this but it requires a whole new way of thinking about database design. If one really wants to take advantage of a platform like Cassandra, they have to be open to having their paradigms shifted somewhat significantly.



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