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Big data analytics driving predictive car maintenance at BMW

6/05/2015 by


Big data analytics are the key to automated predictive car maintenance, according to BMW.

Dirk Ruger, head of business after-sale analytics and digital processes at BMW, listed big data analytics as a vital element of future customer engagement while speaking at EMC World.

"We now use big data and predictive analytics as we wanted to learn more about what our customers like and what they expect from us for the future. The processing of this data lets us manage our business more accurately," he said.

"The primary goal at the moment is predictive maintenance, being able to detect defects at the earliest stage. We have to find the right correlation patterns for all our forward memories and incoming data to predict upcoming malfunctions and their consequences."

Ruger listed data security and a lack of synergy between IT and car development teams as hurdles hampering this goal.

"We have some challenges to do this. Of course we have to handle data while considering and protecting our customers' privacy," he said.

"We have data from the cars that could be sensitive so this is important to us. But more important and the bigger challenge is syncing development cycles, getting our car and IT development teams in sync.

"Cars take years to develop but IT can now come with monthly, or even daily, development cycles. This means that integrating digital development from IT into cars is tough."

Ruger explained that BMW uses the Pivotal big data analytics platform to help overcome these challenges.

"We use the Pivotal components from IT in our back end. This means that when we implement analytics algorithms into our cars we can enrich the data when it gets to our back-end systems," he said.

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