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June 06, 2016
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Accelerating Connected Car Data Science & Machine Learning

According to Gartner Research, by 2020 the total number of connected cars will be nine times more than that of 2015. Additionally, 80% of all new vehicles will have data connectivity, 30% of connected-vehicles will have built-in, over-the-air software capabilities, and over one billion connected automotive subsystems will be shipped.

With the exponential growth of connected vehicles comes the need for Connected Data Platforms, data science, and machine learning algorithms. The rise of interconnectivity and the connected car has led to a complex system enabling safety through autonomy and efficiency through intelligence. The sum of this system generates more insights than its parts.

Managing this system and powering the future of data are modern automotive applications powered by Hortonworks Data Platform (HDP™) for data at rest and Hortonworks DataFlow (HDF™) for data in motion. Together, these form Hortonworks’ Connected Data Platforms, which are ideally suited to managing the Connected Vehicle Data Pipeline.

To analyze the vast amounts of data in real time, automakers leverage the Connected Data Platform. For informed decision-making and optimized selection of machine learning algorithms, automotive manufacturers need a robust and reliable infrastructure with automated data science to rapidly distinguish “signal versus noise” amidst billions of sensors.

Thus Hortonworks has partnered with DataRPM, a pioneer in Meta Machine Learning, to advance the Industrial Internet-of-Things (IIoT) and help Automotive manufacturers gain the valuable insights needed to achieve measurable business outcomes with greater speed and machine efficiency.

According to a Fortune 10 company, predictive maintenance can help automotive manufacturers achieve 10 times their original investment through a production increase of up to 25%, a maintenance cost reduction of up to 25%, and a breakdown elimination of up to 75%.

Additionally, long-term customer satisfaction is predicted to hinge less on GPS, WiFi, and other connected car perks. Instead, customers will be won and retained on an automaker’s ability to predict breakdowns, provide route recommendations based on fuel consumption, and other customizations based upon driving patterns and driver preferences.

According to McKinsey, modern automobiles have the computing power of 20 personal computers, feature over 100 million lines of programming code, and generate up to 25 gigabytes of data per hour. Automakers have spent billions of dollars in building these software capabilities and are turning to the Connected Data Platform to manage the issues around the speed and scale of this data.

The fundamental characteristic of Industry 4.0 is automation. The myriad sensors, actuators, machines, and the mammoth data generated by modern automobiles demand cognitive data science. This empowers automakers with actionable insights through analysis of sensor data and continuous identification of the right signals amidst the noise.

DataRPM delivers the only Cognitive Data Science platform that automates and operationalizes Machine Learning to rapidly solve Mission Critical Business Problems at scale both on premise or in the cloud. From continuous inline data ingestion to actionable insights, automakers can realize immediate benefits, significant cost reductions, and amplify predictive power.

Please visit our booth at TU-Automotive, where Hortonworks and DataRPM will be demoing use-cases that enable automakers take the next step in IIoT.

See you there!


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