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Big data analytics is the often complex process of examining large and varied data sets or big data to uncover information including hidden patterns, unknown correlations, market trends and customer preferences that help organizations to make informed business decisions. It is a form of advanced analytics, involving complex applications with elements such as predictive models, statistical algorithms and what-if analysis powered by high-performance analytics systems.

Driven by specialized analytics systems and software, and also high-powered computing systems, big data analytics offers various business benefits, including new revenue opportunities, more effective marketing, better customer service, improved operational efficiency and competitive advantages over competitors.

The future of manufacturing is in many ways linked to big data. As a matter of fact, Accenture suggests that the industrial Internet of Things (IoT) could add $14.2 trillion to the global economy by 2020. Manufacturers will be able to use a mix of production data from IoT sensors and analytics, along with consumer experience data from sensors and social sources, to create a proactive approach to manufacturing in the near future.



  • Thanks to big data analysis, BMW’s solution (probably integrated with their vehicle design and modelling software) spotted weaknesses and error patterns in the prototypes and in cars already in use. It enabled engineers to remove uncovered vulnerabilities before the prototypes actually went into production and helped reduce recalls of cars already in use.

  • Rolls-Royce uses big data extensively. And one of their most interesting manufacturing big data experiences is connected with modelling new aircraft engines.

  • In 2017, implementing big data and IoT to the processes, Intel predicted $100 million saving.


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