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Nissan Connected Vehicle/Quality Management Data Lake

We’ve just published our most recent case study! This one gives an in-depth look at how Nissan Motor Company Ltd (Nissan), is now able to store huge volumes of data and deploy a variety of data cross-functionally.

Nissan is a Japanese multinational automobile manufacturer headquartered in Nishi-ku, Yokohama. The company sells its cars under the Nissan, Infiniti, and Datsun brands. The company has embarked on a Big Data journey to help solve the business challenges presented by the growth and diversity of data. Nissan turned to Hadoop as the solution to its Big Data problem. After a thorough and competitive evaluation, Hortonworks Data Platform was selected to power its data lake infrastructure.

The connected car is a vision that many automobile manufacturers are aggressively pursuing. HDP has enabled Nissan to collect data across the whole business, specifically data related to driving and vehicle quality. Nissan can now use big data applications that require cross-functional data analysis, such as analyzing the battery usage in electric vehicles and quality management to ensure users have a smooth driving experience. Previously, the company lacked the infrastructure necessary to store and analyze all this data on a long-term data. As part of Nissan’s efforts to fully implement HDP, it is in the process of constructing a data warehouse. The deployment has introduced a data lake capable of storing all types of company data. The internal demand for and deployment of data continues to increase significantly.

Read the full story of Nissan’s use case, now!





The Architectural Journey to our Modern Data Applications – Ford DSC (Data Supply Chain)

Ford embarked on a journey to transform how they use data. The journey included not only the architecture & tools required but an organizational change to support the plan. Adopting Hadoop was only the first of many changes in a multi-year plan to transform the enterprise and vision of using data. This journey to date lead Ford to the term "Data Supply Chain" which enables a standardized process that includes ingestion, discovery analytics / self service, and implementation. This process enables Ford to make discoveries across any data (manufacturing, finance, customer, etc.,) which were previously located in disparate systems with limited or no ability to combine effectively. This session will cover align the technical and organization changes including the following:

  • Best practice – adoption (COE)
  • Organizational changes to incorporate
  • Original Opportunity
  • Reality
  • How we adjusted
  • What we learned




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