This article is the fifth and last in an editorial series with a goal to provide strategic direction for enterprise thought leaders in the manufacturing sector for ways of leveraging the big data technology stack in support of analytics proficiencies designed to work more independently and effectively in today’s climate of striving to increase the value of corporate data assets.
Adopting Big Data for Manufacturing
April 22, 2015 by Leave a Comment
This article is the fourth in an editorial series with a goal to provide strategic direction for enterprise thought leaders in the manufacturing sector for ways of leveraging the big data technology stack in support of analytics proficiencies designed to work more independently and effectively in today’s climate of striving to increase the value of corporate data assets.
Big Data Technology for Manufacturing
April 16, 2015 by Leave a Comment
This article is the third in an editorial series with a goal to provide strategic direction for enterprise thought leaders in the manufacturing sector for ways of leveraging the big data technology stack in support of analytics proficiencies designed to work more independently and effectively in today’s climate of striving to increase the value of corporate data assets.
Common Big Data Pain Points for Manufacturers
April 14, 2015 by Leave a Comment
This article is the second in an editorial series with a goal to provide strategic direction for enterprise thought leaders in the manufacturing sector for ways of leveraging the big data technology stack in support of analytics proficiencies designed to work more independently and effectively in today’s climate of striving to increase the value of corporate data assets.
insideAI News Guide to Manufacturing
April 7, 2015 by Leave a Comment
In this new insideAI News Guide to Manufacturing, the goal is to provide enterprise thought leaders in the manufacturing sector ways to obtain greater value from their company’s data assets through use of big data technology.
Saffron Launches SaffronStreamline™, a Cognitive Analytics Solution for Manufacturers
March 30, 2015 by Leave a Comment
Saffron, a cognitive computing platform designed for mission-critical operational intelligence, announced the availability of SaffronStreamline, a product lifecycle intelligence solution that provides end-to-end knowledge about issues and defects in a product’s lifecycle, helping manufacturers shorten time to market and manage potential risks.
Enterprise Grade Lustre in the Clouds
October 21, 2014 by Leave a Comment
With the release of Intel® Cloud Edition for Lustre software in collaboration with key cloud infrastructure providers like Amazon Web Services (AWS), commercial customers have an ideal opportunity to employ a production-ready version of Lustre—optimized for business HPDA—in a pay-as-you-go cloud environment.
New Survey from GE and Accenture Finds Growing Urgency for Big Data Analytics
October 11, 2014 by Leave a Comment
A new global study, “Industrial Internet Insights for 2015,” from GE (NYSE: GE) and Accenture (NYSE:ACN) reveals there is a growing urgency for organizations to embrace big data analytics to advance their Industrial Internet strategy.
Dirk Slama Keynote on The Internet of Things
October 1, 2014 by Leave a Comment
“The vision for the Internet of Things is very powerful – a world in which assets, devices, machines, and cloud-based applications seamlessly interoperate, enabling new business models and services; with big data analytics as a foundation to support intelligent decision making in this connected world. As with every vision, the question is how to make it happen. This presentation provides key success factors for IoT, as well as a detailed overview of concrete IoT uses cases in the areas of automotive and transport, manufacturing and supply chain, as well as energy. Finally, a framework for IoT implementation is presented, which helps making your IoT projects a success.”
insideAI News Guide to Big Data Solutions in the Cloud
September 23, 2014 by Leave a Comment
For a long time, the industry’s biggest technical challenge was squeezing as many compute cycles as possible out of silicon chips so they could get on with solving the really important, and often gigantic problems in science and engineering faster than was ever thought possible. Now, by clustering computers to work together on problems, scientists are free to consider even larger and more complex real-world problems to compute, and data to analyze.
