Using Big Data for Machine Learning Analytics in Manufacturing

whitePaper | September 1, 2022

Organizations often adopt a hybrid cloud strategy when deploying their first analytical solution to the cloud. The cloud combines sheer infinite scalability with consumption-based pricing. These are desirable attributes for analytical environments that require scalability and can be very expensive when scaled for maximum capacity over an extended period.

whitePaper | February 22, 2023

There are two reasons financial institutions are learning as much about their customers as possible by gathering—and processing—as much data as they can. Firstly, this information is invaluable for delivering more personalized products and services to increase revenues while making smarter decisions and improving operational efficiency. Better customer understanding can improve things like underwriting accuracy, for example, and many others.

whitePaper | March 3, 2023

In today’s world, data is king and has become a major source of competitive differentiation for businesses across all industries. The COVID-19 pandemic has further accelerated the use and importance of data as the world’s governments and businesses adapted.

whitePaper | April 2, 2023

In this white paper, we share how The Alan Turing Institute’s AI for science and government (ASG) programme has been using collaborative and multidisciplinary data science and AI to help tackle climate change.

whitePaper | September 23, 2022

For Intel’s complex factory environments, quickly identifying tools that are operating below their optimal capacity and finding the root cause is crucial for maximizing production output. With thousands of tools involved in production and each manufacturing tool running from one to 50 operations, there are tens of thousands of tools and operations to monitor, analyze and adjust. Knowing where to focus engineering efforts is a monumental challenge.

whitePaper | September 12, 2022

This whitepaper examines clinical trial data contribution policies and the data protection methodologies applied to protect patient privacy. Information published by 29 biopharma sponsors was collected across three data-sharing platforms, collated by sponsor size. Results showed that large sponsor contribution policies can provide helpful benchmarks for medium and smaller sponsors.