Resources

The ebook, How Industrial Data is Transforming Manufacturing, sources insights on how manufacturers are using Big Data from 100 of the leaders who attended the most recent Industrial Data Summit. In this rich source of learning, key topics include:

• Barriers to getting started on using data – and how to break through them
• Understanding the AI trends that matter NOW
• How data can revolutionise quality control
• Increasing end-to-end visibility through supply chain analytics

The Manufacturer’s Guide to Big Data collects together our 10 most popular articles on Big Data and manufacturing – featuring end-user case studies from the factory floor alongside overviews and explainers. Many of the issues covered in this eBook will be addressed in our interactive discussion groups at Industrial Data Summit.

• Key topics: Big Data collection, management, and analysis.
• Use-cases: read insights from Airbus, BAE, Accolade Wines, Enter Fashion, Aerospace Technology Institute, Rakusen.
• Delve deeper: see which articles other manufacturers are reading to get a feel for the perspective of your peers.

Get the inside track on UK manufacturing’s experience of Big Data

We have spent six weeks researching what UK manufacturers are thinking about the challenge and opportunity of Big Data as part of the development of Industrial Data Summit. Take a look at some of the insights shared by the biggest names in British industry.

Download this Cheat Sheet and find out:

• How are Nissan, GSK, Smithfield Foods, BAE Systems Maritime, and other leading manufacturers implementing Big Data?
• What kind of ROI are they targeting – and how do they calculate it?
• What does a data-driven predictive maintenance model look like?

Download this guide and find out answers to these questions:

• What are the various methods for collecting, storing and analyzing data and which would be the most appropriate for my company?
• What are the pros and cons of implementing a Big Data strategy and what are the commonest reason for failure?
• What sort of savings and operational improvements can a data-driven predictive maintenance model produce?
• Which Big Data analytics tools should I be using to gain insight into my data?
• How important is it to have a Big Data security policy in place?