Just as input from humans can add context to data and allow better decisions, it can also enable analytics to diversify, and make itself useful in a greater variety of applications.
This article highlights pain points in common industry operations related to solids handling and identifies opportunities for the application of SME-driven advanced analytics to generate collaborative, scalable solutions.
In the second part of this two-part article, Lisa J. Graham, Seeq Corp., provides use cases of machine learning in action in the oil and gas industry.
How advanced analytics is being used to drive desired business outcomes in pharma.
Machine learning (ML) is a powerful tool, but it requires extensive domain expertise for successful deployment, as Lisa J. Graham, Seeq Corp., explains in the first part of a two-part article.
The questions you ask to uncover your problems should inform your reliance on the cloud.
Advanced analytics and modeling can be used to predict downstream failures, allowing for corrective action before batches are lost.
To create a dataset separate from its context is to create a disconnected perspective representing a static view of a constantly changing process. A smarter approach is ensuring the freedom to explore all data from all sources.
Refineries must mitigate risk, anticipate maintenance, optimize operations and minimize operational expenses. To achieve these goals, plant personnel rely heavily on data to drive decisions.
The aluminium industry continues to make significant strides throughout its supply chain to incorporate emerging digital technologies – including those under the Industry 4.0 and Industrial Internet of Things (IIoT) umbrellas – to increase its competitiveness and to improve its operational excellence.