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The high-tech sector tends to lag behind when it comes to gender parity and diversity. But diverse companies perform better and increase employee satisfaction.

As process manufacturers catch the data science wave, they face an early decision of whether to upskill or onboard. There are pros and cons to each strategy.

Advanced-analytics tools make it possible for engineers and other SMEs to greatly increase productivity and overall plant reliability.

Success stories show how advanced analytics software is used to create insights and improve process efficiency. See three use case examples.

Hybrid data architectures empower process manufacturers to more quickly realize the business benefits from their cloud and IIoT investments.

In a difficult hiring environment, investing in staff and fostering employee-focused cultures are essential.   

Moving from reactive to predictive maintenance requires the right tools for use by subject matter experts.

You’ve escaped pilot purgatory, cleared organizational obstacles, and are now in position to implement an advanced analytics system that will significantly improve manufacturing performance.

Advanced industrial data analytics has a prominent role to pay in process control and automation. The success of process control and automation efforts depends on the skilled design and automation of process behavior understanding. Advanced analytics applications enable the integration of the process understanding with process - and equipment - related relationships, which can be gleaned from historical process data by subject matter experts (SMEs) using advanced analytics.

Batch chemical processes present unique data aggregation, visualization and analytics challenges that may exceed the capabilities of traditional engineering toolsets. For a start, a chronological time stamp of data won’t suffice.