Advanced Analytics, Machine Learning, And Situational Awareness For Manufacturing Data
By Janice Abel
Advanced analytics is a key innovation for digital transformation. Most companies want to find solutions to empower their employees to quickly find insights, rather than waste a lot of time searching for, filtering, and cleansing the available data. They want smarter, easier-to-use tools and solutions that can help their employees – including subject matter experts (SMEs), engineers, and managers - find, under-stand, and take actions to solve their day-to-day problems.
While many industrial companies are rolling out pilots and enterprise analytics projects, it is important for users to understand the features and capabilities of the analytics offerings. The analytics technology suppliers are improving and enhancing capabilities and taking advantage of newer technologies; open source, time-series databases; Big Data and machine learning; better connectivity; and new platforms to improve ease of use. But, as a user, how do you know what features and capabilities are important? Can you connect to the data sources you need easily? Can you easily repair or cleanse data that is bad? What algorithms do you need? What questions should you ask? Perhaps most importantly, does the solution apply to the specific needs of process manufacturing industries?
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