In this week’s roundup, discover 8 ways businesses and industries are using “big” data and learn how mathematics is solving supply-chain disruption. Plus, gain an understanding of what impact machine learning is having on streamlining data center tasks and what the power of democracy means for feature selection. And finally, understand what role advanced analytics is playing in process safety.
8 big data use cases for businesses and industry examples
By Ronald Schmelzer, contributing writer for TechTarget.com
Data is all around us and multiplying rapidly. In fact, data seems to be one of the only resources that can grow without limit, as long as we have enough places to store it and the computing power to handle it. Yet, big data is more than the size of data. Data volume is just one of the “Vs” of big data. We also need to deal with the fact that data can come in many varieties, change at different velocities and have different levels of quality or data veracity.
Solving Supply-Chain Disruption Comes Down to Continuous Intelligence
By Ed Rothberg, contributing writer for SupplyChainBrain.com
Logistical chaos is not a new phenomenon in the supply-chain world, but the scale of upheaval caused by COVID-19 is something that stakeholders have never seen before: 94% of Fortune 1000 companies experienced disruptions from the pandemic, according to Accenture.
Streamlining Data Center Tasks with Machine Learning
By Jake Blough, a contributing writer for DataCenterFrontier.com
Digital transformation in IT departments enables businesses to take advantage of artificial intelligence (AI) and machine learning (ML) to streamline tasks and improve operations in the data center. The key to understanding the distinction between AI and ML is to view automation as an umbrella with artificial intelligence, machine learning and deep learning as subsets of automation. AI is broadly defined as a technique that mimics human behavior. Machine learning uses data and algorithms to understand and improve from experience over time. Deep learning is a subset of machine learning where software trains itself to speak, recognize images and more.
The power of democracy in Feature Selection
By Ouaguenouni Mohamed, contributing writer for TowardsDataScience.com
According to Wikipedia, feature selection, also known as variable selection, attribute selection, or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction.
Advance Analytics for Process Safety
By Michael Chang, contributing writer for Chemengonline.com
Simply by the nature of some of the chemicals used and the associated complex processes involved, chemical plants can be hazardous. With the potential exposure to toxic substances, fires and explosions, incidents can range from minor to extremely serious. They can also result in significant plant damage, injuries, and sometimes even loss of life. Furthermore, major chemical manufacturing incidents can also have a catastrophic impact on the surrounding community and environment.
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