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Predictive Analytics in Manufacturing: A Winning Edge

Sisense

The modern manufacturing world is a delicate dance, filled with interconnected pieces that all need to work perfectly in order to produce the goods that keep the world running. In Moving Parts , we explore the unique data and analytics challenges manufacturing companies face every day. Big challenges, big rewards.

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What is predictive analytics? Transforming data into future insights

CIO Business Intelligence

Predictive analytics definition Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. As such it can help adopters find ways to save and earn money.

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IT leaders embrace the role of business change maker

CIO Business Intelligence

You have to truly understand how systems are used, how data is entered into systems, and how it’s manipulated in order to make decisions. When the manufacturing plant started the program, they had a multi-day retreat, which I personally attended. You can’t be an ivory tower architect,” he explains.

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How IBM Planning Analytics can help fix your supply chain

IBM Big Data Hub

The need to collaborate, share data and agree on definitions across organizational boundaries and systems. In a manufacturing, distribution or retail context, this is the supply plan. In a manufacturing, distribution or retail context, this is the supply plan. The next step is to start layering on constraints.

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IT leaders get creative to fill data science gaps

CIO Business Intelligence

For the past few years, IT leaders at a US financial services company have been struggling to hire data scientists to harness the increasing flood of incoming data that, if used properly, could improve customer experience and drive new products. It’s exponentially harder when it comes to data scientists.

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The most valuable AI use cases for business

IBM Big Data Hub

Promote cross- and up-selling Recommendation engines use consumer behavior data and AI algorithms to help discover data trends to be used in the development of more effective up-selling and cross-selling strategies, resulting in more useful add-on recommendations for customers during checkout for online retailers.

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AI in commerce: Essential use cases for B2B and B2C

IBM Big Data Hub

Poorly run implementations of traditional or generative AI in commerce—such as models trained on inadequate or inappropriate data—lead to bad experiences that alienate consumers and businesses. This includes trust in the data, the security, the brand and the people behind the AI.

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