Intelligent data, technology, artificial intelligence (AI) and machine learning (ML) have started to lighten the burden and establish new ways to facilitate sustainable demand and supply. To learn the answers to these questions, I reached out to several predictive analytics experts in the contact center industry. But many patterns have abruptly changed over the last six months during the COVID-19 pandemic. To adapt to these changes and avoid making inaccurate predictions, predictive analytics or machine learning (ML) systems can be adjusted or redirected by feeding them a new set of learning data directly from this time of change. COVID-19 has reshaped the way humans interact with technology in healthcare. For this article, I wanted to zero-in on three areas key to strategic planning for the post-COVID era—collecting and using customer data, human resources (hiring, performance management and retention), and data security. Predictive analytics models have been enormously successful at identifying patients most at risk of bad outcomes such that resources focus on helping those who are most vulnerable. Predictive Analytics Market is poised to experience spend growth of more than USD 4 billion between 2020-2024 at a CAGR of over 10.76%. Open source COVID-19 Predictive Analytics to identify, stratify and prioritize outreach and resources for those patients most at risk for hospitalization, including from COVID-19 DANIEL FOPPEN: Predictive models are based on past patterns. This webinar, hosted by the U.S. Chamber Foundation and Tata Consultancy Services’ Digital Empowers Campaign, is the first in a three-part virtual event series on COVID-19 response designed to bring the tech community together to address the challenges and opportunities, and create a platform for collective action. COVID-19 has had a massive impact on the manufacturing industry. The Best Use of Predictive Analytics. 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