This week, we highlight how Lityx works with agency partners to allow them to offer advanced analytics to their clients. We share best practices of data governance in multi-cloud environments and explore the ways artificial intelligence (AI) will transform marketing in 2020. We learn how machine learning (ML) is being used to estimate the risk of cardiovascular death. And finally, we examine how AI is being used within the retail space.
by Andrea Steffes-Tuttle, contributing writer for Lityx.com
Agencies frequently communicate to us that they struggle to offer advanced analytics (ML and AI) services to their clients. They note that their clients are demanding sophisticated data approaches to marketing efforts, but admit they have a hard time finding the right resources to offer to their clients. Learn how we partner with agencies to augment their technology stack, enabling them to offer advanced analytic service.
by George Nelson, contributing writer for InformationWeek.com
Like most things in the world of data, creating and maintaining governance policies in a cloud environment requires constant compromise and negotiation. Data governance is an ongoing process. You can’t implement data governance policies, ignore them, and expect them to provide any benefit. The author offers insight into why there’s no silver bullet for establishing data governance policies in multi-cloud environments. He also provides some guidelines for how IT leaders can think about data governance.
by Ipsit Roy, contributing writer for MarTechAdvisor.com
AI is hoping to change the game of customer data, with newfound concepts of ML to anticipate the next move to enhance a customer’s journey–and positively impact your organization’s bottom line. This is done by fostering relevant and compelling interactions with customers, boosting ROI, and impacting revenue. In fact, brands who have recently adopted AI for marketing strategy, predict a 37 percent reduction in costs along with a 39 percent increase in revenue on average by the end of 2020 alone.
by Rachel Gordon, contributing writer for News.MIT.edu
A team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) came up with a new system for better-predicting health outcomes: an ML model called “RiskCardio” that can estimate, from the electrical activity of their heart, a patient’s risk of cardiovascular death. Using just the first 15 minutes of a patient’s raw electrocardiogram (ECG) signal, the tool produces a score that places patients into different risk categories. Learn more about how it works and how it was developed.
by Sarah Feldman, contributing writer IBTimes.com
This infographic highlights the findings of a recent study conducted by Capgemini to understand AI in the retail space. According to this study, ML is retailers go-to AI use across all business types. And the majority of ML implementations are for customer-facing projects rather than operations. Unsurprisingly, the study also found that AI is still undeveloped within this space, with only one percent of AI projects reaching full-scale deployment. View the full infographic and findings.
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