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Retailers can tap into generative AI to enhance support for customers and employees

IBM Big Data Hub

As the retail industry witnesses a shift towards a more digital, on-demand consumer base, AI is becoming the secret weapon for retailers to better understand and cater to this evolving consumer behavior. Generative AI excels at handling diverse data sources such as emails, images, videos, audio files and social media content.

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IBM and TechD partner to securely share data and power insights with gen AI

IBM Big Data Hub

Also, Db2 seamlessly integrates with watsonx Assistant’s natural language processing capabilities to analyze unstructured data and derive insights. Streamlined access to effective product descriptions maintained a competitive advantage in online retail.

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How IBM and AWS are partnering to deliver the promise of AI for business

IBM Big Data Hub

IBM, a pioneer in data analytics and AI, offers watsonx.data, among other technologies, that makes possible to seamlessly access and ingest massive sets of structured and unstructured data. One of the largest children clothing retailer in the US utilizes this solution to streamline its complex supply chain.

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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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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructured data for various academic and business applications.

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Anomaly detection in machine learning: Finding outliers for optimization of business functions

IBM Big Data Hub

However, data scientists should monitor results gathered through unsupervised learning. Because these techniques are making assumptions about the data being input, it is possible for them to incorrectly label anomalies. Engineers can apply unsupervised learning methods to automate feature learning and work with unstructured data.

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Driving new revenue streams through artificial intelligence and advanced analytics

CGI

For more than 50 years, banks have relied on computers and software to manage and secure their data, as well as protect their customers’ interests. We are now on the cusp of a major revolution—something that will be as big for banks as the internet was for retail businesses. What’s driving this momentous change?