Big Data Insurance Optimization Unstructured Data 
Cloudera
OCTOBER 28, 2021
I’ve had the pleasure to participate in a few Commercial Lines insurance industry events recently and as a prior Commercial Lines insurer myself, I am thrilled with the progress the industry is making using data and analytics. Another historic example is crop and livestock insurance in Germany in the 1700s.
IBM Big Data Hub
DECEMBER 4, 2023
IBM can help insurance companies insert generative AI into their business processes IBM is one of a few companies globally that can bring together the range of capabilities needed to completely transform the way insurance is marketed, sold, underwritten, serviced and paid for.
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IBM Big Data Hub
OCTOBER 30, 2023
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. The retailer uses these insights to optimize inventory levels, reduce costs and enhance efficiency.
IBM Big Data Hub
FEBRUARY 14, 2024
By infusing AI into IT operations , companies can harness the considerable power of NLP, big data, and ML models to automate and streamline operational workflows, and monitor event correlation and causality determination. Generative AI can speed and optimize product design by helping companies create multiple design options.
CIO Business Intelligence
JULY 26, 2023
The only thing we have on premise, I believe, is a data server with a bunch of unstructured data on it for our legal team,” says Grady Ligon, who was named Re/Max’s first CIO in October 2022. minutes from the moment the property is listed.
IBM Big Data Hub
FEBRUARY 2, 2023
Misconception 3: All data warehouse migrations are the same, irrespective of vendors While migrating to the cloud, CTOs often feel the need to revamp and “modernize” their entire technology stack – including moving to a new cloud data warehouse vendor.
O'Reilly on Data
SEPTEMBER 19, 2023
Each time, the underlying implementation changed a bit while still staying true to the larger phenomenon of “Analyzing Data for Fun and Profit.” ” They weren’t quite sure what this “data” substance was, but they’d convinced themselves that they had tons of it that they could monetize.
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