Analytics Data science Insurance Security 
IBM Big Data Hub
MAY 8, 2023
In my previous post , I described the different capabilities of both discriminative and generative AI, and sketched a world of opportunities where AI changes the way that insurers and insured would interact. Usage risk—inaccuracy The performance of an AI system heavily depends on the data from which it learns.
Collibra
APRIL 9, 2021
Recapping a discussion moderated by Stijn Christiaens and featuring insurance data experts from Deloitte UK . Insurance is a data-intensive business. Insurance companies need data to better assess risks and price policies competitively, but also profitably.
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Collibra
JUNE 1, 2022
Collibra Adaptive Data and Analytics Governance is available for a free test drive! The foundation of a data-driven organization. Data is more valuable than ever. The key is adaptive data and analytics governance. At Collibra, we believe it’s the next big step forward in data governance. .
Reltio
NOVEMBER 9, 2020
Now Tech: Customer Data Management Solutions, Q1 2020, by Noel Yuhanna, Forrester Research, Inc., Our innovative customers span different industries like life sciences, financial services and insurance, healthcare, CPG, apparel, retail, travel and hospitality and high tech. Jan 8, 2020. But, they all have one thing in common.
Reltio
FEBRUARY 3, 2020
And, to complicate the situation, they need to figure out how to comply with emerging privacy laws and security needs. Connected Customer Data is at the Heart of Winning in the Experience Economy. He and his team spent $300,000 and 9 months upgrading their legacy master data management (MDM) system. Register now!
Collibra
MARCH 4, 2020
Only a decade ago, barely a dozen large corporations had a Chief Data Officer; today, by most accounts, that number has passed 10,000 worldwide. . Their initial role had a lot to with ensuring compliance and securing all data assets. Here’s how we see the data office concept playing out among our customers.
IBM Big Data Hub
FEBRUARY 13, 2024
Key considerations: Tech stack: Ensure your existing technology infrastructure can handle the demands of AI models and data processing. Teamwork: Assemble a team with expertise in AI, data science and your industry. Data: High-quality, relevant data is the fuel that powers generative AI success.
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