Remove Experimentation Remove Risk Remove ROI Remove Testing
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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

Regulations and compliance requirements, especially around pricing, risk selection, etc., Fractal’s recommendation is to take an incremental, test and learn approach to analytics to fully demonstrate the program value before making larger capital investments. Transforming the organization to collaborate/compete with insur-techs.

Insurance 250
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Belcorp reimagines R&D with AI

CIO Business Intelligence

As Belcorp considered the difficulties it faced, the R&D division noted it could significantly expedite time-to-market and increase productivity in its product development process if it could shorten the timeframes of the experimental and testing phases in the R&D labs.

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Machine Learning Product Management: Lessons Learned

Domino Data Lab

Pete indicates, in both his November 2018 and Strata London talks, that ML requires a more experimental approach than traditional software engineering. It is more experimental because it is “an approach that involves learning from data instead of programmatically following a set of human rules.”

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What you need to know about product management for AI

O'Reilly on Data

This has serious implications for software testing, versioning, deployment, and other core development processes. The need for an experimental culture implies that machine learning is currently better suited to the consumer space than it is to enterprise companies. And you, as the product manager, are caught between them.

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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. A key trend is the adoption of multiple models in production.

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10 Books that Data Analyst Should Read

FineReport

Effective use of big data helps companies analyze critical information more accurately, ultimately improving operational efficiency, reducing costs, reducing risk, accelerating innovation, and increasing revenue. – Data Divination: Big Data Strategies. by Dimitri Maex and Paul B.

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Building Smarter Financial Services: The Role of Semantic Technologies, Knowledge Graphs and Generative AI

Ontotext

Nimit Mehta: I think that 2024 is going to be a buckle-down year, but, at the same time, we’ll see a rapid explosion of experimentation. Nimit Mehta : You are talking about the three big ones: cost, revenue, and risk. And, when you get to the top, it’s about risks and existential threats to the business. Show me the ROI.”