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Decision-Making in a Time of Crisis

O'Reilly on Data

The quality of the decision is based on known information and an informed risk assessment, while chance involves hidden information and the stochasticity of the world. Consider risk not only in terms of likelihood but also in terms of the impact of your decisions. The latter resulted in Mike losing his blackjack hand.

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Operationalizing responsible AI principles for defense

IBM Big Data Hub

Defense leaders focused on operationalizing the responsible curation of AI must first agree upon a shared vocabulary—a common culture that guides safe, responsible use of AI—before they implement technological solutions and guardrails that mitigate risk. Designate accountable people to mitigate these risks.

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How To Succeed As a DataOps Engineer

DataKitchen

A DataOps Engineer can make test data available on demand. We have automated testing and a system for exception reporting, where tests identify issues that need to be addressed. It then autogenerates QC tests based on those rules. You can track, measure and create graphs and reporting in an automated way.

Testing 246
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Agile Technology and Big Data Improve the State of Cybersecurity

Smart Data Collective

The risk of data breaches is rising sharply. The number increased 56% between 2017 and 2018. Cybersecurity experts are using data analytics and AI to identify warning signs that a firewall has been penetrated, conduct risk scoring analyses and perform automated cybersecurity measures.

Big Data 110
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Private cloud makes its comeback, thanks to AI

CIO Business Intelligence

As we are testing and dipping our toes in the water with AI, we are choosing to keep that as private as possible,” he says, noting that the public cloud has the horsepower needed for many LLMs of today but his company has the option of adding GPUs if needed via its privately owned Dell equipment. We have no choice. Semple says.

IT 136
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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. Build multiple MVPs to test conceptually and learn from early user feedback.

Insurance 250
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18 Examples Of Big Data Analytics In Healthcare That Can Save People

datapine

If you put on too many workers, you run the risk of having unnecessary labor costs add up. Every patient has his own digital record which includes demographics, medical history, allergies, laboratory test results, etc. All this vital information can be coupled with other trackable data to identify potential health risks lurking.

Big Data 364