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Accelerating AI at scale without sacrificing security

CIO Business Intelligence

Unfortunately, implementing AI at scale is not without significant risks; whether it’s breaking down entrenched data siloes or ensuring data usage complies with evolving regulatory requirements. Similarly, in 2017 Equifax suffered a data breach that exposed the personal data of nearly 150 million people.

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Why ethical AI should be important to CIOs

CIO Business Intelligence

Salima Bhimani has been encouraging the responsible and ethical use of AI for several years as Alphabet’s first chief strategist and director for inclusive and responsible technology, business, and leaders from 2017 to 2023. Are we building AI strategies that are aligned to business goals? Will it mitigate risk?

Strategy 131
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Infor’s Velocity Summit Highlights Multiple Advances and Enhancements

David Menninger's Analyst Perspectives

Infor’s strategy is to tailor software with a high percentage of specific capabilities and functionality for customers in its target industries, delivering a faster time to value. An innate conservatism, aversion to risk and the need to ensure complete accuracy are the human factors at work in this delay.

Finance 130
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TBM helps CIOs translate tech spending to business outcomes

CIO Business Intelligence

The US Office of Management and Budget has also pushed agencies to use TBM practices since 2017. IT spending has evolved from an operational necessity to a key component of business strategy, he says. Energy use has become an important expense to monitor as well, along with more traditional IT costs and risk management.

ROI 96
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What is Model Risk and Why Does it Matter?

DataRobot Blog

This provides a great amount of benefit, but it also exposes institutions to greater risk and consequent exposure to operational losses. The stakes in managing model risk are at an all-time high, but luckily automated machine learning provides an effective way to reduce these risks.

Risk 111
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The trinity of errors in financial models: An introductory analysis using TensorFlow Probability

O'Reilly on Data

Whether financial models are based on academic theories or empirical data mining strategies, they are all subject to the trinity of modeling errors explained below. Time-variant distributions for asset values and risks are the rule, not the exception. The Money Formula , by David Orrell and Paul Wilmott, Wiley, 2017.

Modeling 202
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10 Big Data Examples Showing The Great Value of Smart Analytics In Real Life At Restaurants, Bars, and Casinos

datapine

And while a mere 22% of marketers state that they have a data-driven marketing strategy that is achieving significant results – by leveraging the right insights in the right way, success is inevitable. Doritos and Mountain Dew have both used this strategy with varying levels of success. 4) Consumers Are Deciding The Overall Menu.

Big Data 244