Remove Data Quality Remove Measurement Remove Risk Management
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Middle East tech leaders explore AI’s role in modern risk management

CIO Business Intelligence

In today’s fast-paced digital environment, enterprises increasingly leverage AI and analytics to strengthen their risk management strategies. By adopting AI-driven approaches, businesses can better anticipate potential threats, make data-informed decisions, and bolster the security of their assets and operations.

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5 tips for better business value from gen AI

CIO Business Intelligence

Set clear, measurable metrics around what you want to improve with generative AI, including the pain points and the opportunities, says Shaown Nandi, director of technology at AWS. A second area is improving data quality and integrating systems for marketing departments, then tracking how these changes impact marketing metrics.

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Data Scalability Raises Considerable Risk Management Concerns

Smart Data Collective

As such, you should concentrate your efforts in positioning your organization to mine the data and use it for predictive analytics and proper planning. The Relationship between Big Data and Risk Management. Tips for Improving Risk Management When Handling Big Data. Vendor Risk Management (VRM).

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Managing machine learning in the enterprise: Lessons from banking and health care

O'Reilly on Data

After the 2008 financial crisis, the Federal Reserve issued a new set of guidelines governing models— SR 11-7 : Guidance on Model Risk Management. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk. Model risk management. AI projects in financial services and health care.

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Why HR professionals struggle with big data

CIO Business Intelligence

However, it is often unclear where the data needed for reporting is stored and what quality it is in. Often the data quality is insufficient to make reliable statements. Insufficient or incorrect data can even lead to wrong decisions, says Kastrati. Big data and analytics provide valuable support in this regard.

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Why you should care about debugging machine learning models

O'Reilly on Data

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. There are at least four major ways for data scientists to find bugs in ML models: sensitivity analysis, residual analysis, benchmark models, and ML security audits. Sensitivity analysis.

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CDOs and CDAOs: Rethink your role or fade away

CIO Business Intelligence

Chief data and analytics officers need to reinvent themselves in the age of AI or risk their responsibilities being assimilated by their organizations’ IT teams, according to a new Gartner report. And with data quality tied directly to successful AI projects, CDAOs must also increase their visibility and show how they can help. “Gen