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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data.

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CIOs weigh where to place AI bets — and how to de-risk them

CIO Business Intelligence

There are a lot of risks and a lot of land mines to navigate,” says the analyst. Coming to grips with risk The first step in making any bet — or investment — is to understand your ability to withstand risk. This ensures that none of our sensitive data and intellectual property are availed to an outside provider.”

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AI adoption accelerates as enterprise PoCs show productivity gains

CIO Business Intelligence

Some prospective projects require custom development using large language models (LLMs), but others simply require flipping a switch to turn on new AI capabilities in enterprise software. “AI Production is another area that benefits from AI. “At Webster Bank is following a similar strategy. It’s a good accelerator in the beginning.”

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Introducing the technology behind watsonx.ai, IBM’s AI and data platform for enterprise

IBM Big Data Hub

The emergence of transformers and self-supervised learning methods has allowed us to tap into vast quantities of unlabeled data, paving the way for large pre-trained models, sometimes called “ foundation models.” ” These large models have lowered the cost and labor involved in automation.

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Automating Model Risk Compliance: Model Development

DataRobot Blog

Addressing the Key Mandates of a Modern Model Risk Management Framework (MRM) When Leveraging Machine Learning . The regulatory guidance presented in these documents laid the foundation for evaluating and managing model risk for financial institutions across the United States. To reference SR 11-7: .

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The path to socially responsible AI

CIO Business Intelligence

People across industries envisioned AI as being the fuel for thousands of hours of time saved, replacing jobs to reduce costs, and unlocking innovation at a scale we have never before seen. Interacting with AI data or experiences in silos introduces risk and increases inefficiencies because it’s kept separate.

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What is data governance? Best practices for managing data assets

CIO Business Intelligence

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time.