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How to build a successful risk mitigation strategy

IBM Big Data Hub

.” This same sentiment can be true when it comes to a successful risk mitigation plan. The only way for effective risk reduction is for an organization to use a step-by-step risk mitigation strategy to sort and manage risk, ensuring the organization has a business continuity plan in place for unexpected events.

Risk 80
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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

6) Data Quality Metrics Examples. Reporting being part of an effective DQM, we will also go through some data quality metrics examples you can use to assess your efforts in the matter. The data quality analysis metrics of complete and accurate data are imperative to this step. Table of Contents. 2) Why Do You Need DQM?

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Preliminary Thoughts on the White House Executive Order on AI

O'Reilly on Data

The recent discovery (documented by an exposé in The Atlantic ) that OpenAI, Meta, and others used databases of pirated books, for example, highlights the need for transparency in training data. Operational Metrics. Methods by which the AI provider manages and mitigates risks identified via Red Teaming, including their effectiveness.

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The hard truth of IT metrics

CIO Business Intelligence

And if you think you need metrics to manage you might be feeling guilty about not having enough of them. Good metrics are hard to craft, harder to manage, expensive to maintain, and perishable besides. Bad metrics, in contrast, are easier all the way around, but that doesn’t matter. Bad metrics are worse than no metrics.

Metrics 105
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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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Automating Model Risk Compliance: Model Monitoring

DataRobot Blog

If the assumptions are being breached due to fundamental changes in the process being modeled, the deployed system is not likely to serve its intended purpose, thereby creating further model risk that the institution must manage. Monitoring Model Metrics. Figure 1: Data drift tab of a deployed DataRobot model.

Risk 59
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Gartner Market Guide to DataOps Software

DataKitchen

The document they wrote is exceptionally close to what we see in the market and what our products do ! This document is essential because buyers look to Gartner for advice on what to do and how to buy IT software. Observability : Monitoring live/historic workflows, insights into workflow performance, and cost metrics impact analysis.

Software 130