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4 steps to improving your ESG risk management to increase financial performance

IBM Big Data Hub

Environmental, Social, and Governance (ESG) risk management has emerged as a critical aspect of business strategy for companies worldwide. However, 57% of CEOs admit that defining and measuring the Return on Investment (ROI) and economic benefits of their sustainability efforts remain a significant challenge.

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How A Data Catalog Enhances Data Risk Management

Alation

But data leaders must work quickly, and use the right tools, to understand, manage, and protect data while complying with related regulations and standards. The Increasing Focus On Data Risk Management. The Australian Prudential Regulation Authority (APRA) released nonbinding standards covering data risk management.

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Data Scalability Raises Considerable Risk Management Concerns

Smart Data Collective

The Relationship between Big Data and Risk Management. While the sophisticated Internet of Things can positively impact your business, it also carries a significant risk of data misuse. Tips for Improving Risk Management When Handling Big Data. Risk management is a crucial element of any successful organization.

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The journey to a mature asset management system

IBM Big Data Hub

This blog series discusses the complex tasks energy utility companies face as they shift to holistic grid asset management to manage through the energy transition. Asset performance management (APM) processes, such as risk-based and predictive maintenance and asset investment planning (AIP), enable health monitoring technologies.

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ERM Program Fundamentals for Success in the Banking Industry

Speaker: William Hord, Senior VP of Risk & Professional Services

Enterprise Risk Management (ERM) is critical for industry growth in today’s fast-paced and ever-changing risk landscape. When building your ERM program foundation, you need to answer questions like: Do we have robust board and management support?

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What Is Model Risk Management and How is it Supported by Enterprise MLOps?

Domino Data Lab

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. Systematically enabling model development and production deployment at scale entails use of an Enterprise MLOps platform, which addresses the full lifecycle including Model Risk Management.

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CIO risk-taking 101: Playing it safe isn’t safe

CIO Business Intelligence

When it comes to structural risks you can ignore them as well, but you can’t make them go away by doing so and will be blamed if they’re “realized” (the risk-management term for “becoming real”). Rationalizing the applications portfolio reduces the odds of these risks being realized. IT Leadership, Risk Management

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