Remove Management Remove Measurement Remove Optimization Remove Risk Management
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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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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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Top 10 governance, risk, and compliance certifications

CIO Business Intelligence

GRC certifications validate the skills, knowledge, and abilities IT professionals have to manage governance, risk, and compliance (GRC) in the enterprise. What are GRC certifications? Why are GRC certifications important?

Risk 96
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How to Manage Risk with Modern Data Architectures

Cloudera

To ensure the stability of the US financial system, the implementation of advanced liquidity risk models and stress testing using (MI/AI) could potentially serve as a protective measure. To improve the way they model and manage risk, institutions must modernize their data management and data governance practices.

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IT leaders know they need to manage IT risk—but need help in execution

CIO Business Intelligence

Organizations big and small, across every industry, need to manage IT risk. based IT directors and vice presidents in companies with more than 1,000 employees to determine what keeps them up at night—and it comes as no surprise that one of their biggest nightmares is managing IT risk. trillion annually by 2025.

Risk 82
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What keeps IT leaders up at night?

CIO Business Intelligence

Respondents were asked about their current IT stressors, their approach to modernizing their IT infrastructure, and how they plan to become more efficient and optimized in the years ahead. It helps reduce risk, increase efficiency, optimize resources, and improve both the customer and employee experience.

IT 89
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Managing risk in machine learning

O'Reilly on Data

Model lifecycle management. There are also many important considerations that go beyond optimizing a statistical or quantitative metric. As we deploy ML in many real-world contexts, optimizing statistical or business metics alone will not suffice. Data Platforms. Real modeling begins once in production. Culture and organization.