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The journey to a mature asset management system

IBM Big Data Hub

Asset management and technological innovation Advancements in technology underpin the need for holistic grid asset management, making the assets in the grid smarter and equipping the workforce with smart tools. Robots and drones perform inspections by using AI-based visual recognition techniques.

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Everything You Need to Know About Project Management Dashboard

FineReport

Moreover, the implementation of an effective Project Management Dashboard facilitates data-driven decision-making and sustainable business success. This approach is facilitated by project management dashboards, which enable you to monitor, optimize, and enhance project performance while boosting overall team productivity.

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How Big Data Impacts The Finance And Banking Industries

Smart Data Collective

Nowadays, terms like ‘Data Analytics,’ ‘Data Visualization,’ and ‘Big Data’ have become quite popular. Here are a few of the advantages of Big Data in the banking and financial industry: Improvement in risk management operations. Big Data provides financial and banking organizations with better risk coverage.

Big Data 140
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Best BI Tools Examples for 2024: Business Intelligence Software

FineReport

In addition to these advancements, another prominent trend in data analysis is the growing impact of data visualization. This trend addresses the need for users to interpret complex datasets through interactive visuals that facilitate a deeper understanding of the underlying information.

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7 steps for turning shadow IT into a competitive edge

CIO Business Intelligence

After all, 41% of employees acquire, modify, or create technology outside of IT’s visibility , and 52% of respondents to EY’s Global Third-Party Risk Management Survey had an outage — and 38% reported a data breach — caused by third parties over the past two years.

IT 136
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Automating Model Risk Compliance: Model Monitoring

DataRobot Blog

Monitoring Model Metrics. With this data in hand, we are able to measure both the data drift and model performance, both of which are essential metrics in measuring the health of the deployed model. The accuracy of a model is another essential metric that informs us about its health in a deployed setting.

Risk 59
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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. While our analysis of each method may appear technical, we believe that understanding the tools available, and how to use them, is critical for all risk management teams.