Remove Business Objectives Remove Data Quality Remove Risk Management
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5 tips for better business value from gen AI

CIO Business Intelligence

Instead, CIOs must partner with CMOs and other business leaders to help quantify where gen AI can drive other strategic impacts especially those directly connected to the bottom line. A second area is improving data quality and integrating systems for marketing departments, then tracking how these changes impact marketing metrics.

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Using Strategic Data Governance to Manage GDPR/CCPA Complexity

erwin

The complexity of regulatory requirements in and of themselves is aggravated by the complexity of the business and data landscapes within most enterprises. Creating and automating a curated enterprise data catalog , complete with physical assets, data models, data movement, data quality and on-demand lineage.

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Cloud analytics migration: how to exceed expectations

CIO Business Intelligence

A modern data and artificial intelligence (AI) platform running on scalable processors can handle diverse analytics workloads and speed data retrieval, delivering deeper insights to empower strategic decision-making. Business objectives must be articulated and matched with appropriate tools, methodologies, and processes.

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Beyond the hype: Key components of an effective AI policy

CIO Business Intelligence

An AI policy serves as a framework to ensure that AI systems align with ethical standards, legal requirements and business objectives. While this leads to efficiency, it also raises questions about transparency and data usage. This includes regular audits to guarantee data quality and security throughout the AI lifecycle.

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Best BI Tools Examples for 2024: Business Intelligence Software

FineReport

Financial Services Optimization : In the financial services sector, a major institution leveraged a sophisticated BI platform to analyze market trends, customer behavior, and risk management strategies. This framework ensures that data remains accurate, consistent, and secure across all levels of the organization.

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Building a Data Strategy for Defence Partners

Alation

It should make data available, maintain data consistency and accuracy, and support data security. Gartner describes it as ‘ a highly dynamic process employed to support the acquisition, organisation, analysis, and delivery of data in support of business objectives ’. Why is a data strategy important?

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Automating Model Risk Compliance: Model Validation

DataRobot Blog

What are some steps that the modeler/validator must take to evaluate the model and ensure that it is a strong fit for its design objectives? Evaluating ML models for their conceptual soundness requires the validator to assess the quality of the model design and ensure it is fit for its business objective. Conclusion.

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