Remove Data Governance Remove Data Quality Remove Predictive Analytics
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Modern Data Governance: Trends for 2025

Precisely

Key Takeaways: Prioritize metadata maturity as the foundation for scalable, impactful data governance. Recognize that artificial intelligence is a data governance accelerator and a process that must be governed to monitor ethical considerations and risk.

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AI in Data Governance: Enhancing Data Integrity and Security

ODSC - Open Data Science

Artificial Intelligence (AI) stands at the forefront of transforming data governance strategies, offering innovative solutions that enhance data integrity and security. In this post, let’s understand the growing role of AI in data governance, making it more dynamic, efficient, and secure.

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Mastering healthcare data governance with data lineage

IBM Journey to AI blog

The healthcare industry faces arguably the highest stakes when it comes to data governance. For starters, healthcare organizations constantly encounter vast (and ever-increasing) amounts of highly regulated personal data. healthcare, managing the accuracy, quality and integrity of data is the focus of data governance.

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Data Mesh Architecture on Cloud for BI, Data Science and Process Mining

Data Science Blog

BI provides real-time data analysis and performance monitoring, while Data Science enables a deep dive into dependencies in data with data mining and automates decision making with predictive analytics and personalized customer experiences. It advocates decentralizing data ownership to domain-oriented teams.

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Solving Complex Telecom Challenges with Data Governance and Location Analytics

Precisely

Here are some of the key trends and challenges facing telecommunications companies today: The growth of AI and machine learning: Telecom companies use artificial intelligence and machine learning (AI/ML) for predictive analytics and network troubleshooting. Data integration and data integrity are lacking.

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Effective strategies for gathering requirements in your data project

Dataconomy

Example: For a project to optimize supply chain operations, the scope might include creating dashboards for inventory tracking but exclude advanced predictive analytics in the first phase. Define data needs : Specify datasets, attributes, granularity, and update frequency. Key questions to ask: What data sources are required?

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Beyond data: Cloud analytics mastery for business brilliance

Dataconomy

Predictive analytics: Predictive analytics leverages historical data and statistical algorithms to make predictions about future events or trends. For example, predictive analytics can be used in financial institutions to predict customer default rates or in e-commerce to forecast product demand.

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