Remove Data Governance Remove Data Quality Remove Data Security Remove Monitoring
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Astera’s Guide to Insurance Data Quality and Governance

Astera

A strategic approach to data management is needed to meet these demands — particularly a greater focus on high data quality and robust governance to guarantee accuracy, security, and compliance. It operates under a well-structured governance framework, ensuring accountability and consistency in data management.

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Data Governance in Financial Services: How to Ensure Data Integrity for Risk Management and Reporting

Astera

Introduction   As financial institutions navigate intricate market dynamics and heighten regulatory requirements, the need for reliable and accurate data has never been more pronounced. This has spotlighted data governance—a discipline that shapes how data is managed, protected, and utilized within these institutions.

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Data Pipeline Monitoring: Metrics and Best Practices

Astera

This highlights the need for effective data pipeline monitoring. Data pipeline monitoring enhances decision-making, elevates business performance, and increases trust in data-driven operations, contributing to organizational success. What is Data Pipeline Monitoring?

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Top 20 Data Warehouse Best Practices in 2024

Astera

Given the generally complex nature of the data warehouse architecture, there are certain data warehouse best practices that focus on performance optimization, data governance and security, scalability and future-proofing, and continuous monitoring and improvement.

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Use Case Analysis for Business Intelligence Projects

Business Analysis Knowledge Share

Enhanced Data Governance : Use Case Analysis promotes data governance by highlighting the importance of data quality , accuracy, and security in the context of specific use cases. The data collected should be integrated into a centralized repository, often referred to as a data warehouse or data lake.

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Overcoming Snowflake Challenges – A Practical Guide 

Astera

That’s how it can feel when trying to grapple with the complexity of managing data on the cloud-native Snowflake platform. They range from managing data quality and ensuring data security to managing costs, improving performance, and ensuring the platform can meet future needs. So, let’s get started!

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Scalable ETL Architectures: Handling Large Volumes of Data 

Astera

How to Build ETL Architectures To build ETL architectures, the following steps can be followed, Requirements Analysis: Analyse data sources, considering scalability, data quality, and compliance requirements. Incremental Data Extraction: Supporting the extraction of only changed or new data , efficiently tracking changes.