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The Impact of Data Quality on M&A Success

Astera

For a successful merger, companies should make enterprise data management a core part of the due diligence phase. This provides a clear roadmap for addressing data quality issues, identifying integration challenges, and assessing the potential value of the target company’s data.

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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. Ensure employees have the resources to manage data and adhere to data governance policies effectively.

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Data Quality Tools: Top 8 for 2023 & Beyond

Astera

What matters is how accurate, complete and reliable that data. Data quality is not just a minor detail; it is the foundation upon which organizations make informed decisions, formulate effective strategies, and gain a competitive edge. to help clean, transform, and integrate your data.

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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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Big Data Security: Protecting Your Valuable Assets

Astera

Big Data Security: Protecting Your Valuable Assets In today’s digital age, we generate an unprecedented amount of data every day through our interactions with various technologies. The sheer volume, velocity, and variety of big data make it difficult to manage and extract meaningful insights from.

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An Introduction to Active Data Governance

Astera

Unlike passive approaches, which might only react to issues as they arise, active data governance anticipates and mitigates problems before they impact the organization. Here’s a breakdown of its key components: Data Quality: Ensuring that data is complete and reliable. This includes implementing strict access controls.

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How to Automate Credit Risk Assessment Framework Using ETL

Astera

The data is stored in different locations, such as local files, cloud storage, databases, etc. The data is updated at different frequencies, such as daily, weekly, monthly, etc. The data quality is inconsistent, such as missing values, errors, duplicates, etc.