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Health Data Management | Challenges and Best Practices

Astera

Billion by 2026 , showing the crucial role of health data management in the industry. Since traditional management systems cannot cope with the massive volumes of digital data, the healthcare industry is investing in modern data management solutions to enable accurate reporting and business intelligence (BI) initiatives.

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Top 6 Mulesoft Alternatives & Competitors in 2024

Astera

Shortcomings in Complete Data Management : While MuleSoft excels in integration and connectivity, it falls short of being an end-to-end data management platform. Notably, MuleSoft lacks built-in capabilities for AI-powered data extraction and the direct construction of data warehouses.

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12 Best Data Mapping Tools & Solutions in 2023

Astera

Managing data effectively is a multi-layered activity—you must carefully locate it, consolidate it, and clean it to make it usable. One of the first steps in the data management cycle is data mapping. Data mapping is the process of defining how data elements in one system or format correspond to those in another.

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Legacy System: Definition, Challenges, Types & Modernization

Astera

Despite their critical functions, these systems also lead to increased maintenance costs, security vulnerabilities, and limited scalability. Some common types of legacy systems include: Mainframe Systems Description: Large, powerful computers used for critical applications, bulk data processing, and enterprise resource planning.

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5 Best Claims Automation Software in 2024

Astera

IBM estimates that the insurance industry contributes significantly to the creation of 2.5 quintillion bytes of data every day, with claims data being a major contributor to this massive volume. Manual processing of this data is no longer practical, given the large data volume. Request a Demo

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How Automated Financial Data Integration Streamlines Fraud Detection

Astera

Fraudsters often exploit data quality issues, such as missing values, errors, inconsistencies, duplicates, outliers, noise, and corruption, to evade detection and carry out their schemes. According to Gartner , 60% of data experts believe data quality across data sources and landscapes is the biggest data management challenge.

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Automated Financial Data Integration for Fraud Detection | Astera

Astera

According to a survey by Experian , 95% of organizations see negative impacts from poor data quality, such as increased costs, lower efficiency, and reduced customer satisfaction. According to a report by IBM , poor data quality costs the US economy $3.1 Saving money and boosting the economy.