This article covers everything about enterprise data management, including its definition, components, comparison with master data management, benefits, and best practices.

Amazon Web Services (AWS) ETL refers to a cloud-based set of tools and services that help extract data from different sources, make it usable, and store it in a way that makes it easy to analyze and make decisions based on it. AWS ETL tools offer a unique advantage for businesses seeking to streamline their data processes. These tools are efficient, scalable, and adaptable, making them ideal for a wide range of industries, from healthcare and finance to retail and beyond.

A Smartsheet report found that over 40% of workers spend at least a quarter of their workweek manually extracting data. Tax specialists in many organizations spend hours or even days sorting through piles of paper or PDF documents, looking for relevant information, and entering it into spreadsheets or databases. That’s a lot of time and money wasted on a tedious and error-prone process. Fortunately, there is a better way to handle tax form data extraction.

Snowflake has restructured the data warehousing scenario with its cloud-based architecture. Businesses can easily scale their data storage and processing capabilities with this innovative approach. It eliminates the need for complex infrastructure management, resulting in streamlined operations. According to a recent Gartner survey, 85% of enterprises now use cloud-based data warehouses like Snowflake for their analytics needs.

Insurance companies and third-party administrators are increasingly turning to automated data extraction to expedite the processing of medical insurance claims. This approach serves as a better alternative to time-intensive manual claim management. Leveraging AI technology allows them to efficiently extract crucial data from documents, eliminating manual data entry errors and significantly reducing processing times.

Workplace claims are legal actions or complaints that employees set forth against their employers due to violations of employment laws or contractual agreements. In recent times, employees feel encouraged to speak up for their rights with no workplace harassment, discrimination or unjust treatment. This increased awareness has raised legal standards and regulatory frameworks and thus, employees feel more empowered to report instances of harassment and discrimination.

95% of insurers are currently accelerating their digital transformation with AI-driven claims processing. Traditionally, this process involved manual steps such as claim initiation, data entry, validation, decision-making, and payout, consuming significant time and resources. However, the introduction of AI has replaced tedious manual work, enabling companies to streamline their tasks efficiently.

ETL testing is a set of procedures used to evaluate and validate the data integration process in a data warehouse environment. In other words, it’s a way to verify that the data from your source systems is extracted, transformed, and loaded into the target storage as required by your business rules. ETL (Extract, Transform, Load) is how data integration tools and BI platforms primarily turn data into actionable insights.

The U.S. Bureau of Labor Statistics reports that the incidence rate of nonfatal workplace accidents has decreased over the years, which can be attributed to the implementation of preventive measures in private industry. Despite this positive trend, companies deal with large volumes of unstructured data that demand effective management. Addressing these complexities is easier with Astera’s unstructured data extraction solution.

Claims processing is a multi-faceted operation integral to the insurance, healthcare, and finance industries. It’s a comprehensive procedure that involves carefully examining a claim. Claim processing is not a single-step process; instead, it involves multiple stages, each serving as a critical control point to ensure the accuracy and fairness of the claim resolution.