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What Is Data Processing? Definition and Stages

Astera

Data processing involves transforming raw data into valuable information for businesses. Generally, data scientists process data, which includes collecting, organizing, cleaning, verifying, analyzing, and converting it into readable formats such as graphs or documents.

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Streamlining Healthcare Operations with Automated Data Entry

Astera

Data entry in healthcare is extremely common for one major reason: the number of documents – patient information, medical records, insurance forms, billing forms, lab reports, prescriptions, consent forms, medical charts, and that’s just the beginning. To contrast this number, manual data entry can have accuracy as low as 75%.

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The 10 Essential SaaS Trends You Should Watch Out For In 2020

Data Pine

SaaS is less robust and less secure than on-premises applications: Despite some SaaS-based teething problems or technical issues reported by the likes of Google, these occurrences are incredibly rare with software as a service applications – and there hasn’t been one major compromise of a SaaS operation documented to date.

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What does “hyperautomation” mean?

CMW Lab Blog

It gives flexibility in those areas where automation has never been possible before: processes that are not supported by any documentation or do not rely on any structured data. As a result hyperautomation became an integral part of a digital transformation, aligning RPA and AI to eliminate most of the manual business work.

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Integrating Your Atlassian Cloud Ecosystem: Expert Insights for Maximizing Tool Synergy

Cprime

AI’s ability to analyze vast amounts of data, automate repetitive tasks, and provide actionable insights can significantly enhance the efficiency and effectiveness of the tools within the Atlassian suite.

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Maximizing Return on Your SAP Investment with Process Runner

Insight Software

The Hidden Price Tag of Inefficient SAP Data Processing While the upfront cost of SAP is well-documented, the true cost of inefficient data processing within the system often lurks in the shadows. Imagine your technology team drowning in 30 hours of data tasks per week, as revealed by recent insightsoftware research.