Astera

APRIL 22, 2024

One of the key processes in healthcare data management is integrating data from many patient information sources into a centralized repository. This data comes from various sources, ranging from electronic health records (EHRs) and diagnostic reports to patient feedback and insurance details.

Data Governance 52

Data Governance Healthcare Government Data Discovery 52

Business Analysis Knowledge Share

OCTOBER 27, 2023

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. Incomplete or inaccurate data can lead to incorrect conclusions and decisions.

Business Intelligence 52

Business Intelligence Business Intelligence Supply Chain Data Governance 52

Astera

NOVEMBER 30, 2023

It’s designed to efficiently handle and process vast volumes of diverse data, providing a unified and organized view of information. With its ability to adapt to changing data types and offer real-time data processing capabilities, it empowers businesses to make timely, data-driven decisions.

Data Warehouse 52

Data Warehouse Big Data Healthcare Data Governance 52

Astera

MAY 13, 2024

Government: Using regional and administrative level demographic data to guide decision-making. Healthcare: Reviewing patient data by medical condition/diagnosis, department, and hospital. Automated tools can help you streamline data collection and eliminate the errors associated with manual processes.

Supply Chain 52

Supply Chain E-Commerce Healthcare Data Quality 52

Astera

MARCH 30, 2023

Data sharing also enables better, informed decisions by providing access to data collected by various business functions such as operations, customer success, marketing, etc. Moreover, data sharing leads to better data governance by centralizing their data and ensuring that it is consistent, accurate, and updated.

Data Quality 52

Data Quality Data Warehouse Data Security Finance 52

Astera

JUNE 13, 2023

Evolution of Data Pipelines: From CPU Automation to Real-Time Flow Data pipelines have evolved over the past four decades, originating from the automation of CPU instructions to the seamless flow of real-time data. Techniques like data profiling, data validation, and metadata management are utilized.

E-Commerce 52

E-Commerce Data Quality Real-time Data Data Warehouse 52

Astera

JULY 11, 2023

Automated data extraction tools are becoming necessary because: Scalability: The volume of financial data is increasing exponentially with the growth of electronic transactions. Manual data entry is not scalable and cannot keep up with the volume of data.

Banking 40

Banking Government Real-time Data Monitoring 40