Remove Data Discovery Remove Data Quality Remove Real-time Data
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ElegantJ BI Version 4.2: Business Intelligence With Real-Time Data Access

ElegantJ BI

The newest version of ElegantJ BI includes: Real-Time Cubes: Users have the freedom to work with real time data or cached data. The cube engine enables connection to disparate data sources such as databases, CSV files and MDX data sources like Microsoft® SSAS and SAP® BW cubes.

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ElegantJ BI Version 4.2: Business Intelligence With Real-Time Data Access

ElegantJ BI

The newest version of ElegantJ BI includes: Real-Time Cubes: Users have the freedom to work with real time data or cached data. The cube engine enables connection to disparate data sources such as databases, CSV files and MDX data sources like Microsoft® SSAS and SAP® BW cubes.

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ElegantJ BI Version 4.2: Business Intelligence With Real-Time Data Access

ElegantJ BI

The newest version of ElegantJ BI includes: Real-Time Cubes: Users have the freedom to work with real time data or cached data. The cube engine enables connection to disparate data sources such as databases, CSV files and MDX data sources like Microsoft® SSAS and SAP® BW cubes.

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AI data catalogs in 2024: what’s changed and why it matters

Astera

This is because the integration of AI transforms the static repository into a dynamic, self-improving system that not only stores metadata but also enhances data context and accessibility to drive smarter decision-making across the organization. And when everyone has easy access to data, they can collaborate and meet demands more effectively.

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A Guide to Automated Data Governance: Importance & Benefits

Astera

Instead of relying solely on manual efforts, automated data governance uses reproducible processes to maintain data quality, enrich data assets, and simplify workflows. This approach streamlines data management, maintains data integrity, and ensures consistent data quality and context over time.

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The Future of AI in Data Warehousing: Trends and Predictions 

Astera

For example, GE Healthcare leverage AI-powered data cleansing tools to improve the quality of data in its electronic medical records, reducing the risk of errors in patient diagnosis and treatment. Continuous Data Quality Monitoring According to Gartner , poor data quality cost enterprises an average of $15 million per year.

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12 Cloud Computing Risks & Challenges Businesses Are Facing In These Days

Data Pine

Since we live in a digital age, where data discovery and big data simply surpass the traditional storage and manual implementation and manipulation of business information, companies are searching for the best possible solution for handling data. It is evident that the cloud is expanding.