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Enterprise Data Management: Strategy, Benefits, Best Practices

Astera

Metadata Management Data is valuable, but understanding what that data means is invaluable for organizations. Functioning as a data dictionary, metadata management defines the structure and meaning of your data assets.

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Data Governance Framework: What is it? Importance, Pillars and Best Practices

Astera

Builds a Unified Data Language A common business language and data quality rules help everyone in the organization understand data terms and standards similarly. This approach avoids confusion and errors in data management and use, making communication across the company more straightforward.

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Successful Augmented Analytics Initiatives Do Not End with Implementation!

ElegantJ BI

In order to get business users to embrace and adopt self-serve augmented data discovery tools, the enterprise must approach the implementation with appropriate change management processes. Making Data Analytics part of the day-to-day and strategic decision process is key.

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Successful Augmented Analytics Initiatives Do Not End with Implementation!

ElegantJ BI

In order to get business users to embrace and adopt self-serve augmented data discovery tools, the enterprise must approach the implementation with appropriate change management processes. Making Data Analytics part of the day-to-day and strategic decision process is key.

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Successful Augmented Analytics Initiatives Do Not End with Implementation!

ElegantJ BI

In order to get business users to embrace and adopt self-serve augmented data discovery tools, the enterprise must approach the implementation with appropriate change management processes. Making Data Analytics part of the day-to-day and strategic decision process is key.

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Data Vault vs. Data Mesh: Choosing the Right Data Architecture?

Astera

Flexibility While they differ in their degree of flexibility, both Data Vault and Data Mesh aim to provide solutions that are adaptable to changing data requirements. Data Vault achieves this through versioning and change management, while Data Mesh relies on domain teams to adapt their data products.