Astera

FEBRUARY 28, 2024

In other words, a data warehouse is organized around specific topics or domains, such as customers, products, or sales; it integrates data from different sources and formats, and tracks changes in data over time. This not only aids user comprehension of data but also facilitates seamless data discovery, access, and analysis.

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Astera

APRIL 24, 2024

A data governance framework is a structured way of managing and controlling the use of data in an organization. It helps establish policies, assign roles and responsibilities, and maintain data quality and security in compliance with relevant regulatory standards.

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Astera

MARCH 20, 2024

This article covers everything about enterprise data management, including its definition, components, comparison with master data management, benefits, and best practices. What Is Enterprise Data Management (EDM)? Why is Enterprise Data Management Important?

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Astera

MARCH 28, 2024

They are usually created after a company has defined its data, labeled it, identified the relevant stakeholders responsible for data security, and assigned them access roles. Data Catalog vs. Data Dictionary A common confusion arises when data dictionaries come into the discussion.

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Astera

APRIL 4, 2024

Data governance’s primary purpose is to ensure organizational data assets’ quality, integrity, security, and effective use. The key objectives of Data Governance include: Enhancing Clear Ownership: Assigning roles to ensure accountability and effective management of data assets.

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Astera

MAY 14, 2024

This metadata variation ensures proper data interpretation by software programs. Process metadata: tracks data handling steps. It ensures data quality and reproducibility by documenting how the data was derived and transformed, including its origin.

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Astera

FEBRUARY 27, 2024

It also supports predictive and prescriptive analytics, forecasting future outcomes and recommending optimal actions based on data insights. Enhancing Data Quality A data warehouse ensures high data quality by employing techniques such as data cleansing, validation, integration, and standardization during the ETL process.

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