Data architecture translates business needs into data and system requirements, and seeks to manage data and its flow through the enterprise.

Unlock the power of your data with an effective data governance framework for security, compliance, and decision-making. Data governance frameworks are structured approaches to managing and ...

Learn the differences between effective data quality and data governance practices and how to integrate them to enhance your data strategy.

Quality is a central element in business strategy and academic research. Despite important research on quality, an opportunity for an integrative framework remains. The authors present an integrative ...

Companies that have a functional data quality framework are more likely to be at an advanced maturity level with regards to analytical capabilities.

This paper describes work in progress on data quality, an important element of greater transparency in economic policy and financial stability. Data quality is being dealt with systematically by the ...

With the Open Data Quality Framework, Alation customers can strengthen data governance for Snowflake by making data quality information visible. This allows governance teams to document and enforce ...

Data is essential for the success of any AI project, but understanding what makes data beneficial—or harmful—for AI is crucial.

To mitigate compliance violations, businesses must understand how their data governance framework and underlying data quality impacts regulatory risk.

Master data cleaning with the CLEAN framework, a tested method to turning chaotic datasets into clarity and precision. Clean messy datasets ...