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Data Strategy and Data Governance refers to the development and implementation of strategies for effective data management within an organization.
This segment describes how data quality is defined, measured, and controlled. It covers quality dimensions, validation rules, control points, escalation logic, and processes for handling deviations and continuous improvement. The focus is on traceable quality and effective controls, independent of specific role models or metadata and lineage implementations.
Continuous Data Quality Monitoring ensures the ongoing monitoring and improvement of data quality within organizations.
Data profiling is the process of analyzing datasets to compile statistics about data content and structure.
A method for assessing the quality of data in a system.
A structured approach to identify the root causes of problems.
Data controls are essential measures to protect and ensure the integrity of data.
Data observability enables monitoring, analyzing, and understanding data flows in real-time.
Data Observability Platforms enable organizations to gain insights into their data pipelines to ensure data integrity and availability.
Data Quality Management (DQM) is a systematic approach to ensuring the accuracy, completeness, and reliability of data.
Data validation is the process of verifying and ensuring the accuracy and quality of data.
Master Data Management (MDM) manages and harmonizes critical enterprise data across different systems.
Great Expectations is a powerful framework for validating and documenting data pipelines.
Monte Carlo is a tool for managing and analyzing uncertainties in data projects.
A powerful tool for data analysis and visualization.