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Data & Business Intelligence encompasses the strategies, technologies, and practices for analyzing and utilizing data to support business decisions.
This segment covers governance and quality assurance for data in the analytics domain. It includes responsibilities, data terminology, quality criteria, validation and approval processes, and handling changes to definitions and datasets. The focus is on reliability, traceability, and controlled evolution, independent of specific analytics or visualization techniques.
A Data Governance Framework is a structured approach to managing data to ensure the quality, security, and availability of information.
A method for tracking and analyzing data lineage within information systems.
A method for assessing the quality of data in a system.
A data catalog platform organizes, manages, and efficiently utilizes enterprise data.
A Data Governance Framework establishes guidelines and practices to enhance data management and usage within an organization.
Data lineage standards enable traceability and transparency of data flows within systems.
Protection of personal and sensitive data through organizational, technical and legal measures.
Data quality dimensions are important criteria for assessing data quality in organizations.
A metadata management system organizes and manages metadata to enhance data availability and usage.
A Single Source of Truth (SSoT) ensures that all stakeholders access the same, consistent data source.
Apache Atlas is an open-source project for metadata management that supports governance, data lineage, and data cataloging.
Collibra is a data cataloging and management platform that helps organizations maximize the value of their data.
OpenMetadata is an open platform for metadata management that provides a centralized solution for data lineage and governance.