Metadata management in data warehousing plays a crucial role in facilitating data integration and consolidation. It provides a centralized repository of information about the data, making it easier to understand, access, and integrate data from different sources.

Operational metadata is not a type of metadata commonly used in data warehousing. Technical metadata, business metadata, and structural metadata are the three main types of metadata used in data warehousing.

A centralized metadata repository simplifies data integration and consolidation by providing a single source of truth for all metadata related to the data warehouse. This makes it easier to understand, access, and integrate data from different sources.

Data inconsistency and redundancy, lack of metadata standardization, and difficulty in maintaining metadata over time are all common challenges associated with metadata management in data warehousing.

Data lineage in data warehousing is primarily used to track the movement of data through the data warehouse. This information is valuable for understanding the data's provenance, identifying potential data quality issues, and facilitating impact analysis.

Data integration tools are not typically used for metadata management in data warehousing. Data profiling tools, data quality tools, and data governance tools are commonly used for this purpose.

A metadata-driven approach to data warehousing simplifies data integration and consolidation by providing a centralized repository of metadata that can be used to understand, access, and integrate data from different sources.

Centralizing metadata in a single repository, standardizing metadata definitions and formats, and automating metadata collection and maintenance are all common best practices for metadata management in data warehousing.

Metadata governance in data warehousing encompasses all aspects of metadata management, including ensuring the accuracy and consistency of metadata, defining and enforcing metadata standards, and managing the lifecycle of metadata.

Lack of executive sponsorship, insufficient resources, and resistance to change are all common challenges associated with metadata governance in data warehousing.