Establish a single data access layer for the use cases requiring the data from multiple sources which may be distributed across on-premise and Cloud platforms.

Use cases for data virtualization include:

• Data Integration - Combining data from different systems to support analytics, reporting and decision-making

• Data Governance - Enforcing data quality, security, and compliance standards across heterogenous data sources

• Cloud Integration - Connecting on-premises data sources with cloud-based applications and services

Essential components of a data virtualization solution:

1. Data Catalog - centralized repository for storing and managing data

2. Data Quality Management

• Data cleansing - identified and corrects errors, inconsistencies, and duplicates in data
• Data profiling - analyses data to understand its characteristics and quality

3. Integration with existing systems - provides connectors for integrating with various data sources, including databases, cloud applications etc.

4. Security and Governance - Access controls and audit tracking