A DataOps framework is a set of guidelines and best practices that an organization can use to implement DataOps. The framework outlines the process and tools that are needed to manage and operate data pipelines and analytics in an effective and efficient way. The framework is designed to help organizations to improve the speed, quality, and reliability of data-driven decisions.

A typical DataOps framework includes the following components:

1. Data Governance: Establishing policies, procedures, and roles for managing and controlling access to data.
2. Data Quality Management: Ensuring the accuracy, completeness, and consistency of data.
3. Data Profiling and Cataloging: Documenting the characteristics of data, such as its structure and content, and making that information available to the organization.
4. Data Lineage and Monitoring: Tracking the flow of data through the organization, and monitoring data quality and performance.
5. Data Automation: Automating data pipelines, testing, and validation to improve the speed and reliability of data-driven decisions.
6. Continuous Integration and Delivery: Managing and automating the process of integrating and delivering data changes and updates.
7. Data Security: Ensuring the confidentiality, integrity, and availability of data.
8. Data Literacy: Providing training and resources to help team members to understand and work with data.

The specifics of a DataOps framework will vary depending on the organization and its specific needs, but the goal is always to support the efficient, accurate and secure data management and to enable data-driven decision making.