Our client partnered with First San Francisco Partners (FSFP) to establish and mature an enterprise Data Governance program in order to enable federated domain data ownership and create business context to fuel creation of their AI semantic layer.
About
The organization is one of the world’s largest airlines, operating in the aviation and travel industry. The company provides passenger air transportation, cargo services, and related travel solutions across a global network. With millions of customers and complex operations, the organization relies heavily on data to optimize performance, enhance customer experience, and maintain competitive advantage.
Industry: Airlines / Aviation & Transportation Services
Background
The client engaged FSFP to help establish a comprehensive enterprise Data Governance Program. They recognized that data is a strategic asset and sought to improve data quality, trust, and accessibility to enable better decision-making and operational efficiency. Additionally, they needed to reduce compliance risk, standardize governance practices, and create a foundation for advanced analytics and digital transformation initiatives. Their goal was to implement a program that would scale across the enterprise, align with business priorities, and support future capabilities such as data cataloging and data quality monitoring.
Engagement Type
Enterprise Data Governance, Metadata & Stewardship, Data Quality & Observability, Data Literacy, Privacy & Compliance Frameworks
Opportunity Areas
The client faced challenges common to large, complex organizations:
- Fragmented data ownership and unclear accountability.
- Lack of standardized governance processes across business units.
- Difficulty in prioritizing governance initiatives and enforcing compliance.
- Need for formal governance structures to support enterprise-wide collaboration.
Engagement Snapshot
FSFP partnered with the client to design and implement a scalable Data Governance Program that addressed these challenges through four key workstreams:
Data Governance Operating Model
- Developed and implemented a comprehensive operating model aligned with organizational structure and federated domain-based data ownership.
- Defined roles, responsibilities, and decision rights for governance participants.
- Established escalation paths and accountability frameworks for data decision across the organization.
Governance Groups Enablement
Supported the Data Governance Steering Committee and Leadership Governing Body by:
- Drafting charters and defining scope of authority.
- Facilitating initial meetings and decision-making processes.
- Providing frameworks for prioritization and oversight.
Directives Development and Implementation
Authored and implemented Data Governance Directives (policies, standards, guidelines) including:
- Enterprise Data Governance Policy describing the roles and responsibilities identified in the Data Governance Operating Model. The Data Governance policy and standards align with regulatory and internal compliance requirements across the organization to establish consistency and coordination around data responsibilities.
- Metadata Management Policy and related required metadata documentation standards necessary to support development of an AI semantic layer.
- Data Quality Management Policy, including monitoring, reporting, and issue management standards that assign responsibility to both business and IT stakeholders.
- Data Lifecycle Management Policy and an Enterprise Data Governance Retention Standard, aligned with the organizations records retention policy and designed to enable data archival and removal from hot data storage platforms and reduce data spend.
Data Steward Identification and Onboarding
- Defined the Lead Data Steward and Business Data Steward roles and responsibilities.
- Partnered with business units to identify and onboard stewards for critical data domains.
- Delivered training and enablement materials for effective stewardship.
Outcomes
The program delivered measurable improvements in governance maturity and positioned the client for long-term success in managing data as a strategic asset:
- Improved accountability and transparency for data decisions.
- Enhanced data quality and trust, enabling better analytics and operational efficiency.
- Reduced compliance risk through standardized governance practices.
- Enabled advanced capabilities, such as AI-driven analytics based on certified data assets and the creation of an enterprise AI semantic layer.
- Optimized data storage costs, reducing unnecessary data spend.
- Established a foundation for enterprise-wide governance maturity, supporting future data initiatives.
