020 - Enterprise Data Governance

CSL's mission is to improve health and target rare diseases, so the organization has to use its data efficiently. That depends on data that is reliable, high quality, and easy to access. This project produces clean, trusted, and well-owned data so the company can adopt AI safely and at scale. It does so by governing data where it lives and publishing it through the Informatica data catalog, not by moving it into one place.

Broadly, the organization faces the following core data challenges:
1. Data Fragmentation
2. Lack of Data Reliability
3. Ambiguous Ownership and Accountability
4. Administrative and Governance Deficits

According to the project abstract provided by the client,
• Data is owned, defined, and maintained differently across functions
• Governance relies heavily on manual spreadsheets, task trackers, and slide-based reporting
• Data quality monitoring is largely reactive
• Adoption of standards requires ongoing reinforcement and visibility
• Dashboards show “health” but lack automation and feedback loops that drive action

These challenges are process centric, requiring solutions that blend automation and usability.

Proposed Solution
A data architecture can be established to oversee various hierarchies of data and define access levels. As suggested by stakeholders, a comprehensive Data Governance Dashboard will be a key solution to provide administrative control and visibility over the company’s data, equipped with automation and feedback loops for data governance and management.

Instead of moving all files, the solution keeps source data in its native environment (databases, datasets, and cloud CRM tools) and addresses the problem through four key pillars:

1. Establish an Appropriate Data Architecture to Enforce with Existing Decentralized Data
2. Centralized Visibility via a Data Governance Dashboard
3. Formalized Ownership and Stewardship Workflows
4. Enforce Role-Based Access Controls Over Data