Data Governance Practices and Implementation at DataCake

DataCake faces growing data volumes and fragmented departmental pipelines that create data silos, high communication costs, and unclear cost accounting, prompting the need for a comprehensive data governance framework.

The team identified four key steps: recognizing problems and challenges, defining a strategic positioning, designing solutions and implementation, and summarizing future plans.

Strategically, they emphasized building a unified metadata management platform, a low‑threshold governance workbench, and a detailed cost‑analysis tool to give developers, managers, and operators clear visibility and control over data assets.

Implementation involved a hybrid‑cloud architecture with layers for infrastructure, platform/tools, data collection, data warehouse, and backend services, supporting modules for metadata management, governance workbench, cost analysis, and asset inventory, each providing observability, automation, and cost tracking.

The governance platform now offers fine‑grained metadata observation, automated governance actions, and comprehensive cost analysis, resulting in 60% employee participation, a 25% increase in compute resource utilization, and the release of 3.5 PB of storage.

Future directions include refining existing features, enhancing user experience, and adopting industry best practices, followed by a Q&A covering evaluation metrics, execution processes, cloud‑native challenges, lineage analysis, and cost allocation mechanisms.