Rakuten Unifies a Fractured Data Landscape on OpenMetadata

Companies that grow through many independent services end up with data scattered across as many catalogs. At Rakuten, each engineering team kept its own catalog in a spreadsheet, a Confluence page, or a bespoke web app, so no one had a shared view of what data existed or how the businesses related, and a data consumer could spend two to three months finding whether a dataset was even useful.

Learn how Rakuten's Data Platform team consolidated discovery, governance, and utilization for one of the world's largest digital ecosystems on OpenMetadata, replacing a patchwork of siloed catalogs with a single group-wide platform and laying the foundation for AI agents. Inside this case study:

• How Rakuten replaced Excel, Confluence, and custom catalog apps with one group-wide platform, applying standard governance consistently across teams with data products, a glossary, and tagging
• How the team expects dataset discovery to drop from two to three months to under an hour, and data utilization to rise from a 30-60% baseline toward 80% as businesses onboard
• How Rakuten is building toward AI on the same foundation, from automatic description generation across thousands of tables to MCP integration so agents can query lineage to debug pipelines