Ingest SFCC B2C Data into Tableau with S3 Storage and Data lake Access

Idea Description: Ingest SFCC B2C Data into Tableau with S3 Storage and Datalake Access

We propose building a direct data pipeline from Salesforce B2C Commerce Cloud (SFCC B2C) into Tableau, with all ingested data also stored in an Amazon S3 bucket for long-term retention and access via the enterprise datalake. This integration would extract structured data from SFCC’s subject areas—including Sales, Product, Search, Technical Profiler, and Traffic data—using Salesforce APIs or Tableau connectors (https://help.salesforce.com/s/articleView?id=cc.b2c_commerce_cloud_data_subject_areas.htm&type=5).

The pipeline would:

• Ingest SFCC data into Tableau for real-time visualization and business insights.

• Persist raw and transformed data in S3 for historical analysis, backup, and downstream processing.

• Register datasets in the datalake catalog for cross-platform analytics and governance.

Business Value

1. Cross-Platform Data Reconciliation
   By centralizing SFCC data in Tableau and the datalake, teams can compare it with data from Google Analytics 4 (GA4), Oracle, and DOMS to identify discrepancies in sales attribution, traffic behavior, and fulfillment metrics:

   • GA4 may show high traffic but low conversions—SFCC search logs can reveal friction points.

   • Oracle may report revenue differently due to tax or currency logic—SFCC provides raw order data.

   • DOMS may show fulfillment delays—SFCC profiler data can pinpoint checkout or routing issues.

2. Permanent, Queryable Storage
   Storing SFCC data in S3 ensures long-term access for historical trend analysis, auditability, and machine learning use cases. It also enables integration with Athena, Redshift Spectrum, or other datalake consumers.

3. Self-Service Insights in Tableau
   Business users across eCommerce, marketing, and product teams can explore curated dashboards in Tableau without relying on engineering for ad hoc reports. This accelerates decision-making and improves agility.

4. Operational and Technical Optimization
   Technical profiler data (e.g., OCAPI latency, controller performance) helps DevOps teams monitor backend health and correlate performance issues with business impact.

5. Improved Customer Experience
   Search and traffic data help identify gaps in product discoverability and navigation, enabling UX and merchandising teams to optimize the storefront experience.

6. Strategic Alignment
   This approach aligns with our enterprise data strategy by:

   • Leveraging Tableau as the standard BI tool.

   • Using S3 and the datalake for governed, scalable data storage.

   • Enabling cross-functional teams to work from a single source of truth.