Infor Data Lake: Analytics Setup and Optimization Guide
Infor Data Lake is the analytics backbone of the Infor OS platform, aggregating data from all connected CloudSuite applications into a queryable data store. When properly configured, Data Lake enables cross-application reporting, trend analysis, and predictive analytics that are impossible within individual application silos. However, many organizations struggle with Data Lake setup—data ingestion issues, slow query performance, and difficulty building meaningful reports are common complaints on user forums.
Data Ingestion and Quality
Data Lake receives data through ION message flows, typically as BOD replications from connected applications. The quality of your analytics depends entirely on the quality of ingested data. Common issues include incomplete data replication (not all entities are replicated by default), stale data due to failed ION flows, and inconsistent data formats across applications that make cross-system reporting unreliable.
- Audit default data replication settings—many important entities are not replicated out of the box
- Configure ION flow monitoring to detect and alert on replication failures within minutes
- Standardize date formats, currency handling, and unit of measure across all source applications
- Implement data quality rules that flag inconsistencies before they reach analytics consumers
Report Design and Performance
Building effective Data Lake reports requires understanding the underlying data model, which mirrors the BOD structures from source applications. Star schema views are available for common reporting scenarios, but custom reports often require joining across multiple data objects. Query performance degrades rapidly with poorly designed joins and missing indexes—a report that runs in seconds on test data can take minutes on production volumes.
- Use pre-built star schema views for standard KPIs before creating custom data objects
- Design reports with filters that limit data scope—avoid full-table scans on large entities
- Schedule data-intensive reports for off-peak hours to avoid impacting interactive query performance
- Partition historical data by period to improve query performance on time-based analytics
AI-Enhanced Analytics
Netray's AI agents optimize Data Lake configurations by analyzing query patterns, identifying slow-performing reports, and recommending indexing and partitioning strategies. The agents also build predictive models on top of Data Lake data, turning your historical ERP data into forward-looking business intelligence.
Unlock the analytics potential of your Infor Data Lake—schedule an optimization assessment.
Related Resources
Infor OS Platform: Architecture and Capabilities Deep Dive
Technical deep dive into Infor OS platform architecture. ION, Ming.le, Data Lake, Coleman AI, and how they integrate with CloudSuite applications.
ERPInfor Birst Analytics: Integration and Configuration Guide
Configure Infor Birst analytics for CloudSuite applications. Data connections, dashboard design, embedded analytics, and performance optimization.
ERPInfor ION Connect: Integration Configuration Guide
Configure Infor ION Connect integrations. BOD mapping, connection points, workflow automation, and troubleshooting common integration failures.