The Semantic Data Share: AI-Enabled General Ledger Financial Analytics Powered by Snowflake Cortex

# **Executive Overview**

The Dataplatr Financial Analytics Accelerator for Oracle Financials General Ledger is delivered as a Snowflake Data Share — a ready-to-consume, AI-ready analytical foundation built natively on the Snowflake Data Cloud. It bridges the gap between complex Oracle Financials GL data and actionable business intelligence through high-fidelity semantic views that integrate core components like GL Balances, Journal Details, and Code Combinations. By unifying the GL Ledgers and GL Periods into a cohesive temporal framework, the accelerator provides a synchronized view of fiscal health across diverse accounting cycles and global entities.<br/><br/>Snowflake Semantic Views capture the metadata required for consistent and accurate AI-powered analytics, such as synonyms, sample values, and verified queries. These semantic views translate raw Oracle GL tables and Customer Account sub-ledger details into structured, self-describing models that Snowflake Cortex Analyst can navigate using natural language — eliminating the need for custom SQL, manual joins, or bespoke data pipelines. This integration ensures that the GL Code Combinations act as a dynamic bridge, allowing users to slice financial performance by cost center, department, or legal entity with zero manual mapping.<br/><br/>Finance and analytics teams can immediately query period balances, journal activity, year-over-year variance, and ledger performance through plain English questions. By contextualizing transactional data with the underlying Ledger configurations and Period hierarchies, these semantic views transform raw Oracle GL data into a structured, AI-ready foundation that accelerates time-to-insight, enhances auditability from balance to customer-level detail, and supports enterprise-wide financial decision-making.

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# Business Use Cases

**Example 1 -** <br/>"Provide a year-over-year analysis of our ending balances for the first quarter. Compare Jan, Feb, and March of 2026 against the same months in 2025. I want to see the account code and account description alongside the total ending balance in USD. Calculate the variance between the two years to show which accounts have the highest growth. Include a bar chart showing the comparison for these three months."

**Example 2 -** <br/>"Analyze the monthly net movement in ledger currency for the last 12 accounting periods. Group the results by business segment and period name. I need to see how the total movement fluctuates month-to-month so we can identify seasonal peaks. At the end, generate a line chart showing the trend of net movement for our primary business segments for the full year 2025."

**Example 3 -** <br/>"Identifies which customers generated the most revenue in USD during a specific period."

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# **Why Choose the Dataplatr Financial Analytics Accelerator**

## **Finance-Aware Data Architecture**

The GL Balances Agent is built on a specialised foundation for complex Oracle Financials General Ledger logic. It delivers a high-fidelity schema that inherently reflects the logical relationships between GL Balances, Journal Details, Ledgers, Code Combinations, and Periods — ensuring the data is AI-ready for immediate use within the Snowflake ecosystem.

## **Seamless Integration & Bespoke Customisation**

Dataplatr specialises in tailoring these data assets to your specific Oracle Financials environment. We excel at extending the GL Balances Agent with external silos — such as CRM, Treasury, or Payroll — to create a unified, 360-degree business view. This schema can be customized by Dataplatr to align with your unique industry logic and reporting requirements.

## **AI-First Design for Snowflake Cortex**

The GL Balances Agent provides the semantic clarity needed for Snowflake Cortex Analyst to accurately navigate financial data. Our configuration-driven design allows for the addition of new dimensions or tables without heavy data engineering. Dataplatr can further extend this foundation to facilitate rapid expansion into Accounts Payable, Accounts Receivable, and Fixed Assets.

## **End-to-End Financial Visibility**

The GL Balances Agent provides the clean, structured foundation required for a connected view of the entire finance function. The architecture supports integration across Accounts Payable, Accounts Receivable, and Procurement — providing the audit-ready transparency necessary for enterprise-wide automation and financial control.

## **The Dataplatr Advantage**

The Dataplatr Financial Analytics Accelerator provides a ready-to-deploy, AI-ready foundation on Snowflake. By combining Agentic ELT automation with Dataplatr's deep expertise in Oracle Financials customisation, we help enterprises modernise ERP workflows and achieve faster time-to-insight. Whether you are reconciling multi-ledger balances or predicting next quarter's journal activity, Dataplatr ensures your financial data is clean, connected, and intelligent.<br/>

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