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Functional Specification: Data Entry Intelligence Service

Version: 1.0 Date: October 17, 2025 Status: Final Draft

1.0 Executive Summary​

The Data Entry Intelligence Service is a critical quality gate that processes all incoming data into ChainAlign, regardless of source (SAP, CSV upload, API push, live connection). It performs intelligent validation, quality checks, anomaly detection, schema mapping, metadata generation, and insight extraction before data enters the core schema.

Purpose: To ensure data quality, completeness, and actionability while identifying gaps, anomalies, and questions for the Data Workbench, thereby creating a robust and trustworthy foundation for all S&OP processes.

2.0 Architecture Overview​

2.1 Core Principles​

  1. Universal Entry Point: All data must flow through this service.
  2. Non-Blocking by Default: Quality issues do not block ingestion; they flag the data for review in the Data Workbench.
  3. AI-Powered Intelligence: Uses LLMs for schema mapping, anomaly detection, and insight generation.
  4. Multi-Source Support: Handles SAP, CSV, API, and live connections uniformly.
  5. Feedback Loop: Learns from user corrections and validations to improve over time.

2.2 Service Flow​

3.0 Component Specifications​

3.1 Component 1: Source Detection & Classification​

Purpose: Identify data source type and apply appropriate processing rules.

  • FR-1.1: The service must identify the source system (e.g., SAP_ECC, CSV_Upload, API_Push).
  • FR-1.2: It must use a combination of metadata, header analysis, and pattern matching to classify the source with a confidence score.

3.2 Component 2: Intelligent Schema Mapping​

Purpose: Map external fields to ChainAlign's core schema intelligently.

  • FR-2.1: The service must use pre-configured mappings for known standard systems (e.g., SAP MARA table).
  • FR-2.2: For custom or unknown sources (Z-tables, arbitrary CSVs), it must use an LLM to suggest mappings between source fields and the ChainAlign schema, providing a confidence score for each suggestion.
  • FR-2.3: The service must identify fields that cannot be mapped and flag them for review.

3.3 Component 3: Data Quality Validation​

Purpose: Check data completeness, consistency, and validity at field, row, and dataset levels.

  • FR-3.1 (Field-Level): The service must perform type checking, range validation, format validation, and detect suspicious values (e.g., negative inventory).
  • FR-3.2 (Row-Level): The service must check for missing required fields, referential integrity violations, and business logic violations (e.g., in_stock = false but on_hand_quantity > 0).
  • FR-3.3 (Dataset-Level): The service must analyze the entire dataset for missing data patterns, statistical anomalies, and temporal gaps in time-series data.

3.4 Component 4: Anomaly Detection​

Purpose: Identify unusual patterns using statistical and ML techniques.

  • FR-4.1 (Statistical): The service must detect outliers using standard methods (e.g., IQR) and identify trend breaks in time-series data.
  • FR-4.2 (Business Logic): The service must detect business-specific anomalies, such as stale inventory or demand spikes exceeding 3x the historical average.
  • FR-4.3 (Pattern-Based): The service may use an LLM to identify unusual patterns, placeholder values (e.g., "N/A"), or inconsistent formatting.

3.5 Component 5: Insight & Question Generation​

Purpose: Generate actionable insights and intelligent questions for the Data Workbench.

  • FR-5.1 (Insight Extraction): The service must generate data profile insights (e.g., null percentages, value distribution) and cross-field insights (e.g., low correlation between revenue and demand).
  • FR-5.2 (Question Generation): The service must generate clarification questions for unmapped fields and anomalies, and business context questions to better understand the data.

3.6 Component 6: Metadata & Gap Identification​

Purpose: Auto-generate metadata relationships and identify missing data.

  • FR-6.1 (Metadata): The service must attempt to detect foreign key relationships and hierarchies to build metadata connections.
  • FR-6.2 (Gaps): The service must identify missing required fields (based on the Data Catalog) and calculate a completeness score for each record.

4.0 API & Database Design​

4.1 API Endpoint​

  • POST /api/data-entry/process: The single, universal entry point for all incoming data.
    • Request Body: { tenantId, rawData, sourceMetadata, entryMode }
    • Success Response (200 OK): { success, entry_id, quality_score, needs_review, workbench_task_id }

4.2 Database Schema​

The following tables will be created to support this service. (Note: These migrations have already been created by Pramod as of 2025-10-17).

  1. data_entry_records: Tracks all data import jobs and their summary status.
  2. data_quality_reports: Stores the detailed JSON output of all validation, anomaly, and insight checks for a given entry.
  3. data_staging: A temporary holding area for data that needs_review.
  4. schema_mappings: Stores confirmed and AI-suggested mappings to improve future performance.
  5. data_workbench_tasks: Stores the tasks generated for the UI, including the questions that need to be answered.

5.0 Integration & Workflow​

  • CSV Upload: The existing CSV upload flow will be rerouted to call the DataEntryIntelligenceService.
  • ERP/API Sync: All data connectors will be refactored to push their extracted data through this service.
  • Data Workbench: If needs_review is true, a task is created in the Data Workbench, linking to the staged data and the quality report.
  • Learning Loop: When a user confirms a mapping or resolves an anomaly in the workbench, the feedback is sent back to this service to update the schema_mappings table and potentially refine detection models.

6.0 Success Criteria​

  1. Coverage: 100% of incoming data from all sources flows through the quality gate.
  2. Accuracy: AI-suggested schema mappings achieve >80% user confirmation rate.
  3. Actionability: >90% of critical data quality issues are automatically flagged and presented as tasks in the Data Workbench.
  4. Efficiency: Reduces the manual data cleaning and validation time for new customer onboarding by at least 50%.