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Data validation and duplication removal

Purpose

1.1. Automate the process of validating, normalizing, and deduplicating client intake data for social service applications; ensure data integrity for eligibility, minimize manual correction, and streamline intake and benefits processing.
1.2. Automating this function includes verifying input accuracy, compliance checks, normalization (e.g., address/case formats), and removal of duplicate records before benefits processing.

Trigger Conditions

2.1. Automated trigger upon new submission of social service applications in intake portals, CRM entries, or file uploads (CSV, Excel, PDF).
2.2. Scheduled batch automation for historical or incoming records at designated intervals.
2.3. API webhook from front-end data entry or scanned document ingestion.

Platform Variants

3.1. Salesforce
• Feature/Setting: Data Validation Rules, Duplicate Rules, Record-triggered Flows; automate conditional checks and deduplication cross-object.
3.2. Microsoft Power Automate
• Feature/Setting: Dataflows, Built-in Duplicate Detection, Automated Cloud Flow for new SharePoint/Dataverse entries.
3.3. Zapier
• Feature/Setting: Paths, Filters, Formatter Utilities, Code by Zapier; automate dedupe and validate fields when intake occurs.
3.4. Workato
• Feature/Setting: Data validation recipe actions, deduplication steps, mapping intake apps to backend databases.
3.5. UiPath
• Feature/Setting: Data Scraping, Data Validation Activities, RPA Process for duplicate removal in structured data entries.
3.6. Integromat (Make)
• Feature/Setting: Data Store, Data Structure Validation, Array Aggregator for deduplication in intake process pipelines.
3.7. Google Cloud Dataflow
• Feature/Setting: Automate validation with Dataflow pipelines, use deduplicate transforms on records.
3.8. IBM DataStage
• Feature/Setting: Data Quality Stage for validation, Matching Stage for automated deduplication in intake ETL.
3.9. AWS Glue
• Feature/Setting: FindMatches transform for deduplication, schema validation in ETL jobs.
3.10. Oracle Data Integrator
• Feature/Setting: Data Validation Rules, Duplicate Check Integration, automated process tasks.
3.11. Informatica Cloud Data Quality
• Feature/Setting: Automated Data Quality Assessments, Deduplication Rules for social services applicants.
3.12. Talend Data Preparation
• Feature/Setting: Validation Processors, Deduplication Pipelines for intake batch jobs.
3.13. SAP Data Services
• Feature/Setting: Validation Transforms, Matching and Deduplication Transforms, automate intake master data quality.
3.14. Smartsheet
• Feature/Setting: Automated workflows with cell linking, automated duplicate detection via scripts.
3.15. Google Apps Script
• Feature/Setting: Automated validation and deduplication using custom triggers on Sheets intake forms.
3.16. Alteryx
• Feature/Setting: Data Cleansing Tool, Unique Tool, Formula Tool for automate intake processing.
3.17. DataRobot
• Feature/Setting: Automated Data Preprocessing, Validation Recipes for raw intake data.
3.18. ElasticSearch
• Feature/Setting: Automate deduplication on ingest using unique ID enforcement and validation analyzers.
3.19. Trifacta
• Feature/Setting: Data Wrangling Flow, Automated Deduplication and Validation Steps for incoming files.
3.20. Snowflake Data Cloud
• Feature/Setting: Streams & Tasks, automated SQL validation constraints, deduplication procedures on intake tables.

Benefits

4.1. Automates repetitive manual data checks, reducing staff workload and human error.
4.2. Automatedly enforces compliance and eligibility rules for faster intake and reduced backlogs.
4.3. Automating duplicate detection avoids benefit errors, fraud, and resource misallocation.
4.4. Enables realtime automation for new entries and batch automator for legacy data.
4.5. Rapid scaling via automation without incremental staffing; improved auditability and accuracy.

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