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Data Quality & Master Data Management (Sancus) SaaS Offering

di Tredence Inc

AI/ML driven Data Quality Management

Objective:
Cleanse and consolidate Customer, Vendor and Contract master data using our ML model that are easily scalable across markets/regions to enhance the quality of derived Business Insights.

Key Challenges Addressed:
1. Duplicate entries for Customer or Contact data
2. Multiple records that with partial information that reference the same customer / contact
3. Inconsistent values across different sources for the same customer/contact record.
4. Existing de-dup and cleanse process is extremely time consuming.
5. Existing process is purely rule based that does not scale

How do we address your challenges:
1. A combination of ML based algorithm for data cleansing and de-dup coupled with a rule based approach to address corner cases
2. Ability to enrich the data through API hooks
3. Active learning feedback to improve data matching performance
4. A centralized Data Quality dashboard to monitor source data quality metrics
5. Integration with CRM systems like Salesforce for completing the feedback loop to the source system

Pilot Outcome:
1. Consolidated customer and contact master data after removal of duplicates
2. Enhanced Customer, Contact and Vendor data.

The solution is built on native Azure components to intelligently scale using below key components:
1. Azure Storage Account ([ADLS/Blob]): Storage account to store Input, Intermediate files, and consolidated Output.
2. Azure Databricks: Azure Databricks enables the Sancus algorithm to easily scale for large data volumes.

In uno sguardo

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