The objective is to cleanse and consolidate customer, vendor, and contact master data using our ML model that is easily scalable across markets and regions to enhance the quality of derived business insights.

Sancus DQM can address the following challenges:

1. Duplicate entries for customer or contact data

2. Multiple records that have partial information that reference the same customer or contact

3. Inconsistent values across different sources for the same customer or contact record

4. The existing de-duplication and cleanse process is extremely time-consuming.

5. The existing process is purely rule-based and does not scale.

How do we address your challenges?

1. A combination of ML-based algorithms for data cleansing and de-duplication coupled with a rule-based approach to address corner cases

2. Ability to enrich the data through API hooks

3. Use 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

Highlights:

1. Go-To-Market in 6-8 weeks

2. Master data engine creation suitable for various use cases

3. Auto-scale on Azure Databricks

AI/ML-driven Sancus (Data Quality Management) Solution by Tredence

Автор: Tredence Inc