Data Quality & MDM : 4-Wk Pilot implementation

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.

Implementation Plan: The break-up of the implementation plan is as below:

1. Week 1: Spent on ‘Discovery’ to understand the business and data. ‘Setup’ Sancus in the client environment and ‘Run’ the default algorithm without any modifications.
2. Week 2-3: Review intermediate output and modify the ML algorithm as per the nuances of the given data.
3. Week 4: Review final output, measure accuracy, create golden record & share results.

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.