Major Canadian Retailer
Business objective & project requirements
Aiming to overcome data challenges resulting from running several operations within the company and handling large amounts of associated customer data, the retailer required a comprehensive, 360-degree customer view to support robust marketing campaigns, analytics, and reporting. The project also included fraud protection in the context of their banking service.
Project requirements:
- A comprehensive enterprise customer data integration solution with support for multiple master layers for the same data, including party and household data layers.
- Compatibility with future plans for graph databases and Hadoop deployment.
Initial data challenges
- Multiple retail and service operations within the company, each with their own associated customer data
- 8 separate source systems for data storage, 100+ million records
- Duplicate records for individual customers registered in more than one instance, such as when using a company-branded credit card, an in-store transaction, online interactions, or membership in a loyalty program that has run for several decades
- A legacy MDM system with 800+ highly-redundant matching rules
Solution phases & benefits
Ataccama designed and implemented the retailer’s customer data integration solution, integrating 100+ million records in 8 separate source systems. We improved the retailer’s data quality with cleansing and standardization, and performed data mastering and consolidation. This included high quality deterministic matching, fuzzy algorithms, machine learning algorithms, and interpretation. Thanks to improved data quality of mastered data, the client was able to reduce the number of matching rules from more than 800 to 300, resulting in 60% faster processing and reducing compute costs.
Beginning with 3 source systems, the retailer used this foundation to easily integrate 5 additional source systems without assistance from Ataccama through a straightforward process that reused existing configurations.
Ataccama’s solution dramatically improved the retailer’s customer analytics and marketing campaign management, and the company plans to implement support for real-time customer interactions and decision management on Hadoop.
Solution use cases
- Integrating live data quality processes to ETL and data movements across sources
- Data masking within an enterprise data warehouse
- Data cleansing and standardization services across the organization
- A foundation for the company’s enterprise customer data integration solution
- Supporting analytical activities and campaign management
Business objective & project requirements
Aiming to overcome data challenges resulting from running several operations within the company and handling large amounts of associated customer data, the retailer required a comprehensive, 360-degree customer view to support robust marketing campaigns, analytics, and reporting. The project also included fraud protection in the context of their banking service.
Project requirements:
- A comprehensive enterprise customer data integration solution with support for multiple master layers for the same data, including party and household data layers.
- Compatibility with future plans for graph databases and Hadoop deployment.
Initial data challenges
- Multiple retail and service operations within the company, each with their own associated customer data
- 8 separate source systems for data storage, 100+ million records
- Duplicate records for individual customers registered in more than one instance, such as when using a company-branded credit card, an in-store transaction, online interactions, or membership in a loyalty program that has run for several decades
- A legacy MDM system with 800+ highly-redundant matching rules
Solution phases & benefits
Ataccama designed and implemented the retailer’s customer data integration solution, integrating 100+ million records in 8 separate source systems. We improved the retailer’s data quality with cleansing and standardization, and performed data mastering and consolidation. This included high quality deterministic matching, fuzzy algorithms, machine learning algorithms, and interpretation. Thanks to improved data quality of mastered data, the client was able to reduce the number of matching rules from more than 800 to 300, resulting in 60% faster processing and reducing compute costs.
Beginning with 3 source systems, the retailer used this foundation to easily integrate 5 additional source systems without assistance from Ataccama through a straightforward process that reused existing configurations.
Ataccama’s solution dramatically improved the retailer’s customer analytics and marketing campaign management, and the company plans to implement support for real-time customer interactions and decision management on Hadoop.
Solution use cases
- Integrating live data quality processes to ETL and data movements across sources
- Data masking within an enterprise data warehouse
- Data cleansing and standardization services across the organization
- A foundation for the company’s enterprise customer data integration solution
- Supporting analytical activities and campaign management
