Global Retail Chain Seeks Legacy Migration and Operationalization of Modern Analytics Platform on AWS
Trianz was approached by a global retail chain that processes billions of yearly transactions. To improve retail operations, the client needed help with modernizing its legacy systems to acquire advanced analytics insights capabilities. The retail chain would use the insights from its new omnichannel sales and marketing analytics and customer 360 analytics to improve customer loyalty and increase digital sales.
The Business Challenge for the Global Retail Chain
The retail chain lacked the ability to enable machine learning use cases to help them better understand customer behavior and form personalized marketing campaigns. To improve customer loyalty, increase footfall, and personalize digital campaigns with new product launches, the client needed to migrate its legacy data and analytics platform to a modern cloud architecture on AWS. 
Technology Components Used in the Migration Strategy
After analyzing the client's current state data and analytics legacy landscape, Trianz defined the target state architecture using the following technology components:
AWS API Gateway was used to act as a reverse proxy to accept all API calls, aggregate the various services required to fulfill them, and return the appropriate result.
AWS Lambda was introduced to enable serverless, event-driven compute workloads.
AWS Glue was selected as a serverless extract-transform-load (ETL) solution for data crawling, data catalogs, and data transformation workflows.
Python/Spark was the programming language chosen to enable machine learning and real-time streaming analytics.
Amazon EMR was used to simplify running big data frameworks and process and analyze vast amounts of data.
AWS Simple Storage Service (S3) was chosen as a scalable and configurable data storage platform.
Amazon Redshift was chosen as the cloud data warehouse to store structured and unstructured data.
Snowflakewas the cloud-agnostic SaaS solution chosen for query optimization.
Tableauwas chosen as a data visualization and business intelligence platform to create more valuable insights by converting raw data into visual dashboards.
Power BI was used to facilitate both regular reporting requirements and ad-hoc analytics.
The Trianz Approach to Legacy Data Migration
Trianz' consultants sat down with the retail chain's leadership to understand their unique business needs and challenges before collaborating with AWS to enable the modern architecture in an incremental fashion per the defined priorities.
After a thorough analysis of the existing architecture, Trianz chose to leverage Evove — our proprietary tool and methodology that utilizes high levels of automation and reusable components to drive accelerated and high-accuracy migrations of legacy data. This accelerated the migration of the client’s legacy data, database, ETL, and BI layers from the current state to target state architecture on AWS.
Once the cloud architecture was ready as per AWS well-architected framework, Trianz got to work accelerating the implementation of use cases for machine data. This enabled the retail chain to integrate digital sales data in real-time, organize data for self-service and machine learning, and deliver multiple data products/analytics to its sales and marketing teams.
Transformational Effects After Building a Modern Analytics Platform on AWS
With advanced data and analytics capabilities, the global retail chain was able to refine market basket analysis to analyze purchases that happen together. This enabled them to identify relationships between items in the target of the analysis, allowing them to improve customer segmentation and enhance digital marketing efforts.
Through machine learning and marketing automation, the client was able to optimize product offerings, resulting in an increase in digital sales and higher customer satisfaction and loyalty.
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