Working time:
2025 – ongoing
Industry:
Retail
The service:
Data Architecture
Overview
The client is a global omnichannel retail company operating in over 20 countries, with a strong focus on home goods, furniture, and lifestyle products. With both a mature brick-and-mortar footprint and an expanding digital commerce division, the client serves millions of customers annually across various markets.
Facing increasing competition from online-native brands and changing consumer expectations, the company embarked on a strategic initiative to modernize its data infrastructure. The goal was clear: to enable faster, smarter decision-making across merchandising, inventory, supply chain, and customer experience by consolidating fragmented data sources into a modern, scalable, AWS-native architecture.
We partnered with the client to design and implement a robust data platform on AWS, delivering operational efficiency, real-time analytics, and the foundations for future AI/ML adoption across the business.
Challenge
Retailers today operate in a data-rich but insight-poor environment, and our client was no exception. Over time, their legacy systems became bottlenecks to innovation. Key challenges included:
Disconnected Data Silos
Customer behavior, inventory, logistics, and sales data lived in different systems—ERP, POS, eCommerce platform, and third-party tools—limiting end-to-end visibility and hampering unified analytics.
Batch-Oriented Reporting
Data pipelines were predominantly batch-based, with daily or weekly refresh cycles. This made real-time decisions—such as price adjustments, stock reallocation, or demand forecasting—nearly impossible.
High Cost of On-Prem Infrastructure
The on-premise Hadoop-based data warehouse demanded constant maintenance and costly upgrades. Performance issues and scaling limitations led to data latency and frustration among business teams.
Data Governance & Compliance Gaps
Lack of centralized policies made it difficult to track lineage, apply access controls, or maintain compliance with data regulations across regions—especially important given their multinational presence.
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Solution
We helped the client reimagine their data foundation as a cloud-native ecosystem on AWS. The new architecture was designed to be modular, secure, and extensible—ready to power retail operations at scale. Key pillars of the solution included:
Lakehouse Architecture with Amazon S3 + AWS Glue + Amazon Athena
We migrated the client’s fragmented data assets into an Amazon S3-based data lake, organizing data by domain and business function. AWS Glue was used for metadata cataloging, schema inference, and ETL automation. Analysts and business users now query data ad hoc using Amazon Athena, without needing to wait for engineering teams.
Real-Time Inventory and Sales Pipelines with Amazon Kinesis
We deployed streaming ingestion pipelines via Amazon Kinesis to capture sales and inventory transactions in real time across online and in-store systems. This allowed the client to monitor SKU-level performance, stock-outs, and sales spikes as they happened.
Unified Customer 360 Profiles in Amazon Redshift
We implemented a Redshift-based analytical data warehouse to create unified customer profiles across loyalty programs, online behavior, and offline transactions. This enabled deeper segmentation and journey analysis to drive personalized marketing and in-store experiences.
Fine-Grained Access Controls with Lake Formation
To address compliance and governance challenges, we leveraged AWS Lake Formation to define and enforce data access policies by geography, team role, and data classification. This ensured that sensitive customer data could be accessed only by authorized users and systems.
Cost Visibility and Lifecycle Management
Using S3 storage class transitions and automated lifecycle policies, we helped the client optimize storage costs while maintaining high data availability for frequently accessed datasets. Combined with AWS Cost Explorer and tagging strategies, this brought a new level of transparency to data platform costs.
Blog
5 min to read
Data Infrastructure 101: Building for Scalability and Security
Outcomes
The transformation enabled the client to shift from reactive decision-making to proactive, data-driven operations. Key results include:
90% Reduction in Time-to-Insight
Previously, business reports could take days to generate due to fragmented pipelines. Now, cross-domain dashboards are refreshed hourly or even in real time, dramatically increasing the business’s responsiveness to customer trends and supply chain issues.
60% Lower Data Infrastructure Costs
By decommissioning legacy on-prem systems and adopting scalable AWS services, the client reduced their total cost of ownership for data infrastructure by over half. These savings were reinvested into strategic analytics and personalization initiatives.
Global Inventory Visibility
The real-time streaming data pipelines gave planners and merchandisers live visibility into stock movement across regions. This enabled dynamic reallocation of inventory, reduced overstock situations, and improved order fulfillment during peak seasons.
Marketing ROI Uplift
With unified customer profiles in Redshift, the client launched targeted email and in-app campaigns based on real-time behavioral signals—resulting in a 23% lift in campaign conversion rates and a measurable improvement in average order value.
Foundation for AI & ML Expansion
The clean, governed data platform is now used to support experimentation in GenAI and ML use cases, including dynamic pricing models, personalized search, and even in-store heatmap analysis. The client now has the architectural flexibility to innovate without friction.
