Centralized Data Repository Setup for a US-based Retail Giant

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Our Client

  • A major US-based retailer with a nationwide presence, offering a unique range of consumer products, including groceries, electronics, and household items.
  • With hundreds of brick-and-mortar locations and an extensive online marketplace, the company serves millions of customers nationwide.

Problem Statement

As their business grew, the company faced significant challenges in managing the enormous volume of data generated from both their brick-and-mortar locations and e-commerce platforms. During our discussions with their internal team, they highlighted these issues:

    • The retailer’s outdated systems kept sales, customer, and inventory data in separate silos, preventing a unified view of business operations.

    • Their inability to track real-time sales trends and customer buying patterns led to missed opportunities for targeted promotions and demand forecasting.

    • Handling the growing volume of transactional data from over 500 physical stores and the e-commerce platform caused delays in inventory updates and stock management decisions.

Our Solution

To help our client overcome their challenges, we implemented a customized data solution to optimize our client’s operations. Here are some of the things we did for them:
Centralized Data Warehouse

    • Our analytics team consolidated data from e-commerce, CRM, and inventory systems into a unified warehouse, eliminating data silos and providing a single source of information.

Retail Data Sources

    • We developed ETL processes to integrate data from in-store, online, CRM, and supply chain systems, ensuring real-time, consistent data flow.

Power BI for Automated Reporting

    • Implemented a custom Power BI dashboard for real-time reporting, replacing manual processes and accelerating decisions with timely insights.

Predictive Analytics

    • Our deployed experts used predictive analytics to forecast demand, reduce stock-outs and overstocks, and set up alerts for real-time inventory tracking.

Customer Engagement Insights

    • Integrated loyalty program data to analyze customer preferences, enabling personalized promotions and improving customer retention.

Technology Implementation

Our team built a strong data foundation using the latest technology to gather, process, and protect our client’s data. Here are the main parts of our solution:

  • Data Ingestion and Integration Layer:  We employed Azure Data Factory to automate the extraction, transformation, and loading (ETL) of data from various sources, including e-commerce platforms (Shopify), CRM systems (Salesforce, Microsoft Dynamics), inventory systems (SAP, Oracle SCM), and loyalty programs.
  • Data Storage and Processing Layer:  Azure Data Lake Storage served as the repository for raw data from diverse sources, while Azure Synapse Analytics acted as the central data warehouse.
    We established a unified data model and created separate data marts for sales, inventory, and customer loyalty, ensuring data was readily available for analysis.
  • Data Transformation Layer:  Leveraging SQL Server Integration Services (SSIS) and Synapse Pipelines, we transformed raw data to ensure its cleanliness, normalization, and enrichment with external data (Nielsen, IRI).
  • Data Analytics and Visualization Layer:  Custom Power BI dashboard was designed for on-spot reporting and analysis. It helped visualize sales trends, inventory levels, customer behavior, and market insights, offering a comprehensive overview for business managers.
  • Data Governance and Security Layer:  To safeguard data security, we implemented Azure Role-Based Access Control (RBAC) for managing user permissions, Azure Data Catalog for compliance, and Azure Key Vault for encrypting data at rest and in transit, protecting against breaches.

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Business Impact

    • We improved data accessibility by implementing a centralized data warehouse, cutting report generation time from 3 days to just a few hours and providing a unified view of customer interactions and sales performance.

    • Streamlined ETL processes allowed the integration of data from various sources in less than a day, compared to previous week-long delays, reducing data fragmentation.

    • Automated reporting improved decision-making speed, reducing the time to access critical reports from 2 days to under 1 hour, enabling faster market responses.

    • Predictive analytics and real-time inventory tracking reduced stock-outs and overstocks by 20%, leading to a 15% improvement in product availability and efficiency.

    • Integrated loyalty program data and market research enabled deeper customer insights, driving a 10% increase in repeat customer purchases through targeted marketing.

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