The company highlighted multiple challenges in its data management pipeline when we first sat together for a discussion. Here are the key issues we identified:
Disjointed Data Systems:
The company struggled with fragmented data from various systems, including Shopify for online sales, Salesforce and Microsoft Dynamics for CRM, and SAP/Oracle SCM for inventory. This made it hard to get a unified view of their operations.
Slow Reporting Processes:
Reports took several days to compile due to data being scattered across systems. This delay impacted their ability to make timely and informed decisions.
Inventory Management Issues:
Frequent stock-outs and overstock situations created operational inefficiencies and led to customer dissatisfaction, affecting overall business performance.
Customer Insights Gap:
Data from loyalty and rewards programs was not fully utilized, limiting their understanding of customer preferences and engagement.
Lack of Actionable Visualization:
Without a centralized dashboard, tracking key metrics like sales, inventory levels, and customer behavior was a challenge, hampering real-time decision-making.
Our experts implemented a centralized data warehousing solution integrated with Power BI to address the client’s challenges. Key steps included:
Unified Data Infrastructure: Consolidated data from e-commerce platforms, CRM systems, inventory systems, and loyalty programs into a centralized warehouse.
Data Integration and ETL: Streamlined data ingestion using Azure Data Factory, ensuring real-time synchronization and consistency.
Advanced Reporting: Deployed automated dashboards in Power BI for quick insights into sales, inventory, and customer behavior.
Predictive Analytics: Implemented demand forecasting to optimize inventory levels and reduce inefficiencies.
Secure Governance: Enforced data security using Azure Role-Based Access Control (RBAC) and encryption protocols.
Purpose: Orchestrated and automated ETL (Extract, Transform, Load) processes by connecting to various data sources, transforming the data, and loading it into a centralized data warehouse.
Key Data Sources:
Data Storage and Processing Layer
Purpose: Stored and processed unified data for analysis.
Key Components:
Features:
Data Transformation Layer
Purpose: Prepared data for analysis by ensuring it was clean, consistent, and actionable.
Tools:
SSIS/Synapse Pipelines: Used to remove duplicates, normalize formats, and aggregate data.
Key Steps:
Data Analytics and Visualization Layer
Purpose: Enabled decision-makers to derive actionable insights through dashboards.
Tool: Power BI was used for visualizations and reporting.
Dashboards created for the client:
Data Governance and Security Layer
Purpose: Governed and secured the data while ensuring compliance.
Key Features:
The centralized data warehousing solution transformed the client’s data landscape, enabling real-time analytics and seamless decision-making. With improved reporting, inventory management, and customer insights, the client is now well-equipped to drive operational efficiency and strategic growth.
Retail & E-commerce
North America
End to End Project Lifecycle Management
