Summary
Business Challenge
The retailer’s data was fragmented – POS, e-commerce, ERP, and CRM systems all operated in isolation. Manual data entry led to a 30% error rate, and leadership often debated which report to believe.
Operationally, the company lost an estimated $5 million annually: bestsellers were frequently out of stock, while excess inventory sat unsold, tying up cash and warehouse space. Without real-time insight, purchasing was reactive and promotions were hit-or-miss.
Meanwhile, competitors with sharper digital tools were already delivering personalized experiences and capturing loyal customers. The company needed a unified source of truth and the means to act on it.
Solutions
We began with a structured, phased approach focused on laying a solid data foundation:
Phase 1: Structural and Process Modeling
We applied the IDEF0 methodology to map the organization’s operational landscape. Over 50 core business processes were documented across 6 departments, including Sales, Inventory, Customer Service, and Marketing. This helped clarify data flows, stakeholder roles, and interdependencies, forming the blueprint for future system design.
Phase 2: Building the Data Governance Foundation
A five-step framework was then deployed to establish data as a strategic asset:
1
Identifying Business Needs
Through 20+ stakeholder interviews and workshops, we captured key pain points and translated them into actionable data and analytics requirements.
2
Centralizing the Data
A cloud-based Azure SQL Data Warehouse was implemented, integrating 6 primary systems into one scalable, governed platform.
3
Cataloging and Standardizing
Microsoft Purview was used to catalog over 100 datasets and define ownership, lineage, and terminology, supported by a business glossary of 50+ standard terms.
4
Activating Analytics
Power BI dashboards were tailored for executives, sales, inventory, and marketing teams. With 50+ users trained, real-time, self-service analytics became a core operational tool.
5
Enabling AI
We developed machine learning models, including demand forecasting (LSTM), a personalized recommendation engine, dynamic pricing powered by reinforcement learning, and a customer service chatbot using NLP.
Client Review
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