Enterprise BI & Analytics Platform on Azure for Plant-Based Food Company
Services Covered:

Industry
Food & Beverage
Duration
60 months
Budget
Confidential
Start Date
1 February 2021
Team Size
21-25
Engagement Model
Nearshore
Project Capability Score (PCS)
84/100
Strong
Project Capability Score (PCS) estimates how capable Antdata is of successfully delivering a project like this, based on its past experience, track record, and ability to handle similar work. Learn more about PCS
Client
Confidential
Project Summary
Since February 2021 Antdata has partnered with a plant-based food company to design and operate an enterprise BI and analytics platform. They built a cloud data infrastructure (Azure Synapse Analytics / Microsoft Fabric), integrated ERP and multiple other sources, and created company-wide Power BI dashboards and automation using Power Apps/Power Automate. The platform became a central source of truth with broad adoption, reduced manual reporting, faster data refreshes, lower infrastructure costs, and improved forecast accuracy.
Key Challenges
- Design and implement an enterprise-grade Azure Synapse Analytics architecture integrating ERP systems, internal databases, and external APIs
- Build and maintain a centralized Microsoft Power BI reporting ecosystem covering Finance, Marketing, Sales, Supply Chain, Manufacturing, and ESG
- Develop custom business applications using Power Apps and Power Automate to streamline processes
- Create and deploy machine-learning models for demand forecasting, price elasticity, and marketing ROI analysis
- Implement IoT sensor systems on production lines and deliver real-time Power BI dashboards for production performance tracking
Project Deliverables
- Design and implementation of an enterprise-grade Azure Synapse Analytics architecture (now Microsoft Fabric)
- Integration of multiple data sources (ERP, CRM, marketing, production, ESG systems, external APIs) into a unified governed data platform
- Company-wide Microsoft Power BI reporting suite and interactive dashboards for Finance, Demand Planning, Marketing, Supply Chain, and ESG
- Custom business applications and automation built with Power Apps and Power Automate
- Predictive analytics and machine-learning models for demand forecasting, price elasticity, and marketing ROI
- Implementation of IoT sensor systems and real-time production/Performance dashboards
Project Solution
Antdata designed, implemented, and continuously enhanced a cloud BI and data platform initially based on Azure Synapse Analytics and now Microsoft Fabric. They integrated ERP, CRM, marketing, production, ESG, and external APIs into a governed data environment, developed company-wide Power BI reporting and self-service tools, built Power Apps/Power Automate applications to automate workflows, deployed machine-learning models for forecasting and analytics, and implemented IoT sensor systems for near-real-time production monitoring. They also established data governance, metadata frameworks, CI/CD pipelines, and ongoing support processes.
Project Outcome
- The BI and data platform became a central, company-wide source of truth for reporting and analytics
- Significant adoption with hundreds of users across Finance, Supply Chain, Marketing, ESG and other functions
- Automated data pipelines replaced dozens of manual reporting processes, reducing manual Excel work and ad-hoc data requests
- Reduced data refresh times and infrastructure costs
- Machine-learning models improved forecast accuracy and reduced planning effort
- Power Apps automated operational workflows, shortening approval and reporting cycles from days to hours
- Unified data structures, standardized KPIs, and improved validation and governance; IoT provided near-real-time production insights
Platforms
Cloud
SaaS
IoT
Tech Stack
Azure
Power BI
Client Endorsement
Overall Review Rating
5
5 out of 5 stars
Timeliness
Cost Rating
Willing to Refer
Quality of Deliverables
The BI and data platform Antdata built is now our central source of truth, and adoption across the company has been strong. They've also helped us cut down on manual reporting, speed up data refreshes, lower infrastructure costs, and make our forecasts more accurate.
Note: This endorsement is based on publicly available client feedback from external review sources.