World’s largest multinational FnB conglomerate

Modernizing Analytics with Scalable Data Architecture

We deliver measurable results

Faster access to business insights through real-time Power BI dashboards
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Reduction in manual data processing time
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Reduction in reporting-related operational costs
0 %

Get to know the client

One of the world’s largest multinational food and beverage conglomerates sought to build a modern data analysis and reporting solution. The goal is to consolidate data from multiple systems, streamline transformation processes, and enable real-time, scalable reporting to drive smarter, faster business decisions.

Challenges

They faced challenges with

Data Fragmentation: Integrate multiple disparate data sources into a centralized platform for unified analysis

Manual Processes: Automate data ingestion and transformation pipelines to ensure timely and accurate updates

Real-Time Reporting Needs: Deliver live insights and interactive dashboards via Power B

Performance Optimization: Balance processing workloads between Amazon Redshift and Snowflake to achieve optimal speed and cost-efficiency

SOLUTIONS

Our tools for success

SmartOSC designed a scalable, cloud-based architecture using a best-in-class technology stack, including:

– Airflow for orchestrating automated data pipelines
– Snowflake & Redshift for data warehousing and performance optimization

– Terraform for infrastructure as code

– DBT (Data Build Tool) for transformation logic and modeling

– Power BI for real-time visualization and reporting

Key Use Cases

– Sales & Revenue Reporting: Aggregated data across eCommerce channels to track performance, uncover sales trends, and inform product optimization strategies

– Customer Segmentation & Analysis: Analyzed customer behavior and loyalty data to identify high-value segments, enabling personalized marketing campaigns

– Marketing Campaign Effectiveness: Evaluated the performance of email and personalization efforts, measuring engagement and conversion metrics to refine targeting

– Inventory Optimization: Leveraged historical data for demand forecasting, reducing overstock and stockouts while cutting operational costs