Business Challenge
Our client, a Fortune 500 retail company, faced a fragmented and chaotic reporting process due to data being siloed across multiple legacy systems. This resulted in:
- Inconsistent and unreliable reports;
- Lack of visibility into key business metrics;
- Poor data quality, affecting inventory tracking and forecasting capabilities;
- Inefficient decision-making processes.
The company needed to overhaul its analytics and reporting infrastructure to enable better insights, streamline operations, and ensure data accuracy and availability for sustainable growth.
Zoolatech Approach
We provided the company with a team of strong and experienced Big Data engineers and Java engineers. The ZoolaTech team has implemented a scalable and highly robust Big Data analytics platform that supports advanced analytics and machine learning models.
We focused on such key tasks:
- Data consolidation: Unifying fragmented data from multiple systems into a single platform.
- Event-Driven approach: Implementing real-time data streaming for better operational visibility.
- Automation & optimization: Building hundreds of robust data pipelines for reliable data flow and processing.
- Enhanced data quality: Ensuring the integrity and accuracy of data to reduce errors and provide high quality data for BI reports and ML models training.
Implementation
Data capturing
The first step was capturing data from multiple, disconnected sources within the client’s legacy systems. ZoolaTech set up a unified system to collect data in real time, ensuring that all relevant information, such as sales transactions, inventory levels, and logistics data, was captured without delay.
Data Streaming
Kafka’s event-driven architecture ensures that every data event, such as a sale, inventory update, or delivery status change, is immediately streamed to the data platform. This allows for instant, ongoing data flow rather than relying on slower batch processing.
Eventualization and centralization
As the data streams through Kafka, it is eventualized — meaning each significant business action or transaction is treated as a distinct event. These events are processed through Java-based microservices. By organizing data as events, the system provides granular visibility into every business transaction in real time.
Data processing
To manage and automate the processing of large volumes of data, ZoolaTech uses Airflow. Airflow orchestrates the cleaning, filtering, and transformation of raw data. This ensures that by the time data reaches the central warehouse, it is accurate, standardized, and ready for analysis.
Data storage and warehousing
Once processed, the data is stored in a centralized warehouse. This warehouse is based on Teradata (but it is going to be migrated later on to Google BigQuery for improved scalability and cost efficiency). The warehouse provides a single source of truth for all the client’s data, ensuring that insights are drawn from consistent, high-quality data.
Data analysis
Tableau is used to provide the client with easy-to-use, customizable dashboards. These dashboards allow business users to generate reports, analyze trends, and make informed decisions based on real-time, accurate data.
Additionally, Data Science and Machine Learning models are used to forecast delivery times, optimize routes, predict pricing, and more, enabling advanced operational insights.
Value Delivered
ZoolaTech’s approach to building a scalable, event-driven Big Data Analytics platform enabled the client to overcome significant reporting challenges and positioned the company for future growth. By consolidating legacy systems and implementing advanced data processing and analytics, ZoolaTech delivered a solution that enhanced data quality, improved operational efficiency, and empowered the client with actionable insights for strategic decision-making.
Key milestones we helped to achieve:
- Enhanced data visibility: The new event-driven architecture provided real-time access to previously siloed data, giving the company a comprehensive view of its operations.
- Actionable insights: Advanced reporting through Tableau enables executives and managers to make data-driven decisions, improving overall business agility.
- Optimized operations: Leveraging high quality data for ML models helps streamline logistics, reducing delays and improving customer satisfaction.
Route optimization
One of the key areas where ML is applied is in route optimization. By analyzing vast amounts of data, ML models identify the most efficient delivery routes, ensuring that products are dispatched from the optimal fulfillment center or warehouse. This reduces delivery times, enhances customer satisfaction, and lowers logistical costs.
Price forecasting
Another critical use case is price forecasting. ML algorithms predict the most effective pricing strategies based on historical data, market trends, and consumer behavior. This enables the company to set competitive prices that maximize profit margins while remaining attractive to customers.
Delivery time prediction
Accurate delivery time predictions are vital to maintaining the company’s promise of timely deliveries. By leveraging ML, the company can precisely forecast delivery times, such as predicting a product will take for instance 3.5 days to reach the customer. These insights help optimize supply chain operations, reduce uncertainties, and provide customers with accurate delivery estimates.
Improved inventory accuracy
Prior to the implementation of the Big Data Analytics platform, the company struggled with inventory inaccuracies, with only 60% accuracy in tracking stock levels. Post-implementation, this accuracy has improved to over 90%. Advanced scanning technologies and centralized data processing have enabled real-time tracking of items’ weights, status, and movements, all of which are now integrated into the Big Data platform.
Overall business impact
The adoption of Big Data and ML has transformed the company’s ability to make data-driven decisions. From more efficient logistics and pricing to enhanced inventory visibility, these technologies have not only streamlined operations but also contributed to cost reduction and improved customer experience.
