Profit Analysis System Development

Project Goals

The ProfitSense project was developed to provide a comprehensive data analytics solution for a large corporation with over 60 platforms and 6000 employees. The main goal was to identify which product positions were generating the highest profit and which were causing the most losses. By automating the analytics process, the project aimed to provide management with the necessary insights to improve profitability and reduce losses, ultimately increasing the corporation's overall productivity.

Functional Capabilities

• Automated Profit and Loss Analysis: The system automatically identifies the most profitable and the most loss-making products without the need for manual intervention by a significant number of employees. This automation greatly reduces the time and effort required for such analysis.
• Data Consolidation and Storage: Data from various sources, collected in different formats, were consolidated into Apache Parquet files, a compressed data storage format that optimizes storage and processing efficiency.
• Integration with Analytical Systems: Packaged data in Apache Parquet format were sent to Yandex ClickHouse for in-depth analysis. The system allowed for flexible analysis of the data, enabling the corporation to gain valuable insights into product profitability.
• Product Ranking: The system could identify the top best and top worst product positions, providing the management team with actionable insights on which products to focus on for improvement or elimination.

Solution Concept

The ProfitSense data analytics system was developed to address a critical challenge faced by large-scale enterprises—efficiently identifying profitable and loss-making products across diverse product lines and platforms. Given the complexity and volume of data involved, a manual analysis process was inefficient and time-consuming.

The system consolidated all data, regardless of the source or format, into Apache Parquet files. This compressed data format enabled efficient storage and processing, making it easier to analyze large datasets. The data were then sent to Yandex ClickHouse, a high-performance columnar database designed for real-time analytics, where they could be analyzed comprehensively.

The backend development utilized technologies such as .NET, C#, and MSSQL for processing and managing the data. Python and Docker were used to manage the data flow and ensure seamless integration between different components of the system.

The system provided management with easy access to critical information on product profitability, allowing them to take proactive measures to maximize profits and reduce losses. By automating the analytics process, the system significantly increased the speed and accuracy of decision-making, enabling the corporation to respond to market changes more effectively.

Results

• Increased Productivity: By automating the analytics process, the corporation's overall productivity increased. Management could quickly identify profitable and loss-making products and take appropriate action without the need for extensive manual analysis.
• Enhanced Decision-Making: The system provided management with valuable insights into product performance, enabling them to make informed decisions regarding which products to promote or discontinue.
• Increased Transactions: The identification of the most profitable products led to an increase in the number of transactions, as management could focus on promoting high-performing products. Conversely, the elimination of loss-making products reduced unnecessary expenses and improved profitability.

Technologies and Architecture

• Backend Development:
  • .NET and C#: Used for developing the core backend components that processed and managed the data.
• Data Storage and Management:
  • Apache Parquet: Data were consolidated into Apache Parquet files, a compressed storage format that optimized data storage and made it easy to process large datasets.
  • Yandex ClickHouse: Used for storing and analyzing data, providing high-performance analytics capabilities for identifying profitable and loss-making products.
  • MSSQL and PostgreSQL: Utilized as databases for managing product data, transaction history, and other relevant information.
• Integration and Deployment:
  • Python: Used for managing data flow and integrating various system components.
  • Docker: Employed to containerize the application, ensuring consistent deployment and scalability.
• Operating Systems:
  • Windows and Linux: Supported for both server-side and client-side components, providing flexibility in deployment options.

User Cases

• Management Team: The system provided the management team with real-time insights into product profitability, enabling them to take proactive measures to increase profits and reduce losses.
• Sales and Marketing Teams: Sales and marketing teams used the insights provided by the system to focus their efforts on promoting profitable products and improving or discontinuing loss-making products.
• Data Analysts: Data analysts used the system to conduct in-depth analyses of product performance and provide detailed reports to management.

Integration and Development Process

• Requirements Gathering: The project began with gathering requirements from the management, sales, and marketing teams to understand their specific needs for product performance analysis.
• System Design and Architecture: The system architecture was designed to handle large volumes of data efficiently. The backend was developed using .NET and C#, and data were consolidated into Apache Parquet files for efficient storage and processing.
• Team Formation and Leadership: A team of software developers, data analysts, and system architects was formed to develop and implement the system. The development process followed the Agile Scrum methodology, allowing for continuous feedback and iterative improvements.
• Implementation and Testing: The system was implemented iteratively, with regular testing to ensure that the analytics capabilities met the needs of the management and sales teams. The use of Docker ensured consistent deployment across different environments.

Client Benefits

• Reduced Manual Effort: The automated system eliminated the need for manual product profitability analysis, significantly reducing the workload for employees and allowing them to focus on higher-value tasks.
• Faster Decision-Making: The ability to quickly identify profitable and loss-making products enabled the management team to make informed decisions and respond to market changes more effectively.
• Increased Profitability: By focusing on promoting profitable products and eliminating loss-making ones, the corporation was able to increase its overall profitability.
• Scalable and Reliable System: The use of Docker and Apache Parquet ensured that the system could scale to meet the demands of the corporation's extensive operations while maintaining high reliability and efficiency.