Data-Driven Banking: Unleash the Power of DWH

Building a Data-Driven Platform for Banking Excellence This project seeks to develop a centralized data repository to:

• Optimize business processes: Automate manual tasks and streamline operations.
• Enhance customer satisfaction: Deliver personalized experiences and improve customer retention.
• Gain a competitive edge: Leverage data insights to identify new market opportunities and launch innovative products.
• Empower data-driven decision making: Provide management with the data and tools needed to make informed strategic decisions.

1. Data Source Disparities

Problem: Data is scattered across various systems, with different formats and structures.

Solution:

• Data Inventory: Regular detailed analysis of all data sources to determine their structure, content, and quality.
• Data Standardization: Development of unified standards for data, including formats, encodings, and field names.
• ETL Process Creation: Development of Extract, Transform, Load (ETL) processes to combine data from various sources into a single repository.

2. Data Completeness and Change Tracking

Problem: It is difficult to track whether all data has been loaded into the data warehouse and how it has changed over time.

Solution:

• Data Validation: Development of mechanisms to verify the completeness and quality of data at each stage of the ETL process.
• Change History: Storage of data change history to track changes and analyze trends.
• Quality Control Tools: Use of tools for automated data validation against business rules.

3. Mixed Workload (OLTP and OLAP)

Problem: Simultaneous execution of online transaction processing (OLTP) and online analytical processing (OLAP) can degrade system performance.

4. Sparse Metadata

Problem: Lack of or insufficient information about data makes it difficult to understand and use.

Solution:

• Data Dictionary Creation: Development of a data dictionary containing descriptions of all tables, fields, their data types, and values.
• Business Rule Documentation: Documentation of business rules related to data.
• Metadata Management Tools: Use of specialized tools for storing and managing metadata.

Our project represents a groundbreaking solution for consolidating and analyzing data from various bank systems. We have developed a unified data repository that enables real-time collection, cleansing, and normalization of information from multiple sources.

Key Features of the Project:

• Unified Data Repository: We have created a centralized data repository that integrates information from diverse bank systems. This ensures data consistency, integrity, and simplifies analysis from more than 10 sources on more than 30 TB of data
• Advanced Technologies: The project leverages cutting-edge technologies:
  • ETL: Apache Airflow for orchestrating data ingestion, transformation, and loading processes.
  • Data Storage: PostgreSQL and Citus for scalable and high-performance structured data storage.
  • Analytics: Apache Superset for data visualization and interactive dashboard creation.
  • Metadata: Open Metadata for metadata management and creating a unified data catalog.
• Data Democratization: We have provided broad data access to employees, fostering data-driven decision-making across all key departments.
• Accelerated Digital Transformation: The project significantly enhanced the bank's data maturity, accelerating analysis and decision-making processes.
• Increased Transparency and Reliability: Real-time visualization of key metrics and data ensures transparency in business processes, enabling the rapid identification and resolution of issues.

Project Outcomes:

• Improved Data Quality: Ensuring data consistency and timeliness.
• Accelerated Decision Making: Providing employees with access to timely and accurate information.
• Optimized Business Processes: Identifying bottlenecks and opportunities for improvement.
• Enhanced Marketing Campaign Effectiveness: Personalizing offers and improving targeting.
• Reduced Operational Costs: Automating manual processes and optimizing resource utilization.

Project Benefits:

• Scalability: The solution can easily scale to accommodate growing data volumes.
• Flexibility: Ability to quickly adapt the system to changing business needs.
• Openness: Utilization of open standards and technologies.
• High Performance: Ensuring rapid data access for analysis.

Conclusion:

Our project marks a significant milestone in the bank's digital transformation. By creating a robust data foundation, we have empowered the bank to make informed decisions based on data, leading to increased efficiency and a stronger market position.

Additional Possibilities:

• Machine Learning: Employing machine learning models for forecasting and anomaly detection.
• Internet of Things: Integrating data from IoT devices.
• Blockchain: Ensuring data security and transparency.