Enterprise Data Governance Drives Secure Cloud Data Management
Introduction
This case study dives into the successful collaboration between Hoonartek, a data and AI solutions company, and a leading global financial institution. Established in 1964, our client has played a vital role in the nation’s financial development. Their offerings encompass a broad spectrum of banking products and services, serving individuals, SMEs, and large corporations. With a vast network of branches and ATMs spread across the country, they ensure convenient banking experiences for a diverse clientele.
Their unwavering commitment to exceptional financial services has fueled consistent growth. However, their data infrastructure, a siloed on-prem system built on Teradata and SAS, was hindering their ability to leverage the full potential of their data for strategic decision-making.
This collaboration details the bank’s journey towards modernizing their data infrastructure. We will explore the challenges they faced with their legacy system and how Hoonartek partnered with them to implement a secure, cloud-based data lakehouse built on Databricks. This case study highlights how this innovative solution empowered the bank to unlock the true value of their data, driving better decision-making and propelling them towards continued success.
The Business Challenge
Our client faced several critical challenges with their existing data warehouse:
- Performance and Availability: The legacy system struggled to keep pace with increasing data volume and complexity, leading to slow reporting and limited data availability for analytics.
- End-of-Life (EOL) Hardware: The reliance on outdated on-premise hardware posed significant security risks and limited scalability.
- Data Explosion: The bank anticipated a surge of data from new digital sources due to customer growth and expansion, requiring a more flexible platform.
- Data Governance and Quality: Fragmented data sources and inadequate data quality processes hindered the generation of reliable insights.
- Limited Analytics Capabilities: The existing infrastructure lacked the power for advanced analytics and AI/ML use cases.
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Joint Solution Discovery & Implementation Plan
Hoonartek partnered with the client to design and implement a comprehensive data lakehouse solution on Databricks. The project encompassed several key components:
- Configurable Data Lakehouse Framework: A reusable framework built on Databricks for data ingestion, integration, and management, ensuring adaptability to future needs.
- Data Ingestion Pipelines: Streamlined data pipelines for ingesting structured, semi-structured, and unstructured data from various sources, including legacy systems and real-time feeds.
- Open Table Formats: Utilizing open table formats like Parquet for efficient data storage, sharing, and querying.
- Data Model Design: A flexible data model built to accommodate current and future business needs, enabling seamless data integration across marts.
- Cloud-Based Deployment: Secure deployment on a reliable and compliant cloud platform that aligns with the bank’s overall IT strategy.
- DataOps and FinOps Integration: Embedded DataOps and FinOps capabilities for data quality management, automated data pipelines, and cost optimization.
- Unified Data Platform: Creation of a unified data platform with curated and consumption layers to support enterprise-wide BI, analytics, reporting, and compliance needs.
- AI/ML and GenAI Support: Native support for data extraction, preparation, model training, and deployment of AI/ML and Generative AI (GenAI) use cases on the Databricks platform.
- Data Governance and Observability: Implementation of robust data governance practices with data lineage, cataloging, and data quality rules at the logical element level, ensuring data trustworthiness and traceability.
Outcomes
By implementing the data lakehouse solution on Databricks, the bank achieved significant benefits:
Enhanced Decision Making & Operational Efficiency
- Access to a single source of truth for faster and more informed decision-making across the organization.
- Streamlined data management processes and automated workflows for increased efficiency and cost savings.
Advanced Customer
Insights
- Support for advanced analytics and AI/ML for deeper customer understanding and personalized financial services.
Future-Proof Scalability with Enhanced Security
- The inherent scalability of Databricks allows the bank to readily adapt to evolving data volumes and integrate new data sources as needed.
- The cloud-based platform adheres to the highest security standards and regulatory requirements for data protection.
Industry Perspective
Recognizing the dynamic nature of the financial services industry, Hoonartek proposed a future-proof roadmap for the data platform, incorporating cutting-edge technologies to unlock the full potential of their data:
- Harnessing the Power of Advanced Analytics and AI: By integrating advanced analytics and Artificial Intelligence (AI) capabilities using Databricks, our client could gain deeper customer insights, predict market trends with greater accuracy, and proactively manage risks, leading to more informed decision-making.
- Customer-Centric Data Management: Shifting the focus to a customer-centric approach, the data platform would empower the client to provide personalized financial products and services, fostering stronger and more profitable customer relationships.
Conclusion
This case study demonstrates the transformative power of migrating from a legacy data warehouse to a modern cloud data lakehouse built on Databricks. By leveraging this solution, the bank has gained a significant competitive advantage by unlocking the true value of its data to drive innovation, improve customer experience, and achieve long-term success in the ever-changing financial landscape.
