In today’s hyper-competitive digital landscape, financial institutions face a pressing need to modernize their IT infrastructure. The question is no longer whether to modernize, but how to execute this transformation effectively and strategically. Legacy systems, once the backbone of operations, have become prohibitively expensive to maintain and struggle to meet the sophisticated demands of modern financial markets. Critically, these outdated systems often trap invaluable data within organizational silos, hindering the realization of transformative insights that could drive a significant competitive advantage. This article outlines a strategic 3-step approach, enhanced by Generative AI (GenAI), to unlock the full potential of your data and fuel innovation.
The Challenge of Legacy Systems: A Real-World Case Study
Consider the case of a leading African bank, headquartered in Morocco, grappling with common challenges associated with legacy systems, including complex Oracle databases and intricate SAP Business Objects implementations. Their modernization journey involved a strategic upgrade to an advanced on-premise Cloudera platform, seamlessly integrated with Tableau for enhanced data visualization and reporting capabilities. This project transcended a mere technology upgrade; it aimed to fundamentally transform the bank’s ability to access, analyze, and leverage its data more effectively. The initial migration focused on five strategically selected operational dashboards, streamlining report generation and significantly enhancing data accessibility across the entire organization.
The result? A state-of-the-art data platform enabling the rapid development and deployment of new business use cases. All data transformations are now fully automated within the Cloudera environment, with reports generated automatically in Tableau. This comprehensive modernization effort, rooted in a deep understanding of the existing IT landscape, has become a pivotal step in maximizing the bank’s data potential and driving future growth.
Step 1: Deep Dive – Understanding and Documenting Your Data Landscape with GenAI
At the heart of the bank’s data management challenges lay a complex web of legacy systems, including the Oracle Database, SAP Business Objects, and a multitude of Microsoft Access databases used for localized data enrichment. This fragmented setup, prevalent in many financial institutions, resulted in an opaque and siloed data landscape. Data analysts interacted with the Oracle database through SAP Business Objects, with Data Services handling necessary transformations. However, the reliance on Microsoft Access for further enrichment introduced significant inefficiencies and a lack of transparency in critical data processes.
GenAI Enhancement: Leverage GenAI to automatically document data lineage. GenAI can analyze existing code, database schemas, and transformation logic to create a comprehensive data catalog, highlighting dependencies and potential bottlenecks. This reduces manual effort and ensures accuracy in understanding the current data landscape. Furthermore, GenAI can be used to identify redundant or inconsistent data elements across systems, paving the way for data harmonization.
To gain a deeper understanding of user preferences and operational challenges, the bank conducted thorough interviews with key stakeholders across various departments. These interviews provided invaluable insights into how business users interacted with the data, their specific needs, and the limitations of the existing tools. The Head of Business Intelligence played a particularly crucial role, offering detailed knowledge of the systems and identifying critical pain points. This collaborative approach allowed for the development of tailored recommendations addressing the most pressing issues and aligning the modernization effort with business priorities.
Step 2: Building a Foundation – Establishing Robust Data Governance with GenAI
Recognizing the paramount importance of data governance, the bank established robust frameworks as a cornerstone of their modernization efforts. A new digital factory was created to streamline data processes, supported by a data office organization inspired by data mesh methodologies. This innovative structure allowed different teams to focus on specific segments of the data pipeline, leading to faster development cycles and easier data integrity checks.
GenAI Enhancement: Implement GenAI-powered data quality monitoring. GenAI can learn the expected patterns and distributions of data, automatically detecting anomalies and potential data quality issues. This proactive approach allows for early intervention, preventing data corruption and ensuring data accuracy. GenAI can also automate the creation of data governance policies based on industry best practices and regulatory requirements, streamlining the governance process.
For example, when discrepancies arose during the migration—such as differences in data between the old and new platforms—these were quickly identified and resolved within the established governance structure. This proactive approach fostered trust in the new platform and ensured the bank was well-prepared to take full ownership of its data assets. Furthermore, this approach ensured stronger data access policies, enhancing both security and compliance with increasingly stringent regulatory requirements.
Step 3: Automate for Efficiency – Streamlining Report Generation with Automation and GenAI
Prior to the modernization initiative, the bank’s report generation process was characterized by significant inefficiencies and manual intervention. Business users were required to request data extracts or database refreshes from IT teams, as these critical processes were not automated. These delays were further compounded by issues with data completeness, particularly when reports depended on data from core banking systems that were not promptly updated.
GenAI Enhancement: Utilize GenAI for automated report generation and insights discovery. GenAI can analyze user queries and automatically generate the necessary SQL code to extract and transform data. It can also identify hidden patterns and correlations in the data, providing valuable insights that might otherwise be missed. Furthermore, GenAI can personalize reports based on user roles and preferences, ensuring that each user receives the information that is most relevant to them.
The modernization efforts aimed to completely overhaul this cumbersome process by automating data extraction, ensuring timely updates, and leveraging Cloudera’s robust processing capabilities. This fundamental shift enabled faster report generation, freeing up valuable time for business users to focus on analysis and strategic decision-making rather than tedious data wrangling.
Case Study Deep Dive: Migrating a Large African Bank to Cloudera On-Premises
Mapping Business Objects for Consistency
A crucial aspect of the bank’s modernization was the standardization of business objects across the entire organization. The existing fragmentation of tools and processes had led to inconsistent definitions, undermining trust in the data and hindering effective collaboration. To address this critical issue, the bank implemented a comprehensive data mapping and migration process, assisted by modern tools and technologies.
The approach involved providing clear overviews of transformations within Data Services, defining schemas for the new Cloudera platform using legacy Oracle schemas as a reference, and expediting the documentation of new Tableau reports by analyzing existing report files. While AI-assisted processes were used to accelerate certain aspects of the work, they were not the primary driver. This comprehensive approach not only standardized the data model but also empowered business users and analysts to interact with the data more efficiently, laying a solid foundation for improved decision-making and enhanced analytics maturity.
Key Outcomes of the Modernization
While the project is still ongoing, the migration has already successfully delivered four out of five targeted dashboards with an average explainable discrepancy in data between 0 and 1%, ensuring high accuracy and reliability. Data ingestion and transformation processes were automated and orchestrated using Airflow, enabling daily updates and significantly improving the overall efficiency of data operations. The bank’s team, in close collaboration with external experts, received thorough training and became proficient in their new roles within the data factory, following a well-defined skills matrix, ensuring they were fully equipped to manage and optimize the new data platform.
How GenAI Can Supercharge Your Modernization Efforts (Continued)
This real-world experience illustrates the inherent challenges and significant opportunities associated with modernizing legacy systems. While the journey may seem daunting, with the right approach, including the strategic application of GenAI, financial institutions can unlock the full potential of their data and achieve a significant competitive advantage. GenAI offers powerful capabilities for automating tasks, improving data quality, and accelerating insights discovery. By embracing GenAI, financial institutions can transform their IT infrastructure and become more agile, efficient, and data-driven.
