In international banking, an enterprise-wide data architecture system is challenged by issues beyond simply modernising these data systems. Issues such as regulatory obligations, disparate legacy systems, and cross-border operational environments require designs that meet regulations but also allow for businesses to operate. Over the past 20 years in the financial services sector, Sudhanshu Jain has had a leading role in supplying solutions to the above problems, most recently as the lead architect for Citi Bank’s global data architecture project, one of the world’s largest banks.
The goal of the Citi data architecture project was much more than a refresh of technology. The goal was to redefine how regulated financial data is managed, resolved, and accessed on a global level. The successful implementation of this project will have immediate implications for regulatory compliance, operational risk management, and enterprise decision-making.
Defining Enterprise Architecture Under Regulatory Constraint
When Citi started this project, they had the same issues as many of their global bank competitors: mismatch between data definitions, reconciliation processes all require many manual steps, reporting timelines that don’t match up with regulations and management expectations. All of these things directly impacted BCBS alignment for risk reporting, liquidity management, and capital planning across multiple jurisdictions.
Sudhanshu Jain defined and executed an enterprise data architecture that solved all of these systemic problems by using a standard architecture based on standard data domains, clear data ownership, and governance mechanisms aligned to business and regulatory needs instead of a tool-driven approach. Creating this architecture allows for consistent lineage, auditability, and control while allowing for both analytics and reporting without duplicating processing pipelines.
The framework created ultimately became a reference architecture across multiple businesses which steered how the data platforms would be designed and governed throughout the company.
Leading a Global, High-Impact Implementation
It took strong, enduring leadership to execute the architecture that was built by geographically disparate groups working in strictly regulated environments. Sudhanshu had the responsibility of leading an international team of over 15 data engineers and developers, all of whom worked to coordinate the delivery in all regions while controlling for measurements related to delivery quality.
During Sudhanshu’s leadership of this program, he has helped build, and operationally make functional, 200+ enterprise-class reconciliation and analytic pipelines by integrating traditional banking systems such as Java-based services, ETL frameworks, HDFS storage and Smartstream TLM systems with more modern systems based on big data technologies, AI/ML use cases and a hybrid model using both cloud-based and on-premises computing environments.
Architectural decisions consistently placed resiliency, traceability and auditability as primary design factors to ensure the regulated defensibility of architectural innovation. These principles have now been adopted as best practice design principles throughout many parts of the organisation.
Demonstrated Impact and Field-Level Significance
The architectural design of the system produced measurable, independently verifiable results, including:
1. An approximate 40% increase in the quality of the enterprise data, which improves the level of trust in both regulatory and management reporting.
2. A nearly 60% reduction of manual reconciliation efforts, removing an estimated 3,000 hours of operational workload from the annual operations budget.
3. A reduction in the length of time required to complete both Key performance indicator (KPI) and regulatory reporting from five days to one day.
4. The implementation of standardized, single-source data and reconciliation processes across 10 different operating units in the organization.
These results represent a major advancement in the institution’s ability to achieve compliance with regulatory requirements and, at the same time, reduce both operational risk and cost. The architecture and governance practices that resulted from Sudhanshu’s leadership are consistent with and, in some cases, exceed, the existing standards in use within the financial services industries, for enterprise data management for regulated financial institutions.
Recognized Technical Authority and Domain Expertise
Sudhanshu Jain has experience encompassing the complete lifecycle of enterprise data management (EDM). This includes managing enterprise EDM platforms for governance such as Collibra, Alation, and Microsoft Purview, developing and implementing relational and NoSQL data architectures, and creating and deploying modern cloud data platforms like Snowflake and AWS. He has provided technical leadership in areas including ETL (extract, transform, load) development and implementation, large scale data-processing frameworks, and analytics and visualization tools such as Informatica, PySpark, Tableau, and Power BI.
Sudhanshu has deep domain knowledge of equities, fixed income, derivatives, foreign exchange (FX), and retail banking which makes him uniquely qualified to make architectural decisions that are based on how data is consumed by trading, risk, finance, and regulation functions, especially in reconciliation and audit-sensitive environments.
Professional Leadership and Ongoing Influence
Data governance, making disciplined architectural decisions and working with all technology, business, and control functions are the cornerstones of Sudhanshu’s leadership style. His involvement in the profession of data management is reflected by his memberships in DAMA International and TDWI and his ability to continue to align with core frameworks, such as The Data Warehouse Toolkit and DMBOK.
Today, Sudhanshu is focusing on the expanded use of governed enterprise data platforms to support AI/ML adoption by maintaining explainability, lineage, and regulation—issues that are becoming more critical in the financial services industry.
About Sudhanshu Jain
With well over two decades of experience designing and implementing large-scale enterprise-wide Global Financial Industry Data Architectures (GFIDA) to manage, govern and visualize large-volume structured and unstructured data within: multinational financial institutions.
In recent years, Sudhanshu has held highly visible, critical positions in the technical design and implementation of enterprise-wide data architecture (EWD) to support global regulatory compliance, enterprise-wide risk management and Strategic Decision Making .
Sudhanshu’s credentials include an M.B.A. from the University of Delaware and have long been viewed as one of the foremost authorities on analysing complex Global Financial Industry regulations into implementable enterprise-wide data architecture solutions. As a proven leader with a long history of creating innovative architectural solutions over many years; Sudhanshu continues to provide leadership through technical direction, as well as examples of best practices related to enterprise-wide data management in the Global Financial Industry.
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This story was distributed as a release by Sanya Kapoor under HackerNoon’s Business Blogging Program.
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