{"id":159849,"date":"2024-05-09T05:54:10","date_gmt":"2024-05-09T09:54:10","guid":{"rendered":"https:\/\/innowise.com\/?post_type=case&p=159849"},"modified":"2026-02-26T06:38:42","modified_gmt":"2026-02-26T11:38:42","slug":"data-management-in-banking","status":"publish","type":"case","link":"https:\/\/innowise.com\/de\/case\/data-management-in-banking\/","title":{"rendered":"Datenmanagement-Repository f\u00fcr Banken"},"content":{"rendered":"\t\t
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Data management in banking<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n API<\/a>\n <\/div>\n \n
\n Banking<\/a>\n <\/div>\n \n
\n Business intelligence<\/a>\n <\/div>\n \n
\n Data analytics<\/a>\n <\/div>\n \n
\n Data management (DMS)<\/a>\n <\/div>\n \n
\n Discovery<\/a>\n <\/div>\n \n
\n FinTech<\/a>\n <\/div>\n \n
\n Oracle<\/a>\n <\/div>\n \n
\n Power BI<\/a>\n <\/div>\n \n
\n Python<\/a>\n <\/div>\n <\/div><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Innowise has developed a centralized repository to store, process, and secure large amounts of data related to corporate clients, banking accounts, and payment transactions.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t

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Challenge<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Disparate banking-related data across varios sources that hindered decision-making<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Our client faced challenges managing and extracting value from the vast and growing volumes of data across corporate clients, banking accounts, and payment transactions. With data dispersed across multiple legacy systems, the bank experienced significant data processing and analysis challenges, struggling to gain timely insights for decision-making. Besides, the existing infrastructure lacked scalability, while maintaining various obsolete systems became unsustainable.\u00a0<\/span><\/p>

Another issue stemming from this central challenge was the difficulty of complying with stringent regulatory banking-related requirements. Data storage and management systems were scattered, making it difficult to track, report, and audit data effectively. This increased the risk of involuntary violations as banking reps had to spend much time collecting and verifying data.<\/span><\/p>

The client commissioned Innowise to build a robust data lake architecture that could consolidate their diverse data sets into a single, scalable, and secure environment for data management<\/span><\/a> in banking. They sought a convenient solution to keep track of information about clients, accounts, and transactions and comply with regulatory standards, accelerating the cycle from raw data inputs to actionable business insights.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Solution<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Data lake development to store cleansed and structured banking data<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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We developed a centralized database to store and integrate data streams gathered from a variety of sources, including electronic banking, mobile applications, and social media. Our experienced specialists implemented a medallion lakehouse architecture, focusing on an ACID-driven, multi-layered approach to build a single source of truth for banking data storage.\u00a0\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Data layering<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The foundation of the data lake, the bronze layer, contains raw data ingested from various sources like JSON files, RDBMS, and more, securely stored in its original form. Based on the bronze layer, the silver layer refines this data, cleansing and normalizing it for advanced analytics. Ultimately, the pinnacle of the data structure, the gold layer, contains business-level aggregates for high-level reporting and dashboarding, enabling the bank to generate actionable insights.<\/span><\/p>

Our project team identified all potential data sources, including transactional systems, customer databases, online portals, and more. Our specialists mapped each data source, understanding its format, update frequency, and relevance. As the core step, we developed automated data ingestion pipelines using ETL (extract, transform, load) tools to handle different data formats like CSV, JSON, XML, and RDBMS. Depending on the nature of the data source, we established real-time or batch-processing pipelines.\u00a0<\/span><\/p>

Real-time pipelines were used for data streams that required immediate processing (like transactional data), while batch processing was reserved for less time-sensitive data. As data entered the bronze layer, it underwent an initial validation process, checking for integrity, format consistency, and any corrupt or incomplete records.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Silver layer: data processing and refinement<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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At this stage, our project team focused on enriching the raw data from the bronze layer and transforming it into a more structured and usable format. Our vetted developers identified and corrected typographical errors, data format inconsistencies, and discrepancies, as well as removed duplicate records to avoid misleading insights. We implemented data imputation and flagging strategies for datasets with missing values, sending these records for further review depending on the nature and significance of lost data.<\/span><\/p>

Then, our project team enhanced the data by adding relevant context or additional information. For example, we augmented transaction data with customer demographic information, enabling a more comprehensive analysis. Once data was refined and aggregated, we applied indexing techniques for faster querying and retrieval. Finally, data from different sources are cross-referenced and linked, while similar information from various sources is consolidated into unified datasets, making it easier to perform holistic analysis. By ensuring the data is clean, consistent, and well-structured, we paved the way for advanced analytics and business intelligence in the gold layer.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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