During a complex bank merger in Hungary, the finance team faced a challenging internal environment with unclear responsibilities and a lack of unified technology, leading to inefficient financial reporting and data management. To address these issues, the team was guided to redefine their reporting practice by focusing on key financial questions, which facilitated the development of a stable data structure and master data management concepts using familiar Microsoft applications. The resulting transformation streamlined the reporting process, significantly reducing manual data handling and improving data quality through a unified data structure and automated workflows. This reorganization clarified roles, reduced redundancy, and enhanced the bank's ability to prioritize and address key financial areas.
In a big bank merger in Hungary, the internal environment changes from day to day. Employees are rotated, unified technology platforms are not available and financial reports are long and unfocused. Responsibilities and development tasks related to the information products are unclear and the right business question is unanswered. In this situation, a key question for a finance team is "What is the bank spending on?"another is "Where do we allocate our existing limited resources to?
The finance team struggled with manual report generation and unreliable data quality. This reduced the flexibility of reporting and increased the time needed to produce information to answer key questions. The lack of visibility and traceability of data transformation processes overshadowed the importance ofdata-driven operations. Contributing domains operated in is isolated silos, which blocked cross-functional collaboration, leaving no resources to identify relevant development tasks. Relying on existing staff and their skills, we needed to find a solution for a stable data structure grounded in key business questions.
The analysis team was able to catalogue the key issues in the latest financial report, which were used to identify priorities. The team's focus was on spending, where they learned to define development tasks by setting up a development backlog. In addition to consolidating operations, the analytical team was coached to translate business terminology into data definitions, spend data model, data dictionary and data quality expectations. The required master data management concepts were initially unknown to the organization, but the need to adopt this capability was necessary to produce more efficient data products. In order to achieve rapid results, the development team was involved and relied on the existing capabilities and Microsoft applications that colleagues were familiar with.
We have enhanced the efficiency of identifying key management questions at both the analytical and development stages by transferring the capability to create a unified conceptual catalogue (data model). This shift has facilitated prioritization at both the data and critical question levels, as well as a gradual enhancement of data quality, by defining three specific roles and responsibilities, reducing the need for six individuals to work simultaneously.The adoption of a standardized data structure and production process for the developed data products has minimized manual data transformation and its associated risks. Instead of 18 manual data copying steps, we have implemented a single standardized, automated, and historicized data flow. Furthermore, we have consolidated our approach to include one business rule and one master data list, replacing 16 manual transformation steps.
We respect your data, so we store it in a secured way and never share it with any third parties. For further details please check our data protection policyNextwit data protection policy
