Accounting Hub Design
Due to increased trade volume, the Danish bank wants to build an Accounting Hub responsible for creating accounting entries and posting information to the Data Warehouse and ERP. The bank needs a scalable solution that meets the functional requirements of end users.
The existing manual process for creating accounting entries is time-consuming and error-prone. The bank requires a robust and efficient Accounting Hub that can handle the growing trade volume and integrate data from various operational systems.
The bank engages in the design, analysis, and build of the Accounting Hub. The following steps are taken:
- Data Analysis: Conduct a thorough analysis of the data requirements and sources from Customer, Trade, and other operational systems.
- Source to Target Mapping: Identify the mapping between the source data and the target accounting entries.
- Creation of Logical and Physical Data Model: Develop a logical and physical data model that represents the structure and relationships of the accounting data.
- Setup Data Governance Principles: Define data flow, data quality framework and measures to ensure data governance principles are established.
- Define Metadata Management Framework: Define Metadata Management Framework: Establish a framework to manage and maintain metadata associated with the accounting data.
- Provisioning and Authoritative Source: Implement a technology-driven solution on a robust governed platform to provision and manage the accounting data.
- Operating Model Setup: Establish an operating model for the smooth governance of all data assets, ensuring proper maintenance and adherence to data governance principles.
The Accounting Hub design provides the bank with a scalable solution that effectively handles the increased trade volume. By automating the process of creating accounting entries, the bank reduces manual effort and minimizes errors. The integration of data from various operational systems enhances data accuracy and improves the overall financial reporting process. The implementation of data governance principles and metadata management framework ensures data integrity and supports regulatory compliance. The operating model facilitates efficient governance and maintenance of data assets, ensuring ongoing operational excellence