Management Summary

To harmonize the systems of two large banks into a unified Target Operating Model, we implemented various pipelines in Python. The core focus was on the audit-proof transfer of data, unified delivery into a multi-target structure, and a Migrate-To-Operate strategy. Our expertise in the support processes of financial accounting and controlling, particularly in the real estate sector (e.g., options in Switzerland), combined with our ERP understanding and programming skills, prepared us ideally for this challenge.

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Key Tasks Included

• Supporting the validation of the new Target Operating Model
• Defining extractions from ERP systems and proprietary databases
• Defining and implementing transformations and harmonizations via Python scripts
• Defining loadings through SQL staging data bases and XML interfaces

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Solution Approach

In close collaboration with POM+, we analyzed the target and source systems to identify the necessary interfaces and data structures. We then designed data pipelines and defined the corresponding transformation steps.

• Extraction: Varied by application, ranging from CSV file downloads to SQL scripts. The extractions were stored in an unstructured data repository with mapping files for further processing.
• Transformation: Focused on data cleansing, harmonization of account plans, data augmentation, adjustment of ledger entries, and formatting into appropriate structures
•  Loading: Involved delivering data in various formats such as SQL statements, CSV files, or XML files.

Added Value

The implemented solution offers several key advantages:

• ‍Process Synergies: Existing processes were seamlessly extended to both companies, allowing simultaneous management of the existing and consolidated portfolios. Support processes in financial accounting and controlling are now aligned and unified.‍
• System Synergies: Utilizing the existing infrastructure avoided additional licensing costs and maximized the efficiency of the current server infrastructure. Both companies can benefit from future developments.

The experience gained from this project enables us to harmonize financial data from different ERP systems using Python scripts and transform it from a multi-source to a multi-target landscape.

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