At a glance
Client | An enterprise client in the financial services sector based in the DACH region |
Scope | A Python 2.7-based analytics and data transformation stack for risk, sales, portfolio, client, profitability, and compliance reporting |
Analysed codebase | ~140,000 lines of Python in ~880 scripts, notebooks, helper modules, shell wrappers, and configuration fragments |
Data surface | ~35 upstream data sources (databases, CSV drops, Excel workbooks, SFTP feeds, internal APIs, exports from legacy reporting); ~110 recurring reports, extracts, and analytical outputs |
Dependency surface | ~200 third-party and internal Python packages (pandas, NumPy, SciPy, scikit-learn, matplotlib, xlrd/openpyxl, SQLAlchemy) as well as several database drivers and major versions lagging behind the current state |
User base | ~40 senior analysts working ad-hoc directly on the stack; ~12 management reporting consumers downstream; compliance and audit functions relying on derived datasets |
Method | 7P Legacy Fast Check: Scan. Score. Strategize. |
Tools | Automated Python analysis toolchain, AI pipeline, expert walkthrough (Data Engineering), coding agent walkthrough |
Time-to-Verdict | Scans in hours; full assessment delivered in approximately three weeks |
Deliverables | Assessed as-is state report, prioritised top 10 list, Python 2-to-3 migration assessment, dependency and package availability map, data lineage and reproducibility map, inventory of analytical IP, AI readiness assessment, strategic evolution roadmap and service takeover checklist |
The company
The client is a major financial services provider in the DACH region. Its analytical operating model was based on a Python stack that had evolved over time through the collaboration of data scientists and domain experts. This connected data from various sources with business logic, forming the basis for reporting and analytical evaluations.
An extensive body of domain knowledge lay behind the Python 2.7 stack, providing professional validation of the reporting and analytical assessments. The scripts contained rules and calculations relevant to reporting, compliance and future data-driven projects.
The initial situation
The scripts were sensibly designed for their original purpose. Initially, they answered individual analytical questions, but they gradually evolved into a productive collection for recurring reports. What began as an analytical workbench gradually turned into a critical production data pipeline.
At the same time, however, the technical foundation was ageing. Python 2.7 reached its end of life, key dependencies could no longer be reliably installed and important knowledge was lost when the original authors left. Although the reports were still being delivered, confidence in their reliability noticeably declined.
Before a decision could be made about stabilisation or modernisation, a reliable basis for decision-making had to be established from the mature system.
The solution
7P classified the existing system as a business-critical analytics stack of high business value. This value was determined by combining automated analyses and AI-supported evaluations with expert reviews. This quickly provided a reliable overall picture of the system’s technical condition, reproducibility and embedded analytical knowledge.
The “Legacy Fast Check” identified technical weaknesses.
The system was assessed in such a way that risks and business value could be clearly distinguished. Furthermore, it became clear which knowledge needed to be preserved for future AI initiatives.
The Legacy Fast Check followed a three-step approach:
- Scan: Analysis of the codebase, dependencies, reproducibility and business-valuable transformations.
- Score: Evaluation of the findings with regard to risk, maintainability and future viability.
- Strategise: Development of concrete action options for stabilisation, migration and further development.
The full assessment was delivered within approximately three weeks.
Sebastian Grundhöfer
Senior Solution Engineer
Voice from the project
‘The scripts contained domain knowledge that had been developed over many years and was central to the business. The critical issue was the uncertainty surrounding how reliably changes and results could be verified. The Fast Check made the business core visible, enabling a well-founded discussion about modernisation for the first time.‘
The result
The Fast Check revealed that, although the stack continued to deliver business-critical results, its technical foundation and execution logic had become less resilient over the years. Above all, it became clear that technical risk and business value were closely intertwined.
Runtime and dependencies
The operation still relies heavily on an unsupported runtime environment and outdated package versions. Several dependencies could only be installed with limitations or were tied to mature environments. Consequently, a technical renewal could not be treated as a simple upgrade.
Reproducibility and data flows
Furthermore, it was unclear how results could be traced and reproduced. Execution sequences and manual intermediate steps were not always documented in a way that allowed reports and outputs to be reliably reproduced at any time. This was of crucial importance for a system whose results feed into reporting and compliance.
Domain knowledge in the system
The system’s real value lay in its analytical domain knowledge. Rules and transformations were distributed across many scripts and were only partially documented explicitly. Therefore, modernisation is only sensible if this knowledge is specifically secured and transferred into usable structures.
What the client received
Runtime & dependencies | End-of-life technology and an outdated package landscape make operation and modernisation more complicated. |
Reproducibility | Data flows and calculations were made transparent and critical gaps were identified. |
Analytical IP | Business-critical domain knowledge was located and documented for future use. |
Security & operations | Prioritised measures provide the foundation for a more stable and auditable operation. |
The decision the client can now confidently make
The Legacy Fast Check identified the parts of the system that were technically problematic and the required domain knowledge. This enabled the client to broaden the modernisation discussion beyond Python 2 alone and decide on future action reliably.
Would you like this result for your system?
The Legacy Fast Check offers reliable external insights, including structured scores, a comprehensive risk register and prioritised recommendations for action. Act now!
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