Digital Transformation / Automation
Automated data collection and modernization of the information layer to unlock capacity and prepare new applications. This page provides context, problem, constraints, solution, stack, impact and takeaway in a format that is easy to read and cite.
Automated data collection and modernization of the information layer to unlock capacity and prepare new applications.
An organization with legacy systems, manual data collection and the need to establish a more modern foundation for future applications.
Context
An organization with legacy systems, manual data collection and the need to establish a more modern foundation for future applications.
Problem
Dependence on fragmented tools such as VB6, Access and Excel, with low process clarity and repetitive operational work.
Constraints
Modernize without disrupting operations, structure a centralized platform and introduce AI only where it creates value.
Solution
Python crawler scheduled via GitHub Actions, ETL over SFTP and databases, plus the design of a cloud-ready central data platform and an AI/automation roadmap.
Stack
Python, GitHub Actions, ETL, SFTP, database, cloud-ready architecture
Impact
Automated data collection, improved operational capacity and foundations established for client apps and more efficient processes.
Takeaway
Automation and data platforms create value when they reduce operational friction and build foundations for future evolution.
Dependence on fragmented tools such as VB6, Access and Excel, with low process clarity and repetitive operational work.
Python crawler scheduled via GitHub Actions, ETL over SFTP and databases, plus the design of a cloud-ready central data platform and an AI/automation roadmap.
Python, GitHub Actions, ETL, SFTP, database, cloud-ready architecture