CASE STUDY
An international beverage manufacturer sells its products in various markets.
The challenge
- Objective: To analyze which products are sold in which market with which frequency and which products are no longer in stock
- High manual effort to extract the relevant data
- High susceptibility to errors due to manual processes
Solution
- Transformation of the raw data using a Python script
- Provision of the input and output files via an FTP server or provision of the application in a Docker container
- Advantages of the Docker container: all data remains on the customer’s local computer (data protection); development environment is the same as the production environment, i.e. no problems such as incorrect Python versions or missing Python modules
Result
- Automatically generated and error-free output files for further analysis
- Automated quality checks for timely detection of possible irregularities in the data
- Error-free process due to elimination of manual work
- Time savings thanks to the high processing speed of Python
- Preservation of data protection due to Docker container