We were contacted by a global freight forwarder looking to improve its logistics operations. The company’s issues came down to its rich unconnected data sources that had varying data quality available, a lack of automated analysis, and time delays in available data usage. Another problem was its on-premise environment, which boasted limited flexibility and performance. This was mainly down to a small data science team that had limited resources.
Our first port of call was to offer support in the form of setting up a sustainable data pipeline and data lake. We switched the on-premise environment to a more flexible offering, shifting extract, transform, and load (ETL) as well as the analytics workload to the cloud.
We also ensured a joint implementation of selected machine learning use cases, building up a platform prototype for the company’s business units to extract data insights for customer handling.
After working with Eraneos and implementing our ideas, the global freight forwarder was the proud owner of an automated data pipeline as well as a prototype of a comprehensive customer analytics platform. Due to these implementations, the company has since seen several use cases go live for internal users.