Efficient analysis and visualisation of after-sales data through an automated data pipeline
Using the AWS cloud platform to process after-sales data through an automated data pipeline
After-sales data such as information on complaints, customer satisfaction surveys or customer loyalty measures is often stored in different locations, different formats and for different purposes. Insular information management systems like these are known as data silos, and make evaluation of the data very time-consuming if not impossible. Cloud-based data analysis and visualisation using an automated data pipeline can be the perfect solution when it comes to optimising after-sales activities and strengthening customer loyalty. And machine learning methods provide useful support.
Requirements of the machine learning solution
The goal is efficient data analysis with an integrated analytics platform
Integration of heterogeneous data sources
Structuring of the data
Creation of an automated end-to-end data pipeline
User-friendly implementation of dashboards to visualise the data
AWS cloud platform – the perfect solution for collecting and structuring after-sales data
In order to properly structure after-sales data, we’ve developed an automated cloud solution on AWS. This enables the integration of heterogeneous and distributed data sources. The data is imported automatically at regular intervals, and a centralised data repository is built to store all data securely in one place (avoiding the problem of data silos). By implementing an end-to-end data pipeline, data can be processed, transformed, aggregated, cleaned and analysed. A clear and simple visualisation of the data is created using Tableau.
At a glance: Added value through the use of automated data pipelines
Fast, efficient decisions
Available data is relevant and meaningful
Data can be evaluated and displayed quickly and easily
Data is readily accessible to a wide range of users on custom dashboards