A Multi-Dimensional Framework for Measuring Enterprise Data Engineering Maturity in Cloud-Native Data Platforms

Abstract

This study proposes a multi-facet cloud-native maturity model of enterprise data engineering. The report employs the following dimensions as the key dimensions to determine the readiness of an organization: automation, scalability, governance, DataOps, and observability. Normalized cloud performance indicators determine the levels of maturity using a machine learning model of a Random Forest. This model has an accuracy of 0.875, indicating that it is a good predictor. The good performance is demonstrated by the precision, recall, F1-score, and AUC values of the ROC. The results underline key drivers of maturity, namely automation and governance. The framework provides a basis to assess and advance to an improved level of enterprise data engineering maturity in current cloud-based setups in a systematic, scalable manner.