AUTOMATED DATA QUALITY MONITORING SYSTEMS FOR ENTERPRISE DATA WAREHOUSES

Authors

  • Noori Memon,Suresh Sankara Palli

Keywords:

Data Quality, Enterprise Data Warehouse (EDW), Automated Data Quality Monitoring, Data Governance, Machine Learning, Artificial Intelligence (AI), Real-time Monitoring, Data Integrity, Anomaly Detection

Abstract

In today’s data-centric world, storing and handling vast data is possible because of Enterprise DataWarehouses (EDWs). The worth of these systems greatly rely on the trustworthiness of the datathey possess. Ensuring data is correct, reliable and on time is possible today with the help of Automated Data Quality Monitoring Systems

References

Barroso, L.A., Hölzle, U. and Ranganathan, P., 2019. The datacenter as a computer: Designing warehouse-scale machines (p. 189). Springer Nature.

Cai, L. and Zhu, Y., 2015. The challenges of data quality and data quality assessment in the big data era. Data science journal, 14, pp.2-2

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Published

2023-07-20

How to Cite

Noori Memon,Suresh Sankara Palli. (2023). AUTOMATED DATA QUALITY MONITORING SYSTEMS FOR ENTERPRISE DATA WAREHOUSES. Journal of Computational Analysis and Applications (JoCAAA), 31(3), 687–699. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3616

Issue

Vol. 31 No. 3 (2023): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.