ETL Best Practices for Data Quality Checks in RIS Databases

Checks and Balances: Monitoring Data Quality Problems in Network Traffic Databases

ETL and Data Quality : Which Comes First ?

Usually, an early task in any data warehousing project is a detailed examination of the source systems, including an audit of data quality. Data quality issues could include inconsistent data representation, missing data and difficulty around understanding relationships between the various source systems. As ETL and Data Quality technologies converge, it’s important to use the right tools at th...

Best Practices for Culturally Sensitive Data Visualizations

rights, including translation into other languages reserved by the publisher. No part of this journal may be reproduced or used in any form or by any means without written permission from the publisher, except for noncommercial, educational use including classroom teaching purposes. Product or company names used in this journal are for identification purposes only. Inclusion of the names of the...

Best Practices for Real-Time Data Warehousing

Today's integration project teams face the daunting challenge that, while data volumes are exponentially growing, the need for timely and accurate business intelligence is also constantly increasing. Batches for data warehouse loads used to be scheduled daily to weekly; today's businesses demand information that is as fresh as possible. The value of this real-time business data decreases as it ...

Quality Measures for ETL Processes

ETL processes play an increasingly important role for the support of modern business operations. These business processes are centred around artifacts with high variability and diverse lifecycles, which correspond to key business entities. The apparent complexity of these activities has been examined through the prism of Business Process Management, mainly focusing on functional requirements an...