An Alternate Perspective on how Accurate ETL and Data Integration Improves Business Intelligence (BI)by Cisco Systems, Inc.
Published on: 01/20/2012
The extract, transform and load (ETL) and data integration processes that populate a data warehouse offer ample room for efficiency and accuracy to the business. Tapping into this, however, requires a small but powerful shift in perspective on how to schedule the jobs that comprise these processes.
Consider the fact that ETL processes are often scheduled using job schedulers that do not easily extend to jobs beyond the data integration platform. Yet, ironically, ETL processes must extend across the entire enterprise, because business intelligence (BI) requires data from diverse applications and data sources. Shifting from a focused point-solution model to managing ETL processes from a broad enterprise automation perspective would obviously yield smoother ETL workflows across diverse systems.
Read this brief white paper and learn how many BI teams, such those at Electronic Arts (EA), are shifting their perspective on extract, transform and load (ETL) job scheduling to an enterprise-oriented view, elevating efficiency and offering greater control and data accuracy.
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