Performing extensive extract, transform and load (ETL) may be a sign of poorly managed data and a fundamental lack of a cogently developed data strategy. I realize this is a provocative statement; but, in my experience, there is a direct correlation between the extent of redundant data, which is a serious problem, and the amount of ETL processes. When data is managed correctly as an enterprise asset, then ETL is significantly reduced and redundant data is completely eliminated. This leads to tremendous savings in maintenance and greater efficiency in new development while improving data quality.
Let's examine why ETL exists and the root cause of redundant data. ETL gained in popularity as companies began to outgrow antiquated legacy systems. As functionality was moved from the legacy system to more robust, open-systems architecture, ETL played an indispensable role in moving the data. Unfortunately, many companies failed to completely retire their outdated systems. As a result, they are now maintaining duplicate data and the ETL processes that create it.
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