Most people segregate the regular data away from big data in their thinking process. The structured relational or non-relational data is often thought of as the regular data and the data in web logs or files is thought of as big data. Traditionally, managers who are successful in last few decades embraced such thinking and reaped great career benefits. However, the new breed of managers have begun to look at the data assets quite differently as they evolve into formulating new strategic drives.

Conceptually, it may be true that big data assets have quite a few differences with the traditional data assets. However strategically, this isolated thinking is leading to segregating the data organizations into two silos. This can make long-term information strategy costly, ineffective and suboptimal for the organizations.

Traditional extract, transform and load (ETL) has existed since the times when data warehousing evolved to help move data from legacy mainframe applications.  Therefore, data movement from files to relational or dimensional databases for the consumption by reporting engines has been the focus of ETL. Even in the data world today where most focus has been on data visualization or analytics or business intelligence, data professionals recognize the importance of effective ETL engines as the backbone.