Is data engineering overtaking data science


When Harvard Business Review first touted the data scientist as sexiest job of the 21st century  back in 2012 the role was still in its infancy. The promise of advanced analytics and the insights that business could gain – about their customers, their interactions, their products and everything else were rightly identified as potential gold.

Data was the new oil and data scientists were the prospectors, the drillers, the refiners and the distributors rolled into one. At the time I suggested that the data scientist function would need to be fulfilled by a group

With the benefit of hindsight it became clear that data science is an end game – in fact most data scientists spend more time finding and preparing data for analysis than they spend on actual analytics.  The critical skill for data science is less analytics modelling and more data preparation – good old fashioned, tedious data management work that has always been needed for analytics. This data engineering capability is being seen as separate to data science

This lead me to ask, last year, “Is data science still the sexiest job of the 21st century?

This morning I saw the following question posed on Quora, the popular Q&A platform:

Is a data engineer more in demand than data scientists?

The various responses are worth reading in their own right but broadly speaking the realization that data engineering from more than 60% of the effort of any data science program, combined with the reality that data engineering is also the practical part of the deliver means that data engineering is overtaking data science as the primary role player in most analytics teams, and is in equally short supply,

This makes the requirement for automation of routine data engineering tasks, such as data ingestion and data cleansing even more critical.

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