Over the last decade, most companies have completed a digital transformation. This has produced unimaginable volumes of new types of data and much more complicated data at a higher frequency. While it was previously apparent that Data Scientists were needed to make sense of it all, it was less apparent that someone needs to organize and ensure this data’s quality, security, and availability for the Data Scientists to do their jobs.
So in the early days of big data analytics, Data Scientists were very often expected to build the necessary infrastructure and data pipelines to do their work. This was not necessarily in their skill sets or expectations for the job. The result was that data modeling would not be done correctly. There would be redundant work and inconsistency in the use of data among Data Scientists. These kinds of issues prevented companies from being able to extract optimal value from their data projects, so they failed. It also led to a high rate of Data Scientist turnover that still exists today.
Today with the onslaught of completed corporate digital transformations, the Internet of Things and the race to become AI-driven, it is crystal clear that companies need Data Engineers in abundance to provide the foundation for successful data science initiatives. This is why will we continue to see the role of Data Engineers grow in importance and breadth. Companies need teams of people whose sole focus is to process data in a way that allows them to extract value from it.