The rapid growth in Artificial Intelligence (AI) is being driven by its potential for making important decisions without a need for massive investment in human resources. That much makes economic sense. We know that requires a lot of data to support and inform those decisions. And we know there are massive amounts of data out there for the taking. But what has not been fully considered so far is that those data are not necessarily in a useful form. In fact, usually not.
Data engineers are those who can retrieve those raw data and conform it to the needs of the analytical tools. Data scientists are those who take these formatted or structured data and analyze it to enable decisions to be made - or, in the case of AI structure it to meet the requirements of the AI software.
There has been a tremendous emphasis on the need for data scientists in the literature. However, a recent report by Forrester points out something else. By far the greatest demand is for data engineers, not data scientists.
In other words, there is lots of data out there, and pretty good capability for using it, but the gap is in turning the raw data into something that can actually be used.
The takeaway? More attention needs to be paid to the means available to structure and format raw data into platform independent formats.