Is Data Engineering Needed in Product Development?

We have electrical engineering, mechanical engineering, structural, electronics, civil,etc. Each of these engineering disciplines has a specific set of formulas, principles and heuristics that govern now the engineering discipline is performed. We codify these rules to protect the general public. Would you feel safe driving over a bridge that was engineered by a person who didn't study structural engineering?

In a world where most products produce or consume data, we need to understand how to engineer the data. The impact of improperly engineered data can be just as catastrophic as that of an improperly engineered bridge. Incorrectly collecting and  analyzing data can lead to improper interpretation which can lead to decisions that turn out to cause harm. Any product that generates or consumes data should have a data engineer on the team.

A data engineer is not a programmer or a data modeler (although these skills may be useful) but a person that addresses issues in designing, building, managing, and evaluating advanced data-intensive systems and applications. The data engineer studies big data techniques, information theory, entropy (the measure of uncertainty in a random variable). A good data engineer has both the ability to manipulate data and an understanding of the analytic purposes to which the data are going to be used.

There is a danger in engineering today that each engineering discipline operates independently without thinking about the product as a system as a whole. There needs to be a person on the engineering team who can “bring it all together”. A person who knows enough about each discipline to synthesize all aspect of the product, mechanical, electrical, data.

This TED talk by Marco Annuniziata, The Chief Economist at General Electric, explains why data is so important to products and my extrapolation, why data engineering is critical to product development teams.