Thought process

Someone's mental procedures or cognitive activity based on interpretations, past experiences, reasoning, problem-solving, imagining, and decision making make up their way of thinking or their thought process.

One can only guess how particular individuals will actually think, or their exact thoughts at a given point of time or during an activity, or what thought process they will use to accomplish their objectives, but in general terms, a data developer may spend more time thinking about data convenience (making the data available as per the requirements), while data scientists are all about data consumption (concluding new ways to leverage the data to find insights into existing issues or new opportunities).

To paint a clearer picture, you might use the analogy of the auto mechanic and the school counselor.

An auto mechanic will use his skills along with appropriate tools to keep an automobile available to its owner and running well, or if there has been an issue identified with a vehicle, the mechanic will perform diagnosis for the symptoms presented and rectify the problem. This is much like the activities of a data developer.

With a counselor, he or she might examine a vast amount of information regarding a student's past performance, personality traits, as well as economic statistics to determine what opportunities may exist in a particular student's future. In addition, multiple scenarios may be studied to predict what the best outcomes might be, based on this individual student's resources.

Clearly, both aforementioned individuals provide valuable services but use (maybe very) different approaches and individual thought processes to produce the desired results.

Although there is some overlapping, when you are a data developer, your thoughts are normally around maintaining convenient access to appropriate data resources but not particularly around the data's substance, that is, you may care about data types, data volumes, and accessibility paths but not about whether or what cognitive relationships exist or the powerful potential uses for the data.

In the next section, we will explore some simple circumstances in an effort to show various contrasts between the data developer and the data scientist.