Division of labor favorizes the tasks done repeatedly, where knowledge of the broader processes is not needed, where aspects as creativity are needed only at a small scale. Division invaded the IT domains as tools, methodologies and demands increased in complexity, and therefore Data Science and BI/Analytics make no exception from this.
The scale of this development gains sometimes humorous expectations or misbelieves when one hears headhunters asking potential candidates whether they are upfront or backend experts when a good understanding of both aspects is needed for providing adequate results. The development gains tragicomical implications when one is limited in action only to a given area despite the extended expertise, or when a generalist seems to step on the feet of specialists, sometimes from the right entitled reasons.
Headhunters’ behavior is rooted maybe in the poor understanding of the domain of expertise and implications of the job descriptions. It’s hard to understand how people sustain of having knowledge about a domain just because they heard the words flying around and got some glimpse of the connotations associated with the words. Unfortunately, this is extended to management and further in the business environment, with all the implications deriving from it.
As Data Science finds itself at the intersection between Artificial Intelligence, Data Mining, Machine Learning, Neurocomputing, Pattern Recognition, Statistics and Data Processing, the center of gravity is hard to determine. One way of dealing with the unknown is requiring candidates to have a few years of trackable experience in the respective fields or in the use of a few tools considered as important in the respective domains. Of course, the usage of tools and techniques is important, though it’s a big difference between using a tool and understanding the how, when, why, where, in which ways and by what means a tool can be used effectively to create value. This can be gained only when one’s exposed to different business scenarios across industries and is a tough thing to demand from a profession found in its baby steps.
Moreover, being a good data scientist involves having a deep insight into the businesses, being able to understand data and the demands associated with data – the various qualitative and quantitative aspects. Seeing the big picture is important in defining, approaching and solving problems. The more one is exposed to different techniques and business scenarios, with right understanding and some problem-solving skillset one can transpose and solve problems across domains. However, the generalist will find his limitations as soon a certain depth is reached, and the collaboration with a specialist is then required. A good collaboration between generalists and specialists is important in complex projects which overreach the boundaries of one person’s knowledge and skillset.
Complexity is addressed when one can focus on the important characteristic of the problem, respectively when the models built can reflect the demands. The most important skillset besides the use of technical tools is the ability to model problems and root the respective problems into data, to elaborate theories and check them against reality.
Complex problems can require specialization in certain fields, though seldom one problem is dependent only on one aspect of the business, as problems occur in overreaching contexts that span sometimes the borders of an organization. In addition, the ability to solve problems seem to be impacted by the diversity of the people involved into the task, sometimes even with backgrounds not directly related to organization’s activity. As in evolution, a team’s diversity is an important factor in achievement and learning, most gain being obtained when knowledge gets shared and harnessed beyond the borders of teams.
Written as answer to a Medium post on Data Science generalists vs specialists.
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