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In my last article, I discussed the increasing impact of automation on business and the displacement of jobs. With artificial intelligence looming as the ultimate disruptor, the overall theme of job displacement has shifted more towards knowledge-intensive jobs which would of course include data scientists. The article ended on the note that this article would look at the future of the data scientist in a world increasingly more influenced by automation and artificial intelligence. In this second article, we want to focus its impact on the analytical file which represents the information inputs into any predictive analytics solution.
The year is 2029 and you are a newly hired junior data scientist where your primary role is to build predictive analytics solutions. What would you be expected to do? In order to better understand and appreciate the current role of the junior data scientist, it is important to understand what the role might have looked like in 2019. In 2019, the specific programming demand would have been for individuals with knowledge in R, Python, SAS, or a number of other more traditionally intensive computer-based languages such as Java or C++. High levels of mathematical and statistically-based knowledge would also be expected to be one of the core skills requirements for a junior data scientist. No question that the key skills requirements would have more of a technical bent rather than the softer skills which might translate to business knowledge and how these technical solutions would be applied in a business setting. The thinking in 2019 would be that the tech skills are the immediate need. Meanwhile the organization’s training and internal development programs would build those softer skills of domain knowledge and how to practically apply these solutions within the given business. The development of this hybrid would be ever-evolving with tech skills as the initial foundation complemented by increasing domain knowledge. The more successful hybrids would comprise those data scientists who would ultimately end up in executive-level positions.
Now let’s forward to 2029 and what might be the requirements of the junior data scientist. In an age of artificial intelligence and increased automation, the need for coding and programming will be minimized. But what does that mean to the junior data scientist of 2029? Information and data will still be analyzed and the need for an analytical file will remain as one of the core steps within the data science/data mining process and certainly in the development of any predictive analytics solutions. Yet, it is the tools which will improve in order to enable the data scientist to create the analytical file. We are already observing evidence of this through a number of vendors that offer GUI interfaces where the user clicks on icons that represent a certain data function. At the end of this process, the user ends up with a map of all the different processes and tasks which were required to create the analytical file. The analytical file can then be used to develop models or to produce reports and tables. There is no need for programming as the tasks and functions in creating both the analytical file and the required reports/tables are represented by drop-down icons.
This ability to facilitate the creation of the analytical file process is…
