Experts believe that data science will be replaced with business science in future

It is been so long since Harvard Business Review declared data science to be the sexiest job in 2012. Unfortunately, if we look back at how data scientist role is performing in the technology sector, it is more like the profession is slowly dying. Experts too think that the world is overrating data science professions throwing data at off-the-shelf algorithms.

If we consider the 'best jobs' ranking from 2017 to 2019, we see the data scientist role being dramatically losing its place. Data science played similar to 'business analyst' position in the 2010s. Business analyst was taken as the coolest job in the 1980s. However, as technology evolved and more professions came into the digital radar, the business analyst profession lost its spotlight. Experts predict that history will repeat itself in data science's case. More and more emerging trends are occupying the workforce every day. Humans are being replaced by better humans and robots. As automation takes centre stage, a lot of tech-related jobs are losing its ground in the digital era. Henceforth, it is time that you choose wisely and keep updated on trends to not end up being a genius data scientist with no job.

Why is data science dying?

Data science is a blend of various tools, algorithms and machine learning principles with the goal to discover hidden patterns from the raw data. Data scientists do exemplary analysis to discover insights from data and also use various advanced machine learning algorithms to identify the occurrence of a particular event in the future. A data scientist will look at the data from many angles, sometimes angles not known earlier.

The concept of data science came to existence when John Tukey wrote about a shift in the world of statistics in 1962. He mentioned "as I have watched mathematical statistics evolve, I have had cause to wonder and to doubt… I have come to feel that my central interest is in data analysis." In his statement, he was referring to the merging of statistics and computers at a time when statistical results were presented in hours, rather than days or even weeks if is done by hands. In 1974, Peter Naur authored the book 'Concise Survey of Computer Methods' using the term 'data science' repeatedly. In 1994, Business Week ran the cover story 'Database Marketing' revealing the ominous news companies had started gathering large amounts of personal information. However, the data science took centre stage starting from 2010.

Unfortunately, data science covers a lot of subjects in it. The term 'data scientist' might be appealing to hirers, but it involves a lot of expectations and diverse responsibilities. Companies expect data scientist to develop and investigate hypotheses, structure experiments, and build mathematical models to identify the optimisation points. We gather exploding amount of data every day. The role of a data scientist is to determine data and get answers that will decide the future of a company. However, the evolution in the technology landscape is driving data science to its demise by streamlining three different trends- the automation of individual workflows typically performed by data scientists, the creation of data products effectively taking away certain repetitive part of the job for data scientist and finally a move towards higher value-added work. People are switching form data science career for various reasons like manager's interpretation, unsuitable infrastructure and uncertainty.

Workflow automation is becoming common

Workflow automation refers to the design, execution and automation of processes based on workflow rules where human tasks, data or files are routed between people or systems based on predefined business rules. Since workflow automation minimizes human labour and maximizes revenue at a low cost, an increasing number of companies are choosing the emerging technology over hiring a data scientist. For example, with the S3 or Athena combo offering and easy setup data-lake on Amazon or Lambda functions powering model development, most of the works that take a data scientist to set up a proper and scalable infra has been outsourced to these cloud providers.

Data products eliminate repetitive tasks

Data products are tools or applications that process data and generate results. Businesses can use the results of such data analysis to obtain useful information like churn prediction and customer segmentation, and use these results to make smarter decisions. For example, CRM platforms have moved heavily towards data products by offering propensity models, recommendation engines, automated AB testing, etc.

Higher value work will become next 'data science'

As mentioned above, data science has taken business analyst position in the 2000s. The same will happen in future. Some other higher position will take over data science's place in the digital world. Experts anticipate that it could be business science. The term 'business science' underlines the multi-disciplinary and context-based aspect of the technology sector. Business science is the implementation, understanding and learning of interactions between process, data or systems and people. All of this is done with the goal of being successful in business, which is providing a good or service to the public.