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The Future of Data Science

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  • Last Updated : 26 Apr, 2022
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For years we have heard that ‘Data Science is the future’, however, after all these years of individuals flocking into this domain, the question that begs to be answered is, ‘What is the future of Data Science?’. 

The evolution of Data Science over the years has taken form in many phases. It all started with Statistics. Simple statistics models were employed to collect, analyse and manage data since the early 1800s. These principles underwent various modulations over time until the rise of the digital age. Once computers were introduced as mainstream public devices, there was a shift in the industry to the Digital Age. A flood of data and digital information was created. This resulted in the statistical practices and models getting computerized giving rise to digital analytics. Then came the rise of the internet that exponentially grew the data available giving rise to what we know as Big Data. This explosion of information available to the masses gave rise to the need for expertise to process, manage, analyse and visualize this data for the purpose of decision making through the use of various models. This gave birth to the term Data Science. 

What does the future hold? 


  • Splitting of Data Science: 
    Presently, the term data science is perceived quite vaguely. There are various designations and descriptions that are associated with data science like Data Analyst, Data Engineer, Data Visualization, Data Architect, Machine Learning and Business Intelligence to name a few. However, as we move into the future, we would begin to better interpret and understand the contribution of each of their roles independently. This would greatly broaden the domain and we would begin to have professionals gaining expertise in these domain-specific roles giving a clearer picture of the workflow associated with each role.
  • Data Explosion: 
    Today enormous amounts of data are being produced on a daily basis. Every organization is dependent on the data being created for its processes. Whether it is medicine, entertainment, sports, manufacturing, agriculture or transport, it is all dependent on data. There would be a continuous increase in the demand for expertise to extract valuable insights from the data as it continues to keep increasing leaps and bounds.
  • Rise of Automation: 
    With an increase in the complexity of operations, there is always a strive to simplify processes. Into the future, it is evident that most machine learning frameworks would contain libraries of models that are pre-structured and pre-trained. This would bring about a paradigm shift in the working of a Data Scientist. Creation of models for analysis wouldn’t remain as their native responsibility but rather it would shift to the true analytics of the data extracted from these models. Soft skills like Data Visualization would come to the forefront of a Data Scientists skill set.
  • Scarcity or Abundance of Data Scientists: 
    Today thousands of individuals learn Data Science related skills through college degrees or the numerous resources that can be found online and this could result in newer aspirants getting a feeling of saturation in this domain. However it is essential to realize that data science is not a domain that can just be learned, it needs to be inculcated. No doubt the skills being learned are of immense importance, but these are just the tools that help to work with the data. The mindset and the applicative sense of using these tools to accomplish various analytical tasks is what makes a true data scientist. Thus it should be remembered that there could always be an abundance of individuals who have learned Data Science, but there would always be a scarcity of Data Scientists.

The future of Data Science is not definitive however what is certain is that it would continue to evolve into a new phase depending on the need of the hour. Data Scientists would exist as long as there exists Data.

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