data science

Whitepaper: 7 Trends for Data Science in 2021

Livia Eichenberger Blog, Data Science

Management Summary

Over the last decade, the field of data science has not only dramatically gained in interest but has also changed and developed considerably. Given the new technological advances and the steady increase in data, we expect data science to keep evolving in the future. In particular, our research suggests the following seven key trends for the next year:

  1. Answering the Question of Why
  2. Modelling Uncertainty
  3. Automation in Data Science
  4. Democratization of Data Science
  5. Data Science Operations
  6. Ethics of AI
  7. Specialization of Data Science Roles

The first five topics can be divided into two main trends, and points nr. 6 and 7 can be seen as resulting trends thereof.

The first main trend, including the first two points, is to find new ML/AI solutions to solve important issues that current ML/AI models are still not capable of solving yet: Answering the Question of Why (i.e., modelling cause and effect) and Modelling Uncertainty. Only when models can grasp causality and uncertainty they can become truly intelligent and provide us with reliable and safe decisions and actions. Therefore, academic institutions, as well as large technology companies, are investing heavily in these areas of machine learning and AI. The next challenge for data scientists will be to understand these models and apply them in practice to create data-driven value for businesses and organizations.

The second main trend, including points 3 to 5, is to bring current ML/AI models into production to create data-driven value. The democratization of Data Science and Automation in Data Scienceis bringing current ML/AI models to a much broader audience so that techniques and solutions of Data Science Operations to put these models into production will become principal drivers in the data science solution landscape. As a result, new issues concerning the Ethics of AI will arise. Such issues relate to ensuring that these technologies do not harm (in a broader sense) humans or other morally relevant beings. To respond to the trends mentioned above, we expect a necessary specialization of Data Science Roles. Thereby, the demand for roles such as Data Engineers and ML Engineers is expected to grow in the upcoming years substantially.

To best prepare your organization for these emerging trends in data science, we have identified three key actions for each trend that you can already take today. Get ready and leverage the future of data science to create sustainable data-driven value for your organization.

Über den Autor
Livia Eichenberger

Livia Eichenberger

I am a data scientist at STATWORX and especially interested in Causal Machine Learning. I love the logic in data science and the beauty of neat and structured code!

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STATWORX
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