Data Science Connections

Are Data Scientists Jobs Targets for Automation?

While I think quite a few jobs and activities are optimal targets for automation, I don’t think that the data scientist role is likely to be one of them.  “The need for human creativity and innovation in data expert-level tasks will always be needed.  Robots won’t be able to think outside the box when data interpretation calls for new data model methods to be applied or built to solve unknown or hidden business issues as they arise” (Covington 2016, 183).  While software tools like PowerBI and Tableau can handle a lot of analytics and drive a lot of insight, they are not as strong at predictive analytics that a data scientist would tackle with more advanced tools and thinking.

While I do agree that there will likely be a spike in the need for data scientists, followed by a decline, I don’t think we will be there in 5-10 years but maybe in 50.  Currently, few people are proper data scientists and to achieve those credentials requires 10-20 years of multi-disciplinary experience.  I think we will see a large rise in this role, but not everyone is suited to all the requirements of the job and it will stay relatively niche in my opinion.  I don’t think the demand for data scientists will ever be as large as the variety of analyst roles, or even data translators.  However, I do think they will be critical components to many organizations attempting to build solutions in complex environments where predictive analytics are needed.  At max, I would think there would be slightly less data scientists than total executive level positions.  Ideally, if each executive had their own data scientist, there would be enough question answering solution creation to satisfy the market easily enough.

I think data translators will be a much more ubiquitous role, both because they are paid less and the time to develop the skills requires takes less time.  I think the data translator role will see more of the rise and decline predicted for the data scientist role by Covington.  However, I think that will be more an artifact of categorization.  The role of data translator is likely to be morphed or phased out in the future in my opinion as it becomes ingrained in the culture.  With more reliance on simplification tools, like PowerBI or Tableau, management will need to shift to more data-informed decision-making structures.  With digitization and decentralization of data and decisions, I can envision a future where managers become more like data translators, relating their team’s operational business data with the strategic direction from the executive teams, allowing managers to not only translate the data upwards, but also act upon it directly in a more efficient organizational structure.

As someone who has been honing data scientist skills for the past 15 years, I don’t think that the next twenty years will see a decreased need, in fact it will increase in that time period.  Data scientists will likely be the executive-level analyst that is delivering large strategic analytics for organizations.  However, I do feel it will be a very niche role and that data translators will be required at a much higher degree and will be tapped for management roles and to handle most of the operational analytics.

Author: Logan Callen


Covington, Daniel. 2016. Analytics: Data Science, Data Analysis and Predictive Analytics for Business. 5th Edition. South Carolina: CreateSpace.

Harvard Business Review Press. 2018. HBR Guide to Data Analytics Basics for Managers. Boston: Harvard Business Press.

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