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Following recent news about automating data analytics, there is a vocal contingent who maintains that data scientists are irreplaceable. Max Kanter, the leader of a recent MIT research report regarding automation, lowered the cause for alarm among data analysts by stating that, “We view the Data Science Machine as a natural complement to human intelligence.” 

Others agree that together, automation and data scientists create a powerful analytics duo -- and both are necessary for success. "It’s hard to see automated analytics completely replacing data scientists, just as it’s difficult to imagine automation and converged systems eliminating the need for IT professionals," Chris Nerney with CSC writes. "But if automation speeds up the process of data analysis, it could be a powerful tool in human hands."

What Makes Data Scientists Valuable?

In a world where data analytics is at the forefront of most business leaders' minds, data scientists remain the most sought-after technology experts. Data scientists are even said to have, "the sexiest job of the 21st Century."

Experienced data scientists have a perfect storm of skills that make them rare and valuable. Experienced analysts have acquired technical skills and an understanding of computational analytics, combined with sharp business acumen. They tweak and manipulate an analytics engine throughout the duration of a project, and identify patterns and relationships among data sets. Then, they translate those insights into a user-focused, goal-oriented presentation layer. Their job is an art as much as it is a science. 

Because so few people have the total package of business, analytic, and computational skills, it's estimated in a McKinsey report that by 2018, the United States could face a shortage of 140,000 to 190,000 "people with deep analytical skills." Moreover, the Harvard Business Review reminds us that data analysts are difficult to hire and retain due to the competitive market requiring their services.

Software Needs People

Some businesses opt to engage in analytics using a SaaS data analysis product. Stakeholders assume that when they take this route, they can designate analytics tasks to their IT team. But relying on a product that does not include the services of a trained data scientist typically leads to failed analytics projects.

Data scientists are intimately familiar with the analytics backend on which they work, and also have experience across many different business sectors. This knowledge allows them to identify insights from multiple data sources.

Unlike a simple software product, data scientists have ability to work closely with clients, identify core business goals and unique user needs, then dive into the nuances of an analytics backend. With this understanding, analysts devise meaningful, customized presentation layers.

Solution as a Service Joins Software and People

Vendors with a Solution as a Service (SolaaS) approach to data analytics understand that for an analysis project to succeed, it's necessary to work with skilled data scientists. With SolaaS, a single vendor provides both the analytics backend and the services of highly trained data analysts.

The SolaaS analysts have a deep understanding of the challenges faced by businesses across a variety of industries. This knowledge, plus their drive to collaborate with clients, helps them deliver a customized visualization layer. Ultimately, SolaaS recognizes that an analytics project's success depends on the services of people as much as the software.

Summary

There has been much talk in the analytics industry about automated analytics and whether it will replace the need for data scientists. Regardless of how powerful automated analytics will become, there will always be a need for the human element in any analytics project. Solution as a Service recognizes the valuable role people play in analytics and marries a powerful backend with the services of the industry's most experienced data scientists.