If you belong to the world of data science, you must be aware of the fact that there is a critical crisis of appropriate data science talents or data scientist. Amid such cutthroat competition that prevails in the current times, some organizations are trying to bridge the skill gap by putting AutoML tools to use in front of citizen data scientists along with the warning of it potential backfire.

Back in 2016, Gartner coined the title "citizen data scientist" to address data professionals who use advanced software like AutoML packages to develop predictive analytic applications. Although the primary job of citizen data scientists lies outside statistics and analytics, their knowledge of the business and access to new tools turns them into a valuable asset to the organization who can tackle simple and moderately sophisticated analytical tasks.

As organizations are increasingly prioritizing the usage of more advanced predictive and prescriptive analytics, the skills of traditional data scientists to address attached challenges can turn out to be expensive and difficult to come by. In such a scenario, citizen data scientists can be an effective and efficient way to fill in their shoes. Organizations can fully make use of the abilities and capabilities of such professionals. Amid this accelerating need of data science capabilities, AutoML tools prove to be a great package to jumpstart their initiative and projects.

Gartner predicted that 40 percent of the data science tasks will be automated by 2020, using AutoML tools and platforms due to better software and a constant shortage of data science talents. It was also noted that the ranks of citizen data scientists are growing five times faster than full-fledged data scientists. While, without a doubt, the wave of the citizen data scientist is trending, there are certain experts and business leaders who are not hopping on the bandwagon of this momentum. They believe that citizen data scientist may exceed at certain tasks, but there is a possibility that they fail in others due to lack of specialized data science training. According to such leaders, citizen data scientists will help implement predictive analytics across a range of fairly commoditized use cases but may quickly feel out of their depth with more advanced use cases that deal with more challenging data sets.

On the other side, in some organizations, when a full-fledged data scientist tries to use AutoML tools, he often feels too constrained as certain aspects of his job are already automated including – accessing data sources, spinning up compute environments, ensuring the correct drivers are in place and tracking models over time. Although such automation only involves DevOps part, not the statistical reasoning part.

Some experts believe that when an organization relies on non-experts to build predictive applications to regulate industry demands, they unintentionally increase their financial exposure followed by unpleasant events. The critics are concerned about the possibility that such unspecialized professionals might develop models having risks associated with them due to lack of deep understanding of the statistical fundamental for models.

With the emergence of new technologies and flourishing advancements of data science, organizations need to select the right professional to tackle challenges associated with the continuous developments. The basic element that makes a huge difference between both sets of professionals is that citizen data scientists can handle easier use cases, but well-versed data scientists are efficient enough to face tougher challenges. For better data science prospects, co-existence is the quintessential key.