Companies are collecting data from many more sources today than in the past. The growing majority of all this new data is unstructured in nature, meaning that traditional storage repositories struggle to handle it. As a result, enterprises are increasingly employing data scientists to help them make sense of all this new data. This has put a major strain on the supply of institutionally trained data scientists who are expensive to recruit in the first place.
The effects of this reality are two-fold. Companies are having to get creative in how they tackle what they are seeing as a major data problem. First, they are deploying more advanced software tools, like those that offer data discovery and self-service, providing autonomy and on-demand analysis. However, these software solutions do not exist in a vacuum, and enterprises need intelligent folks to put them to good use. This is where the emergence of the citizen data scientist comes into play. Bringing data democratization to end-users across the entire enterprise, these novice data scientists are unlike their highly-trained data scientist peers in that they have no formal or educational training.
Citizen data scientists are largely made up of business analysts and other personnel that may have experience working within an organization’s data architecture and using software tools to derive valuable business insights from data. Companies are using the citizenry to bridge the gap between their data and the discoveries they acquire from analysis.
All over the world organizations are evaluating their in-house talent to determine which individuals have the potential to develop in this role. This reality has been forced on data-driven companies since data scientists are so expensive and difficult to court. This is all becoming possible due to the advanced feature enhancements being introduced into Data Management tools and the explosion of the Hadoop platform, making it easier for companies to store, organize and prepare large data sets for use within Business Intelligence software.
The birth of the citizen data scientist has made widespread data democratization across the enterprise possible. The role is really still in its infancy though, and organizations will need to make the judgement as to whether users with no formal training can handle the rigors of a pseudo-scientific position. After all, there’s a reason data scientists are paid a median salary of $110,000. The hope is that by democratizing data on a mass scale, users will gain confidence in their abilities to use the software solutions at their disposal to gain answers to important business questions. Users who are more involved in the nitty gritty of their own work also prove to better understand crucial business processes.
According to research and technology firm Gartner, the number of citizen data scientists will grow five times as fast as their highly-trained counterparts. Gartner adds: “The convergence of data discovery and predictive analytics will help organizations bridge the gap between diagnostic and predictive analytics capabilities. It will also enable them to progress along the analytics maturity curve. The convergence will help predictive analytics reach a broader audience of business analysts and citizen data scientists. It will also increase the usability of predictive analytics tools.”
The work that data scientists have done in the enterprise to help turn Big Data into big insights is one of the main factors in the success of modern data collection. But considering the lack of easily-acquirable skill, the price at which that skill comes with, and the fact that data tools have advanced to the point that a wider audience of users can feel comfortable using them, a new era in Big Data may be upon us. The use of citizen data scientists allows for a company-wide focus on data that cannot be had any other way. Giving more users access to the data that runs their day-to-day operations makes a whole lot of sense.
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