Enterprises 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, organizations 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 affects of this reality are two-fold. Organizations are being forced to exhibit some creativity in tackling what is increasingly being called a ‘data problem.’ 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 peers in that they have no formal education.
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 is all becoming possible due to the advanced feature enhancements being introduced into BI and analytics tools, as well as the explosion of the Hadoop platform, making it easier for companies to store, organize and prepare large data sets for use within enterprise middleware.
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 analyst house Gartner, Inc., business users will make up a greater share of their organization’s analytics output than professionally trained data scientists by next year. The researcher argues that this trend will come as a result of the proliferation of self-service capabilities in enterprise settings, turning line-of-business personnel into formidable data analysts. Gartner recommends addressing four key areas when planning to deploy self-service BI and analytics, which you can read about in more detail here.
The work that data scientists have done in the enterprise to help turn big data into big insight 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 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.