As the business world quickly adopts new digital platforms from customer relationship management software to social media, collections of data continue to grow, exceeding the expectations of many while coming as no surprise to others. Either way, experts agree that these new compendiums of data are critical to the success of organizations competing in today’s corporate environment.
Called big data, this enterprise IT trend has evolved dramatically in the past few years, and now businesses demand solutions for managing, analyzing, visualizing and extracting detailed insights from vast collections of structured and unstructured information. With some help from the cloud and better means of finding and comparing data, big data and the analysis of it have become IT department mainstays, and as a result, most organizations are looking for data scientists. These professionals – only sometimes referred to as IT professionals – leverage mathematics, computer science and business expertise to develop big data programs that not only prove valuable but inspire insightful change to corporate practices and policies. In essence, these individuals develop tools or models for analyzing and visualizing actionable information in accordance with business needs.
The road to big data is paved with data scientists.
However, there is a problem with regard to data scientists: Businesses are struggling to find a proverbial unicorn who can create optimal data analytics programs, discover insights and apply them to basic corporate rules. According to IBM’s Rob Thomas, there are either PhD-possessing data scientists or “fake data scientists,” InformationWeek reported. These two types of professionals cannot feasibly support a world built on big data. Those with expensive degrees will expect larger salaries that many businesses simply cannot afford, and the “fakers” are essentially useless.
Thomas suggested the solution lies in the “80 percent” of space between data scientists with PhDs and those who have no idea what they’re doing. Those 80 percent of individuals are the sweet spot with some expertise and smaller paychecks.
Power from the people
Enter citizen data scientists. Gartner defined these tech professionals as people who work in predictive and/or prescriptive analytics but are actually masters in a separate field, technique or IT sector. Citizen data scientists won’t usurp big data power from those with PhDs. Instead, they will become the core of analytics at organizations that don’t demand the most cutting-edge data science initiatives.
The fact of the matter is that there are not enough data scientists out there, and IT professionals who pursue a citizen data science role could find a wealth of employment opportunities. Additionally, with so many businesses in a variety of industries trying to take advantage of big data, there are likely jobs in dozens of sectors with a multitude of different positions and responsibilities.
“Citizen data scientists are now required.”
Simply put, citizen data scientists are now required, and every type of organization will be looking for these tech professionals.
Who can be a citizen data scientist?
IT professionals looking for a new sector, a different job or even a way to spice up their typical everyday duties should consider filling the shoes of citizen data scientists. This is because they can come from any background. Programmers can use toolkits to analyze or visualize data, networking professionals can ensure that data flows smoothly between systems, data storage experts can find efficient ways to store information and there is even room for artificial intelligence experts. Obviously some backgrounds are better than others, as Thomas specifically pointed to those with experience in the cloud being good citizen data scientist contenders.
Contributing to Forbes, Margaret Harris of Oracle said that there are generally three different types of data scientists that the majority of organizations are looking for. First, there are data scientists who have a strong background in IT and are familiar with programming for big data platforms. As a note, however, anyone with experience using big data-affiliated languages would be a good fit here.
Second, Harris pointed out that those adept at developing models would be valuable data scientists. They know the steps to take to extract actionable information from complex corporate workflows. These tech professionals could also understand how to uncover predictive models.
Lastly, many citizen data scientists are more familiar with the line of business than the nitty-gritty technical details. IT department managers or supervisors fall into this category along with some professionals who couldn’t be further from being tech experts.
Hone those skills
Citizen data scientists might be proficient in some areas and not so much in others, but there is one constant that all potential big data professionals should remember: Citizen data scientists need training and mentors. The whole corporate world must foster the growth of these PhD-free individuals, as there just aren’t enough data scientists with specific big data degrees to go around. With support, any IT professional can become a citizen data scientist.