The onslaught of big data is well-documented, and the consensus is that the technology that has enabled its rapid proliferation has far outpaced the ability of human study (and matriculation rates) to keep up with the resulting need for analysis.
Big data technology has impressed many businesses, stimulating their appetites for building the future now and allowing them for the first time seriously to consider turning former data-driven pipe dreams into attainable business goals. So, the excitement has grown right along with the technology's promises, and cross-industry demand for data scientists has risen dramatically.
As highly trained and dedicated professionals specializing in the science behind data, data scientists (a.k.a., “quants” or "data analysts") were being snatched up as the most obvious choices to satisfy these new and expanding data needs. Of course, the law of supply and demand kicked in, so these data scientists became very expensive to keep and maintain. Now the perfect, fully rounded, platform-agnostic data scientists—if the perfect ones ever existed at all—are nowhere to be found in the open market. Searching for one is like looking for a unicorn: it would be magical to find one, but good luck with that.
Question: What is a self-respecting business to do when faced with the rising costs of resources that are deemed necessary to take revenue to new heights?
Answer: Find a way to achieve the same results through alternate, possibly less expensive means, without waiting for the future crop of data scientists to graduate from college, despite the fact that more and more colleges and universities have responded to the skills gap with the establishment of data science degree tracks.
Everyone was looking for unicorns. What they found was better.
Not so long ago, in editorials written not so far away, they were called “accidental analysts,” those who would use and manipulate data without benefit of substantial, formal statistical and algorithmic training. Today the popular name is “citizen data scientists.” Whatever you call them, the rise of such data handlers is not by accident, nor is it by design. It is simply by necessity.
Think of it like your cable or satellite bill—why would you want to pay for a pre-arranged subscription package that contains much more than you need? The lack of unicorns has brought businesses to the realization that maybe they don’t need one or two full-blown data scientists who can do EVERYTHING. Instead, they can assemble citizen data scientists who can do what is NECESSARY.
In a recent article, Shawn Rogers, chief research officer of Dell Statistica, rightly observes the economics of the situation: “Not every company can afford a data scientist, which is a big reason why citizen data scientists will become a big part of the data ecosystem as it evolves.”
In the same article, Innovation Enterprise’s Laura Denham states pragmatically, “Everyone in the organization needs to be able to leverage the data to some degree, and it cannot simply be left to one highly trained individual sitting at the top of the firm dishing out insights.”
"Why not?" you may ask. Because insights would take too long! Anymore, data analytics technology has increased user expectations for rapid turnarounds in data processing as well as in decision-making. The ever-shortening patience of customers and employees alike simply won’t tolerate the turnaround times associated with traditional, centralized decision-making models. (Unless, of course, there is an assurance of more accurate results, as noted in this video chat between Rogers and Dell’s Joanna Schloss.)
But who would be the data processors and decision makers in such a decentralized scenario? Generally, that would be the line-of-business (LOB) users who are working with data collected at critical process points. If only these people could engage effectively with the data—especially in real time—then they could provide quick decisions that improve quality, maintain efficiency and impress customers. These are the citizen data scientists, and there are probably many such potential analytics users in your own organization.
How is it even possible to custom-train your own substitute unicorns?
It is possible because the main ingredients for successful data analysis include curiosity and creativity, not technical expertise. For instance, Robert Murphy, managing partner at Movéo, suggests in his Webbiquity guest blog that the top six characteristics necessary for success in a data-driven marketing world include creativity, curiosity, communication skills, a knack for strategy, a desire for continuous learning and statistical/technical expertise. Notably, he listed the technical expertise last.
Why is this? As author Simon Sinek says, "You don't hire for skills, you hire for attitude. You can always teach skills." Analysts come from all walks and disciplines; the technology is teachable, so anyone with the right aptitude and attitude is trainable. Simply put, you can’t teach curiosity or creativity as easily as you can teach enough technical expertise for your people to be effective with data analysis and model building, specialized for your business needs and performing in concert with other citizen data scientists.
To support this burgeoning market, Dell has been retooling its Statistica predictive analytics platform to make it easier for citizen data scientists to perform their duties. In the latest 13.1 release (to be generally available in mid-June 2016), citizen data scientists will find it easier than ever to build and reuse workflows, configure in-database processing with three simple steps, compare and deploy advanced models, conduct visual analyses and drill-downs, and find patterns through new network analytics. With Statistica 13.1, Dell encourages its customers’ citizen data scientists to apply data science to the most important questions in their organizations, improving the speed and relevance of data science projects.
To see some demonstrations how Statistica 13.1 will be a boon for citizen data scientists, register now for our free June 21 webcast, “A Day in the Life of a Citizen Data Scientist.”
