Even as more organizations buy and implement Big Data architecture, many aren’t considering how many people they’ll ultimately need to mine insights from all those datasets.
Analyst firm McKinsey & Company predicts that, by 2018, there’ll be a shortage of 1.5 million data experts. One route to mitigating that potential shortfall: train more tech pros in the minutiae of data analytics. That’s a tremendous opportunity for candidates who want to enter the Big Data space.
What You May Already Know
Within the Big Data community, there’s a perpetual need for math and science skills, especially algorithmic knowledge. Ajit Jaokar, a teacher at the University of Oxford and founder of Futuretext, a practitioner course in data science for the Internet of Things (IoT), has observed PhDs from various math and science disciplines leaping into the Big Data arena.
“I also believe domain knowledge, the ability to solve problems and to apply complex ideas in new domains will be key,” Jaokar said. “These two areas, algorithms and business knowledge, are the ancillary skills employers could look for with potential to retrain.”
Tamara Dull, director of Emerging Technologies for SAS Best Practices, a thought-leadership team at SAS Institute, believes that one critical (and subtle) data-related skill is the “ability to discover the unknown unknowns.”
While traditional data technologies have taught us to model data and find answers according to defined questions, the new era of Big Data has room for variables. “This leaves the door wide open to not only discover known unknowns with our data,” Dull added, “but also discover the questions we never thought to ask before. If a candidate can demonstrate this skill, it should position them well ahead of the pack.”
Big Data experts must also learn how to communicate effectively across teams and departments. As data becomes increasingly influential in the enterprise, cooperation and “soft skills” grow correspondingly crucial.
What You May Need to Succeed
When it comes to Big Data, which hard skills are in demand? Apache Hadoop, the open-source framework that allows for data-crunching on large hardware clusters, remains a favorite, and the programming language R is popular among many data scientists.
But Jaokar believes that tools such as Python and R, although relevant, may not matter as much as the ability to solve large, complex problems, build predictive data science models and understand distributed processing.
While it’s become significantly cheaper and faster to store and process data, Dull added, none of that additional capacity will matter unless candidates know how to analyze data, discover unknown unknowns, and gain insights that actually add value. “I strongly recommend getting into analytics, such as SAS,” she said. “And for those who are more interested in data ‘plumbing,’ I’d focus on Apache projects, which are fueling most of the big data technology movement.”
How to Stand Out in the Crowd
Dull offered three ways that candidates can make themselves memorable in a Big Data context:
- Become a great data storyteller. An Excel sheet or pie-chart no longer captures attention. You need to tell a great story that highlights your data and shows why the latter is so important to an organization.
- Become a data privacy advocate. The increasing reach of Big Data has brought privacy discussions to the fore. If you can persuasively outline the nuances of the debate, people will turn to you as an expert.
- Become an expert in cybersecurity. Powerful tools have made it easier than ever for bad actors to penetrate even highly secure systems. Knowing how Big Data can help crack through security—as well as defend systems from intruders—will make you a valued contributor to pretty much any organization.
And do your research: knowing best practices of firms that rely on Big Data is the easiest way to implement your own data-analytics solutions.