Big Data, Big Problem: Coping With Shortage of Talent in Data Analysis

Business.com / Hiring / Last Modified: February 22, 2017

Forty percent of companies are struggling to find and retain data analytics talent, and the picture is starting to look even more bleak.

Big data is a big deal. The sheer quantity of data generated over just the last few years far exceeds the entirety of the previously accumulated human historical data record.

Moreover, CyberCoders reports on an estimate that the digital universe will reach 40 zettabytes (45 trillion gigabytes) by the end of the decade, a 50-fold growth.

However, as Jonatahn Shaw pointed out in Harvard Magazine, it’s not the amount of data that makes it a really big deal, it’s the ability to actually do something with it.

Assuming, that is, you can harness not only the computational power, but the data analytics professionals required to sift through the “immensity of stuff” to uncover the relationships meaningful to your business and your customers.

According to a 2015 MIT Sloan Management Review, 40 percent of the companies surveyed were struggling to find and retain the data analytics talent. And the picture is starting to look even bleaker.

International Data Corporation (IDC) predicts a need by 2018 for 181,000 people with deep analytical skills, and a requirement five times that number for jobs with the need for data management and interpretation skills.

Related Article: Dizzying Data: What the Rise in Data Complexity Means for Business

Given the explosive growth on the help-wanted boards for data analytics experts and the intensifying competition to fill more jobs than there are qualified people, what can your company do to attract and retain talent?

Campus Recruiting

Deloitte’s Analytics Trends 2016 report notes that while there is a rising number of university analytics and data science programs (more than 100 just in the U.S.), they nonetheless can’t crank out enough sufficiently trained people to meet demand.

Consequently, the report recommends that companies should:

  • Actively recruit on campuses with data analytics programs.
  • Develop internships and student projects both as a recruiting tool and as a way to groom students for an efficient transition to the general business world and company culture.
  • Establish meaningful and rewarding career paths with an infrastructure in place most likely to interest and attract new talent. Consider that a Bain study of more than 400 companies with revenues in excess of $1 billion found that about a third lack the state-of-the-art tools, quality data, processes, and incentives likely to attract data-savvy professionals.

Related Article: Big Data is Redefining the Supply Chain: Putting Business Intelligence to Work

woman at desk in front of computer working on a graph

Work From Within

The strategy of Cisco Systems is much like the sports team that relies on its farm system to develop from within rather than recruiting talent from elsewhere (and which typically requires large financial incentives to do so successfully).

Cisco established an internal data analytics education program in partnership with the University of Washington and North Carolina State University.

It also established a Data Visualization Lab to provide not only a physical space with the tools to perform data analytics, but to encourage collaborative analytical problem-solving. 

Since its opening in February 2015, more than 200 employees have used the lab for a variety of projects. Initial experience was sufficiently positive, and now Cisco plans to have a dedicated visualization lab in each of its organizations by 2017.

Cisco may be on the right track here. Doug Henschen of InformationWeek argued that there are slim pickings in recruiting outside talent.

“We hate to dash the hopes of those counting on a lot of external hiring, but it's unlikely you'll fill the talent gap with recent graduates and people lured away from other companies," he wrote.

"The good news? It's a good bet you won't have to beg existing employees, particularly younger employees, to line up for training opportunities.”

Related Article: Storytelling By the Numbers: How to Make Visualizing Data Easy

Split Roles

Another approach is to redefine the data scientist role. Typically, this involves both analyzing and managing data.

The latter requires less specialized skills than advanced analytic capabilities. Offloading management responsibilities means that some of your people are freed up to do more specialized tasks.

"Advanced analytic capabilities are going to be in high demand and hard to find," noted Steve Bulmer of Datalink in an InformationWeek article. "And that's one reason I think that big data, and the roles that are required to manage and analyze big data, are going to be split."

SMART Ways to Look at Data

The best approach is probably a combination of external outreach and internal development. But before staffing up, you need to take a step back and look at what you want to do with that data.

Define your business objectives, look at what kinds of data can best help you achieve those objectives, and then develop your analytical capabilities.

This is what Bernard Marr writing in CGMA Magazine calls the SMART model, narrowing your data requirements into manageable focused areas. 

He emphasizes that, “Leaving the reporting to analysts and designers alone is as unproductive as leaving it to the executives. There needs to be collaboration and interaction between the people creating the results and the people who need the results to make decisions.”

Just because you have the data and the experts to analyze the data doesn’t necessarily mean you’ve got the answers you need. But it’s an important first step to using big data in ways that best get you big results.

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