Strategies for recruiting data analytics and related skills

 

Do employers truly understand their own dire need for data analytics, or more broadly, data science and analytics skills? A new report says that by 2020, new job postings that require these skills will hit 2.72 million. There is a concerning gap between the expectations of educators and the expectations of business executives when it comes to getting students ready for the job market. That is according to a study released by the Business-Higher Education Forum and PwC.

If you are like most employers, in the next several years you will prefer job candidates with data science and analytics skills. And yet, only 23 percent of educators believe their graduates will possess those skills.

The report makes concrete suggestions for both employers and higher education. Here, we will highlight the recommendations for employers who need to harness skills in data science and analytics.

What exactly are data science and analytics skills?

According to the report, “The term analytics refers to the synthesis of knowledge from information. It’s one of the steps in the data life cycle: collection of raw data, preparation of information, analytics, visualization, and access. Data science is the extraction of actionable knowledge directly from data through either a process of discovery, or hypothesis formulation and hypothesis testing.”

People who need to make data-driven decisions include directors in Human Resources, Marketing, IT, and the C-suite. Data science jobs include systems analysts, data administrators, business intelligence analysts, data engineers and much more.

This skills gap affects much more than just data scientists. Jobs from the C-suite to the frontlines are increasingly affected by the need for analytics. According to the report, this is a revolution. “As with the revolution in work brought on by the personal computer (PC) 30 years ago, data science and analytics, hand in hand with machine intelligence and automation, are creating a new revolution in work.”

Businesses who do not attract and retain talent in data science and analytics will eventually be outcompeted.

What does a business do to attract and retain skills in data science and analytics?

The report details four recommendations to employers:

1. Look beyond the diploma and hire for skills, too.

It’s time to admit that a degree is only a proxy for skill sets. While recruiters can argue the effectiveness of using proxies, it just doesn’t work with DSA skills. The market for these skills is full of disconnected dots. STEM grads are not necessarily prepared to use DSA in business, and business grads are not necessarily taught DSA skills. There is a growing number of DSA degrees, but they haven’t been around long enough for many recruiters to trust their viability, let alone assume they will make the list of annual campus visits.

Where does this leave us? According to the report, “It is left to hiring managers and recruiters to determine how candidates meet skill requirements in this changing environment. To do that they need two things: 1) a common nomenclature to trade in DSA competencies and skills; and 2) a closer, more collaborative relationship with higher education aimed at creating programs that will provide job candidates with the skills they need.”

Researchers have identified skills common to data science jobs across broad skill groups. Those are:

• Applied domain skills (research or business)
• Data analytics and machine learning
• Data management and curation
• Data science engineering
• Scientific or research methods
• Personal and interpersonal communication skills

Employers shouldn’t expect to find all of the above skills in one individual. Rather, they should use these skill groups as a guide to forming teams whose members collectively have a full skill set.

These skills fall into three categories that employers should assess: data analysis, decision-making and problem-framing:

Rod Adams is US Recruitment Leader at PwC. He says that while the diploma is required, PwC also hires “for specific skills in data literacy, communication of data, and how students can link data to business value.  We have also developed learning curriculum for our interns to emphasize the critical importance of these skills and encourage them to consider this in their course selection as they finish their education.”

2. Invest in market-driven programs that link learning with work

The private sector is largely untapped for data science and analytics program funding. According to the report, “few colleges and universities say private funding is a primary source of funding for DSA coursework.”

If an employer is considering funding a market-driven DSA program, it should look for programs that are:

• Applied
• Experience-building
• Aligned with the mission and academic reputation of the institution
• Collaborative
• Diverse and non-exclusive

PwC, for example, is launching a five-year, $320 million commitment called Access Your Potential™. It aims to focus on providing tools, training and mentoring to students, educators and guidance counselors across the U.S. with the goal of closing the opportunity, education and skills gaps. They will help more than 10 million students in underserved communities gain access to financial capability and technology skills curricula as well as equip 100,000 teachers and guidance counselors with tools to prepare and guide students in making sound financial choices and understanding tech-based careers. The commitment will also leverage the financial acumen and technology skillsets of PwC’s 46,000 Partners and staff, as well as build upon the firm’s network of educators and nonprofit collaborators.

3. Know the roles: structure your people plan for the digital economy

Because employers do not yet fully understand the nature of DSA skills, “they are not reshaping their people strategies to support the acquisition, development, and retention of people with these skills,” according to the report. “Many companies have adapted some of their human resources practices to accommodate workplace innovations, but often, strategies and systems are essentially unchanged—even as new technologies are changing the nature of work.”

An effective people plan, then, must clearly define the skills and competencies for each role in a company, “not just for data science teams, but for all the roles that are linked to those teams. A comprehensive plan requires an assessment of how to deliver people with the right skills, right knowledge, and right experience to the right places—now and in the next three to five years.”

Unless a company develops a plan to align its people to its digital strategies, it’s unlikely their digital strategic investments will live up to their potential.

When hiring, employers need to identify the barriers to entry. We put up barriers to keep out the people who don’t fit, but especially in recruiting for DSA skills, those barriers might be keeping out the right people.

Screening practices for these skills must recognize that highly qualified candidates might come through non-traditional paths. To improve their screening, employers should:

• Beware unconscious bias
• Don’t overstate educational requirements; do consider alternative credentials
• Use contests to spot talent
• Create mentor-apprentice relationships
• Tap into talent exchanges

Diversity initiatives are a big part of this, as it forces employers to see how barriers to entry may disproportionally affect some groups of candidates more than others. Adams at PwC says that they provide training and tools to help increase people’s “awareness and understanding of differences and why they matter, so their actions can contribute to our inclusive and high-performing workplace culture.” For example, PwC trained people about “unconscious assumptions that can potentially shape our perceptions, decisions and behavior – and our ability to be effective and inclusive leaders.” Before offering a promotion, an employee would be required to take that training, and all new hires take the training as well. In addition, PwC created a Women in Technology program that “empowers women at all levels by creating opportunities for leadership development, mentorship and more.”

4. Prioritize life-long learning

In addition to hiring new roles in data science and analytics, existing roles will evolve to demand more of these skills across an organization. Training and development are essential. The report cited a Gallup poll that surveyed executives. Half of the survey respondents “said that within the next three years, greater data and analytics skills will be required of everyone in operations, finance and accounting, and marketing and sales in their companies.”

In addition to traditional learning and development methods, employers should consider Massive, Open Online Courses (MOOCs). PwC, for example, collaborates with Coursera to offer a training called Data Analysis and Presentation Skills. They encourage everyone in the whole firm to take this training. In addition, says Adams, all of their training courses “include skill development using the appropriate technology tools.”

In addition, employers can signal to people what they want. Show your people a map of where their competencies may lead them, implying what areas they need to strengthen. For example, name the competencies that spell out success for their job and their next potential job. Next, provide a list of resources or course options that would help them build the skills necessary to advance.

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