July 30, 2019
July 30, 2019
Contributor: Jackie Wiles
Data can uncover sources of digital talent you might not have considered.
What do a health insurer, a car maker and a bank have in common? The answer is their talent needs. They are likely competing for many of the same people and for skills that, until recently, were only on an IT recruiter’s radar.
Those skills, such as big data, 3D printing, cloud hosting and virtual reality, are what companies now need to pursue digital opportunities — from product enhancements like mobile- or chat-based apps to vastly disruptive business transformations of product offerings, go-to-market channels and operations.
“Strategic workforce management has changed drastically in the past 10 years, and the complexity is only increasing as the labor market tightens. Organizations are competing for skill sets that were previously unheard of but are now in extremely high demand,” says Alex Johnston, Group Vice President at Gartner. “Simply put, it is harder today than it has even been to plan for, find and hire the talent we need.”
Gartner TalentNeuron™ data confirms the converging demand for talent today. Forty-nine percent of all job postings by S&P 100 companies in 2018 were for just 39 roles — all of which require in-demand skills such as data analysis, advanced coding and solution selling.
Data can also reveal untapped opportunities. When it comes to both forecasting your digital talent needs and finding that talent, “Data is the essential ingredient for the digital age,” says Ashley Tatum, VP, Advisory, Gartner. “Laying different datasets over one another provides clarity and insight with which real decisions can be made about sourcing strategies during digitalization.”
Before you even look at labor market data, first pin down the talent requirements and criteria you need for a particular role or to grow a particular capability. For example, now that automakers are developing self-driving cars, they need a wide range of IT capabilities, from artificial intelligence engineers to software developers and machine learning specialists.
Ask yourself what kinds of skills and capabilities you will need to create digital products, customer experiences and operational efficiencies.
Once you’ve identified those needs, data can locate untapped or less obvious candidate pools to augment current approaches to sourcing talent for emerging digital roles.
Here are just five examples of opportunities you might not have considered, but data can reveal:
The cost of labor is always a critical consideration, but data can provide a rich comparison of geographies and the associated costs and benefits.
One U.S. credit reporting firm with a major hub in São Paulo, Brazil expects to save $50 million over a 5-year period by relocating certain roles to cheaper cities that still meet hiring commitments made to the government.
A global telecom and networking company invested $2 billion in a large R&D talent expansion in Canada instead of Eastern Europe or Asia — potentially saving 20% to 25% compared to building a talent hub in the U.S. — while maintaining high quality standards needed for the region’s clients.
Do you always search first in locations with big talent pools? This is an obvious approach. But as a result, places with a lot of talent are also hotbeds of demand, especially for new roles sought across industries. By analyzing data on both talent supply and demand, you can generate more options, depending on your other requirements.
One U.S. computer hardware company utilized an always-on data crawl to compare the supply of talent in different locations and forecast future talent pools and labor costs in existing and potential operating centers to frame decisions about opening and closing operations in various locations.
Data can identify the next tier of cities where digital talent pools are still nascent. In these locations, you can stretch limited recruiting resources and pull specific HR levers, such as recruitment advertising or relocation packages, to expand your pipeline of suitable candidates and build your talent pool.
One computer software company wanted to tap into the growing pool of R&D talent in Eastern Europe. The obvious choice was Warsaw, Poland, given its large pool of talent, but competition for talent was also very high there. Data identified emerging sources of R&D talent in alternative locations, including Budapest, Hungary, and Bucharest, Romania. The company opted to establish smaller, high-impact R&D centers in multiple locations, which helped ramp up the team faster than would have been possible from a single, centralized location.
In searching for digital talent, you’ve probably noticed already that you’re increasingly bumping up against adjacent industries and companies looking for the same skills. But don’t just see this new competition as a threat; see the opportunity. Use data to identify nontraditional competitors and find out which companies are actively hiring for the talent you want, and add these less-obvious sectors to your own sourcing criteria to build a larger, more viable candidate pool.
Given the hypercompetition for mature and emerging digital roles, it’s important to target entry-level talent pipelines that are the foundation of future capability growth. Recruiters are always keen to build relationships with flagship schools well-known for producing certain IT capabilities, but data can identify other places in which talent with certain skills is located, for example, career or technical institutes that are producing IT talent for certain job families such as software engineering.
These schools provide an ancillary but potentially lucrative role in campus hiring programs. One Asia-based computer software company, seeking to serve its U.S. user base more effectively, used data to build a list of university towns in which there was ample IT talent. This gave the company access to a cost-effective and renewable talent pipeline for its IT professional services teams in the U.S.
Data provides other opportunities to inform key aspects of your process for recruiting in the digital age and finding talent internally. Two examples:
This article has been updated from the original, published on March 6, 2018, to reflect new events, conditions or research.
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