Using Big Data to Solve Your Tech Talent Problem
From Silicon Alley to Silicon Valley, everyone’s battling for tech talent. For every Groupon, Pinterest, Dropbox, and SurveyMonkey there are plenty of startups with less familiar names seeking engineers, designers, product specialists, and data scientists to grow their business. But with more jobs open than there are actively searching candidates, how can startups and established organizations win the war for talent?
The key is to reach candidates long before they start actively hunting for their next job.
Companies in fast-growth mode cannot wait for the right candidates to come to them – they need to proactively go out and find their talent. Engaging passive candidates (i.e. candidates who aren’t actively applying to new jobs) is an important strategy, but strategy is only half the battle. Reaching this elusive target requires data and the ability to mine the data, make sense of the information and make it actionable.
Reviewing a candidate’s resume or online profile may reveal information about work history, skills, certifications and other experience but these days individuals are much more than their resume. Today’s leading social media and content platforms offer an opportunity to develop rich pictures of potential hires. The last few years has seen an exponential growth in social-networking data. Every tweet, Quora answer, blog post, and other online action reveals more about an individual and adds to the data deluge.
And it’s not just personal networks that are contributing to the data growth phenomenon; while LinkedIn is considered the de facto professional network, there are other communities like GitHub, StackOverflow, and Dribble where technology professionals are leaving digital footprints and providing clues about how satisfied they are in their job and the likelihood of them looking for a new position.
Retailers like Target are combining big data and predictive analytics to understand the buying habits of consumers in order to optimize sales opportunities and increase customer engagement. Recruiters can now use big data and predictive sourcing analytics to improve the probability of predicting which candidates will be ready to leave their current position or proceed from candidate to hire.
Powerful new tools such as what are building at Entelo offer an automated approach to “predictive sourcing”, helping organizations determine which candidates are likely to make a move or show signs of coming to market. It analyzes more than 70 variables that are leading indicators of a pending job change, tracking everything from layoff announcements and merger and acquisition activity to length of time at current company and social profile activity.
Getting insight into candidate behavior and identifying the right moment when a prospect may be open to a new opportunity can make it easier for recruiters and hiring managers to get to the right talent faster. For example, a software engineer who hasn’t been promoted in two years and is employed at a company experiencing workforce reductions may be primed for an intervention by recruiters.
Availability of big data and technology that makes it easy to understand the signals buried therein can be the answer to the tech talent problem, allowing startups, growth companies and established organizations to get closer to the talent they need to innovate and achieve their business goals.