As we continue to build a community of top technology talent, one observation pops up again and again: The frustration of poor job fit hits employers and employees alike.
For job-seekers, unsatisfactory jobs lead to stress, burnout, under-performance, and of course, attrition. It isn’t better on the other side.
Hiring managers experience equal frustration—because in their case, it’s about more than an exchange of skills and rewards. A bad hire translates into project delays and can even cause project failure, not to mention the sunk cost of on-boarding such a resource.
But here’s a question: If hiring managers knew what bad hires look like, would they on-board them at all?
What Exactly Is A “Bad Hire”?
Are skills all that set apart good hires from the bad? Even for software developers, and other technology professionals, (for whom ‘hard skills’ count for a lot!) the answer is no. A job-seeker with strong technical knowledge will still be a bad hire if he/she doesn’t make the effort to upgrade skillsets or explore areas other than his/her core expertise. For instance, at Xperti, we receive many applicants with a decidedly strong background in Java development. The ones who qualify for the best opportunities are those who regularly add to their existing skillset, with experience in Angular, Spark, etc.
So hiring managers are well-advised to measure a jobseeker’s adaptability during the assessment. But is it just technical/skill-based adaptability? What about industry norms? Save on on-boarding costs by rewarding candidates who’ve made the effort to familiarize themselves with industries other than the ones they’ve worked in.
We’ve seen the trend of headhunting passive candidates really catch up. i.e. Approaching candidates who aren’t looking for jobs. Done right, this helps create a healthy talent pipeline. Done wrong, it creates a vacuum between the organization’s existing talent pool and the one it is acquiring externally.
This vacuum provides important insights into the third—and most critical—a factor that hiring managers need to look into Cultural adaptability. In a thorough solution, candidates are tested over a wide range of areas: Professionalism, flexibility, communication skills and more.
|Relevance to role|
|Prior domain experience|
As we mention in another blog , there are reasons that exclude staffing agencies and recruiting firms from measuring these factors, and/or measuring them accurately. But does this mean managers who on-board candidates without this assessment aren’t any worse off than those who do? Is it a case of “If it ain’t broke, don’t fix it?”
According to the numbers, no.
How High Are The Costs for Technology Projects?
- Time taken to locate the resource
- Negotiation results
- Speed of onboarding
- JD/resume fit
All of which describe what happens before someone joins a project. None of these can adequately predict what happens after the talent comes on board.
Setting 30, 60 and 90-day targets helps clarify expectations on both ends, provided these targets are quantified and objective. For instance, in the case of onsite projects, absenteeism thresholds could be one measure. Above-the-line problem solving, which goes beyond the contractor’s defined JD could be another metric in the 60- or 90- day range. And so on.
This helps define what organizational culture looks like when applied to practical work situations.
Measure success over the long run:
If success is the absence of failure, is the converse also true? We know what the traits of bad hires look like, especially in technology projects. Does the absence of those traits indicate success? The answer, unfortunately, is not that straightforward. The workplace is changing. Diversity and inclusion mean that demographic and psycho-graphic assumptions about the workforce that was true two decades ago are probably illegal today. Likewise, skills that would have earned a market premium just 5 years ago are mainstream today. The point: Using short-term results as a yardstick is both misleading and damaging. Xperti’s advantage is that it is built on nearly two decades of recruiting analytics—with its database regularly replenished with new technology resources. This gives a long-term and real-time snapshot of talent equity.
Be mindful of how recruiting analytics will be influenced by the past and the future:
Past biases will damage predictive analytics. But what about our assumptions about the future? How do they influence our recruiting decisions today? When planning the contractor-permanent hire mix in resource charts, what assumptions do we make? How accurate are they? When it comes to technology projects, it’s quite possible to underestimate emergent technologies.
Bad Hires for Technology Projects – Conclusion:
Conversely, the hype around legacy software might die in the industry long before even the decision to phase it out starts at the enterprise level. In such cases, finding ‘niche specialists’ is both a challenge and a risk. Does your business depend on a solution that’s too big to dispose of with, and too old to scale? Map out your recruiting requirements against that with timelines for a realistic picture of your recruitment costs.
In subsequent posts, we’ll be covering how recruiting analytics can help minimize the incidence of bad hires. In the meantime, share your thoughts and questions about good and bad hires with Xperti today!