Backing away from background checks and performance ratings compounds avoidable hiring mistakes.
Hiring, retaining, and developing the best leaders, professionals, and frontline performers forms the common thread that successful leaders from Jobs to Gates, Buffet to Bezos, and Welch to Zuckerberg invoke when asked what led to their success. In books from Built to Last, Good to Great and Great by Choice, Jim Collins argues that getting the right people on the bus, in the right seats comes before working out the details of the brilliant strategic plan. In The Headhunter’s Edge, Jeff Christian made the same argument while outlining a talent-centric investment strategy for VCs, PE firms, family offices, and CEOs. While advances in financial, information, logistics, and engineering technology eat away at those sources of strategy execution risk, there are forces taking human capital risk in the other direction. This post documents what those forces are. A follow-up post covers how innovations in natural language analytics offer hope for Reducing Human Capital Risk.
Sources of Increasing Human Capital Risk
Performance ratings, whether by a single manager or multi-rater, are going away.They are already gone at: Accenture, Adobe, Con Agra, Cargill, Deloitte, Eli Lilly, the Gap, Intel, Juniper Networks, Medtronic, Microsoft, and Sears, among others . Some have argued that performance ratings contained little information about the talent being rated anyways . But the solution to bad data isn’t worse data, as in the frequent collection of team leader micro-opinions on team members, as advocated by Buckingham and others. The solution is unbiased analytics based on big data. More on that Friday.
Background checks are under attack and in retreat, according to an article in the Wall Street Journal . That makes it harder to avoid the kind of Insider Risk that has generated large corporate fines and rumbling about criminal executive culpability. The initial response here has led in the direction of digital forensics using the rusty dull blade of Boolean search strings that have never worked well for finding top talent, much less detecting Insider Risk. The digital investigators grind through a company’s digital data trove of emails, documents, texts, postings, and presentations looking for specific words or phrases or combinations thereof that point to risky behavior or risk propensity. That’s where big data can produce big positive impacts, but Boolean search delivers too many false positives and false negatives to have much detection power. More on Friday concerning what works better.
Trends leading to more hiring mistakes drive the third source of Human Capital Risk. Hiring mistakes not corrected by background checks or performance management get compounded over time, leading to costly and sometimes fatal levels of Strategy Execution Risk. Just ask the former executives at Kodak, RCA, Wang, Compaq, General Foods, Arthur Anderson, Wards, American Motors, and the list goes on. Finding the very best talent for strategically pivotal roles begins with attracting the right talent pool, screening it down to a manageable number, and then selecting the best talent via final decision interviews. This post focuses on the screening and selecting bits.
Accurate screening at scale requires valid online assessment tools. At from 60 to 400 respondents for each posted position, manual phone screening just isn’t feasible. Neither has it proven to be accurate in any case. But here’s the paradox. Candidates will complete short, fun, science-free online assessments. They just aren’t accurate– (i.e. make mostly hiring mistakes with few hits). At the other end, traditional multi-item assessments with long, repetitive, text-dense testing deliver reasonable accuracy, but candidates abandon them in shockingly high numbers. Even when taken on a PC, completion rates top out at around 50%. When taken on a smart phone, completion rates for a 20+ minute process fall to single digits . So savvy hiring managers should be asking themselves, “What are my chances of hiring the top 1, 5, or 10% talent I want, when completion rates are so low?” Good question, and the number isn’t encouraging.Help is coming on Friday.
The final source of rising Human Capital Risk lies in the broken behavioral interview. I should know. I published the initial research and wrote the book on Behavior Description Interviewing back in 1986 . The problem here isn’t that behavioral interviewing doesn’t work. The problem is that hiring managers attend the interviewing courses, eat the doughnuts, and then mostly keep doing what they have always done– which is to pay much more attention to how candidates answer questions than to what they say. Hiring managers remember one or two behavioral questions from the workshop, but retreat to their tried and tired generality and opinion questions. They rarely re-direct candidates who stray from behavioral answers. They take mostly illegible notes, even to themselves, if they take notes at all. They rate answers after the interview if they rate answers at all, which most don’t. Then they wonder why they don’t get the better talent found in the published research on behavioral interviewing when the research participants actually followed best practices. Fortunately, natural language analytics called Latent Semantic Indexing offers proven power to solve the three main sources of increasing Human Capital Risk. But by now you know the drill.Stay tuned. Friday.