Finding the future

As you slide down the banister of life,

May the splinters never point the wrong way.

—Irish blessing

Recruiting can seem like a game of chance. You hope the right candidates see your job posting, pray the information they provide is truthful and accurate, and keep your fingers crossed after they’re hired that they will perform as promised. However, is recruiting really about luck? Does pure serendipity lead you to the candidate pot of gold at the end of the proverbial recruiting rainbow?

The Six Steps of Relying on Luck as a Hiring Strategy:

1.      Locate a candidate with a resume detailing every skill and experience you ever wanted.

2.      Interview the candidate and find they are blessed with the ability to perform expertly on such occasions.

3.      Reflect on how lucky you are to have found a perfect hire.

4.      Discover that, once on the job, the candidate proves shockingly ineffective and otherwise unfit for the job and your culture.

5.      Curse your bad luck.

6.      Repeat.

In this age of big data and the accompanying insights available to discern between candidates and what they have to offer, do we rely on luck to recruit successfully.

 Is your recruiting strategy aligned to impact overall business performance?

I can recall a conversation with a friend at leading a national recruiting team in the healthcare industry during a record-breaking year. They had filled 5000+ positions, but in the organization’s operational performance reviews, challenges with staffing remained a topic of conversation

After many collaborative and brainstorming discussions with the sales and operations departments, they were able to identify the key areas related to recruiting that were truly performance drivers for the company. As a result, they developed additional ways to measure and track performance, and develop strategies that would help to achieve those goals. They truly had to take a step back and look at how they could prioritize their recruiting efforts in addition to what impact this would have on the business. Various results showed that it’s the delicate balance between quality and quantity that can sometimes make a huge difference in the staffing process.

When a department or business unit has a staffing need, most managers typically view theirs as most important and highest priority. However, in a world where recruiters are managing a high volume of open requisitions, and recruiting resources are limited, it’s important to give the right level of focus on each position (which means sometimes a bit more, others get a bit less). Ultimately, the requisitions that will produce the greatest impact to the business’s revenue will get more attention and focus. We have to take a step back and ask ourselves; “is the focus on the low hanging fruit”? To further clarify, are the positions that are easier to fill getting the majority of time and resources in order to elevate the number of positions filled each month? When this happens, we leave the vacancies that are more impactful but harder to fill lingering around with an unacceptable amount of time to fill them.

So, how do we transition to truly focus on the requisitions that will provide most impact when filled? The first thing is to give those a higher level of priority than others. Let’s take it a step further.

How could we think about prioritizing recruiting efforts:

Big Data, Predictive Analytics and Hiring

Sports industry, especially cricket, in India is booming at a rapid pace. Not only on the pitch but also in terms of sports-based application. Technologies like artificial intelligence and machine learning are becoming the backbone of those platforms now. While the betting industry is already leveraging ML and AI, the sports bodies like BCCI have only now started to make use of the emerging tech to make critical decisions.

The Board of Control for Cricket in India (BCCI) this week announced the 15-person India team for the upcoming ICC World Cup. But while the list was announced by chairman MSK Prasad and acting secretary of the Indian board Amitabh Chaudhary, the decision, which also had some controversial changes, was also to some extent inspired by data analytics. Rishabh Pant, the critically-acclaimed wicketkeeper and Ambati Rayudu a fiery batsman have been left out of the team by the selectors, which caused many to raise eyebrows.

CKM Dhananjay, the Indian team’s data analyst, had prepared a special package doing an entire SWOT analysis of the World Cup hopefuls as well as the opposition since the 2017 Champions Trophy. The five-member national selection committee was given a three-and-a-half hour presentation one day before the meeting in order to give them a clear picture of the players’ performance. This presentation also included an in-depth predictive analysis of the weather conditions to be expected in England from May to July 2019.

So far, the BCCI selectors have only been looking at raw data such as match scores, runs, strike-rates and wickets, among others.

However, this report answered questions such as:

·        How does the English cricket team fare on their home ground?

·        How does New Zealand fare against Indian wrist spinners?

·        How has Australia negotiated our orthodox left-arm spinners during a particular phase of play?

·        What was Kedar Jadhav’s strike-rate during a particular phase of play?

·        What are the specific conditions when the top three [batsmen] have faced trouble against any particular opposition?

·        What are the kinds of bowling attacks expected?

·        Who among the Indian players can be successful against specific bowlers?

Hiring By Algorithm

The use of predictive hiring analytics is surging. In a time of corporate belt-tightening, the cost of a failed hire is too great to ignore the potential benefits of these new methods. Corporate giants, including Google ,Sears, Wells Fargo are embracing the power of data and analytics to vastly improve the success rate in talent acquisition and retention, and the days of the traditional job interview are rapidly disappearing.

Consider the realities of finding and retaining top talent:

Nearly a quarter of all new hires leave their company within a year of the start date.

Younger workers are no longer viewing a job as a lifetime commitment, and most are constantly on the lookout for a better opportunity. Thus any tool that helps identify a better hiring decision is going to be invaluable. Gartner, the research firm, predicts that data will grow by 800 percent over the next five years.

Search firms are incorporating predictive analytics into their recruiting activities.

In truth, measured talent assessment is not new.. But the advent of more sophisticated digital technology has vastly increased the effectiveness of these assessments. More and more companies are now trying to get on the bandwagon of analytics by saying they have online cloud-based analytics for predictive hiring, but often it means they’ve just developed another online personality test. You have to be careful about sifting through a lot of bad data that is out there.

Perhaps more important in this move toward Big Data is the ability to analyze and understand the information that is being gathered. Many organizations are doing all types of assessments, but what comes out of the graphs, bar charts and numbers?

Google Leads the Way

At Google, the HR function is called “People Operations,” and under Laszlo Bock, the leader of that organization, Google has become the gold standard for hiring analytics. Indeed, all hiring at Google is based on data and analytics and is guided by a “people analytics team.” Given its meteoric growth—from its founding—Google is clearly focused on finding and hiring the best and the brightest.

Google’s workforce productivity is off the charts. Reportedly, on average, each employee generates nearly $1 million in revenue and $200,000 in profits each year. It would appear that Google’s

Google determined that “G.P.A.’s and test scores are worthless as a criterion for hiring. They found they don’t predict anything. Instead, Google has focused on ways to use data to measure leadership skills, cognitive ability, humility and ownership.

Google has four key goals:

·        Using analytics to expand the candidate pipeline and bring more talented people into that pipeline.

·        Using analytics to improve decision making and identify the best candidates.

·        Making the candidate experience remarkable. Every candidate should have a “magical” inter-view experience and a “magical” hiring process.

·        Making the hiring process fast and efficient.

Google receives 2 million resumes every year. And even though the Company has a very thorough and rigorous hiring process, sometimes strong candidates who would be a great fit don’t get hired. Google doesn’t want to lose out on these great candidates, especially if it happens for these wrong reasons.

The people analytics team created a systemic approach to reviewing rejected candidates and established metrics for scoring the resumes of prospects who were turned away. From this, a new list of candidates is created and shared with internal recruiters, staffing teams and hiring managers to decide whether to call back these candidates for an open role.

 In other words, at Google, “don’t call us, we’ll call you” is not just lip service. The system has apparently generated more than 20,000 return visits for rejected candidates since 2012.

Google has also incorporated best interviewing practices through research. First, it cut down its onerous 12-interview process to no more than four for non-technical jobs and five for technical jobs. Not only was this process incredibly stressful for candidates, but having studied every interview ever done at Google, the group found patterns of predictability that changed the company’s outlook.

Google has concluded that its most predictive interviewer was actually the wisdom of the crowd. In other words, Google didn’t find that any particular interviewer group, like senior Googlers or long-time employees, were better at identifying who we end up hiring. It is really the average score of four interviews. The wisdom of the crowd was correct 86 percent of the time in picking the best candidate, and any additional interview beyond that added only one percent more accuracy.

Google also used analytics to revamp its interviewing process, removing brainteasers and similar challenges because they didn’t find any connection between an ability to solve those puzzles and a successful candidate. Instead, Google identified its best interviewers and introduced consistent rating scales and a common set of questions that each interviewer must ask. Every Googler who does interviews must attend regular training so that they all know what constitutes a bad answer and what makes a really great answer.

The results are stark: Google has halved the hiring-process time, and candidates have a far better hiring experience. And Google has never wavered from its core value: embracing smart people who are excited to do cool things, who love solving problems and love to learn and collaborate with others

The Human Touch

Analytics are a transformative force of our age. It turns out they improve decision making in all walks of life—not by a huge amount, but there is a little edge to be gained everywhere. In some areas, humans are still pretty good at hiring, and there are aspects of the recruiting process that still need the human touch. It’s folly to hire someone without meeting them and talking to them.

One of the things we know about this selection process is that if we went on a purely analytic basis, we’d make very bad decisions. Conversely, if we went purely on gut instinct, we’d also make very bad decisions. So somewhere in this, the analytics help us raise the bar for the pool that we’re looking at, and then we have to use really great interviewing techniques and analytics to help us make a better decision.

How will talent management evolve in the age of Bigdata

 Reliance on a resume and a round of interviews, the popular-but-flawed conventional approach, is going the way of the dinosaur. With these new approaches, the numbers are all there, and you can eliminate the weirdness and discrimination that goes into personal judgment. If you can get lots of quantitative data, you can really improve the allocation of talent and make people more fulfilled in their careers and companies more profitable by having the right people in the right job.

 Predictive hiring analytics is not a panacea; it’s more a potent tool than a magic bullet.