The success of any company is directly related its people. In fact, 71 percent of CEOs view human capital as the top factor for sustainable economic value, according to a report by Harvard Business Review, with 43 percent saying that investing in people is a major priority.
It’s no wonder, then, that employers and recruiters are continually improving their methods of finding, hiring and retaining top candidates. And data analytics looks to be the new hero in HR and recruitment.
Hiring managers have always relied on data to make decisions, career expert Heather R. Huhman writes. IQ tests and skills aptitude tests have been relied on to determine whether someone should be hired or fired. The shift to big data just means the questions will become more nuanced and the answers more telling. Now, hiring managers can use data to assess whether a candidate will be the best fit for the role, how long they will likely stay with the company and what their levels of engagement will be.
This is a similar idea to that suggested by Kandarp Sharma at Ascentt. Data such as university grade-point averages, educational qualifications and work history have always provided insight for determining a candidate’s potential. But these days, HR teams gather data from personality traits such as the candidate’s overall happiness quotient. This adds another layer of assessment beyond skills and experience.
Below, we address the power of data analytics, how hiring managers can use it and why they should.
Looking at LinkedIn
LinkedIn is launching the beta phase of its Talent Insights this year, writes Ingrid Lunden at Techcrunch. She explains the self-service, big data analytics product will make it easier for recruiters to delve deeper into statistics for hiring and employment.
LinkedIn says its Talent Insights will give organizations on-demand access to its real-time data and insights on talent pools and companies. The reports will provide “easy-to-understand visualizations of your data for exporting and sharing, with business leaders and key stakeholders,” adds Eric Owski, head of product, talent insights and talent brand at LinkedIn.
This information will come from talent pool and company report functions, with the former providing insight into where candidates live and the industries and companies in which they work. It will also provide hiring managers with information to help gauge the best schools from which to source candidates, and how employees are engaging with their company.
Company reports will give recruiters access to data about companies’ workforce, the talent they lose and gain, the dominant skills in the organization and help determine how best to attract candidates.
Having the Right Tools and People For Data Analysis Is Vital
Data is not much help without the requisite analysis. Recruiter Michael A. Morell says the challenge is not mining data, it’s understanding it. This requires the “right tools to analyze it, and the right people who can provide meaningful insight.” He stresses that the best hiring teams will use the available tech in a sophisticated way.
For instance, technology will help reveal the right candidates, but hiring managers will need to know how to weight the data points to provide the best possible fits for a position.
Morell adds that personal interaction and communication remain powerful sources of data — often more effective than publicly available information about candidates. These personal sources include whether or not they responded to an email and how quickly; if they showed up at an interview as promised; plus of course references. Each data point will need to be weighted in the job/applicant matching algorithm.
Data will change how HR teams are built and function, write co-authors of the Talent Analytics in Practice report from Deloitte Insights, Josh Bersin, John Houston, Boy Kester. They argue that big data will help HR leaders develop people analytics teams that combine multidisciplinary skills, and long-range plans to ‘datafy’ HR.
The problem, they write, is that hiring managers and recruiters still need to understand its true potential.
Data Analytics Gives Meaning to Information on Resumes
One such potential area is candidates’ resumes. Paul Nelson at Search Technologies says “scanning resumes for keywords alone can’t provide sufficient success metrics.”
Instead, companies need to use statistical models and predictive analytics to “look beyond keywords and into semantic analytics for both structured and unstructured content, extracting metrics like industries, companies, job titles, skills, experiences, certifications; and then compare them to the job descriptions.”
Processing all this “granular data” can present and rank top candidates with “the highest probabilities of success in the position, speeding up the hiring process and boosting fill rates,” Nelson says.
The Predictive Power of Data Analytics
Resumes are just one source of data. The recruitment process itself provides a “treasure trove of data” that can lead to predictive power as to whether or not a candidate will be a high performer and good cultural fit, writes Ian Cook, head of workforce solutions at Visier Analytics.
He says data analytics banishes gut decisions, replacing them with a wholly scientific process.
However, Cook warns of the limitations of technology common to hiring professionals. “Applicant Tracking Systems’ (ATS) usefulness to recruiters stops at the point of hire,” he says. The problem is that most hiring managers leave the ATS data on one system and the subsequent data on other systems. That siloed nature of information is obviously limiting and what makes it hard to assess and predict the long-term success of a hire.
Cook goes so far as to say an ATS may even eliminate suitable candidates from the pipeline because it will preclude candidates with job experience that does not exactly match what’s stated on the job description.
The solution is to combine all the bodies of data — from sourcing to hiring and beyond — into an interactive and central system. This will give accuracy, predictive power and the ability to compile more complete data profiles.
Data Improves Feedback From Hiring Managers to Recruiters
Tied to the above issue of siloed data is the lack of interplay between relevant parties and the data they hold.
HR industry thought leader and professor of management at San Francisco State University, Dr. John Sullivan says data can also improve the relationship between recruiter and hiring manager. Normally, hiring managers only have contact with recruiters when a vacancy is being filled and communication ends when the new hire starts. This means there is no feedback loop between the two as to whether the new hire was worth the effort. Analytics about new hires can solve this problem.
Analytics Helps Retain Staff
Talent analytics doesn’t just help find the best candidates, it also allows companies to track employee productivity and efficiency. Meghan Biro, founder and CEO of TalentCulture refers to it as “an incredible predictive tool, a trustworthy future-caster, HR’s own crystal ball.”
Jacob Koshy at Prompt Cloud sees the value in data analytics in making the most out of an organization’s staff. By knowing what employees are capable of, where they excel and what their interests are, employers can use this data to provide better opportunities to team members. This means employees are more likely to stay at a company as they are being engaged and challenged at optimal levels.
HR and data analytics expert Tracey Smith says that in this tough job market, retention and recruitment continue to be major focus points for businesses and it is no wonder such emphasis is placed on analytics. She reveals how she used data to help a global tech company work out why their star employees were getting poached by the competition. The company wanted to know where to focus its retention efforts. The data showed that three specific, highly specialized job roles were being targeted and the exact levels of experience of the poached employees. With this knowledge, the company could focus on keeping talent close.
Data Analytics In Action
Jeremy Stanley, former VP of Data Science at Instacart, sets out a real-world example of hiring data scientists at the firm, which he says is more efficient than traditional interviews, saving time and energy.
The process involves a take-home test assessing a candidate’s ability to solve a series of difficult challenges, and a full “data day” spent working beside the team on open-ended challenges.
Stanley writes: “We manage this process as a funnel. Of 500 inbound applicants, 250 (50%) will submit a take-home test, 25 (10%) will pass, 20 (80%) will come to the data day, 4 (20%) will pass the Data Day, and then 3 (75%) will accept the offer. That means in order to find a single great hire, we need over 150 applicants.
“The key levers to pull here are (A) the quality of the applicants in the funnel, (B) the success rates in submission of take-home tests and attending a Data Day, and (C) the accuracy of the take-home test and Data Day filters.”
Analysing data within this framework means candidates can be assessed at each stage in relation to where they were sourced from, so hiring managers can “identify higher performing channels, and the stages in your funnel that are filtering too aggressively.”
Data analytics can provide hiring managers with predictive power and more accurate insight into candidates’ skills and their suitability to companies and roles. It can also point them to where the best candidates are located. The savvy hiring professional will work at incorporating data analytics within their holistic hiring strategy.
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