Category Archives: Big Data and HR

Quality of Hire: A Vaguely Valid Metric?

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In November I wrote about Linkedin’s 2016 Global Recruiting Trends Report (you can re-read it here) and took them to task about their methodology. Turns out they did a bit of a miscalculation and corrected data that looked askew. Good on them. As I looked at a relatively new infographic about their survey data, I was again intrigued by some of their findings. In a good way.

The infographic, found in Linkedin’s Talent Blog, 4 Recruiting Trends to Watch in 2016, boils the report down to 4 key points – and they are good ones:

  • Quality of Hire is the magic metric
  • Employers are finding quality hires faster through professional networks
  • Employer branding is bouncing back as a top priority
  • Employee retention is growing as a top employer priority

The big question raised in my mind by this infographic is: how should we define quality of hire. Linkedin helps us understand that perhaps we should be talking about this a little more than we are.

Linkedin 2016 Quality of Hire

Linkedin’s data show that around the world, the KPIs that define quality of hire shift between three primary metrics:

  1. New Hire Performance Evaluation
  2. Turnover/Retention
  3. Hiring Manager Satisfaction

These are interesting and good metrics. But are they the correct metrics to use in judging wether a hire was a quality hire?

As more employers shun “labeling” performance and leave traditional performance management systems and their inherent biases in the dust, having fair, accurate and reliable performance evaluation metrics may be harder and harder to obtain – especially for employees new to their jobs.

Turnover and retention data are somewhat valuable in that they measure whether the new employee actually commits to their job and the organization and decide to stay. The challenge with this particular measure is that it is two-sided. Employees can quit their jobs if they don’t like their employee experience more easily than employers can fire new employees who don’t perform. It’s hard to make a case that turnover or retention are valid measures of quality of hire.

And hiring manager satisfaction, while maybe the most influential measure, is the least scientifically valid assessment of the three: every manager has their own performance benchmarks that are shaped by their experience, education and time in the job. Certainly a new employee’s ability to create a positive relationship with their boss is significantly influential in creating a positive impression from a performance evaluation perspective. And that makes it only vaguely valid.

It’s interesting that employers in different parts of the world have developed different steps to develop Linkedin’s “magic metric.” That there is not the emergence of a common standard (SHRM or CIPD anyone? Bueller?) creates opportunities for stakeholders to get confused about what is trying to be accomplished. And that just makes it harder to make a business case for a critical aspect of talent management.

I think Linkedin has pointed out an opportunity for significant value in the talent management game:  unless and until we can develop a relatively standard, valid set of KPIs for Quality of Hire, we can’t really make sense of whether or not we’re hiring the great talent we all need. And since having the right talent available to us when and where we need it will make the difference in whether our businesses survive or not, getting a handle on the magic metric just might be helpful.

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Filed under Analytics, Annual Performance Reviews, Big Data and HR, China Gorman, Data Point Tuesday, Global HR, HR Analytics, Linkedin, Performance Management, Quality of Hire

What Do You Know About the Hourly Workforce?

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Here’s an eye opener:

“As of 2014, hourly workers make up 56.7 percent of the United States workforce. Think about that for a moment. More than half of all people working the U.S. make an hourly wage. That’s 77.2 million workers aged 16 and up. Yet there is little data to be found about the hourly worker. The U.S. Census publishes a total number of hourly workers and breaks that number down by very broad age characteristics, full-time vs. private sector and race. But that’s all. The segment is so ignored that even the monthly unemployment report doesn’t categorize the workforce by salary vs hourly. The U.S. Department of Labor recognizes them only in an annual report on minimum wage workers. To understand the majority of laborers in the United States, we are left to guess.”

redeapp is changing this through the commission of a series surveys and reports from Edison Research. The first, Profile of The Hourly Worker: Demographics, Devices and Disconnection, crossed my desk right before the end of 2015. And it’s pretty interesting.

Redeapp provides private and secure communications platforms that connect companies with their hourly, front-line employees and those without company email access. So they have a vested interest in having a deep understanding of this segment of the workforce. What they’ve found, in some cases, seems counter-intuitive. Like this, for example:

Profile of Hourly Worker 1.png

If the data are to be believed, more than 30% of the U.S.’s hourly workforce has 1-3 years of college or more – with fully 24% having some graduate credits or an advanced degree! I would not have expected that 49% of our hourly worker population would have a 4-year college degree – or a high school degree and some college credits.

Another surprise: email is used by this segment of the workforce multiple times each day in their general work responsibilities. But here’s the rub: only 50% of this segment have an email address provided by their employer. And 42% report that they use their personal email account for work communication either sometimes or often. How many liabilities and risks can we count here?

Given that scenario, this chart becomes very interesting:

Profile of Hourly Worker 2

The risk and control issues that exist in an un-secured corporate communication environment are quite large. Clearly, understanding hourly workers and how to communicate with them is a priority for organizations that employ this segment of the workforce. And perhaps, this segment of the workforce isn’t quite what you pictured.

Take a look at this survey report. It’ll make you think about your communication strategies. In a good way.

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Filed under Big Data and HR, Bureau of Labor Statistics, China Gorman, Corporate Risk Management, Data Point Tuesday, Employee Demographics, Employee Loyalty, Hourly Workers, HR Analytics, HR Data, redeapp, Strategic Workforce Planning, Uncategorized, Workforce Management

Watson Says Multiple Channels of Recognition Mean Higher Engagement

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Here’s what I like about whitepapers from IBM’s Smarter Workforce Institute: they are short in length and long on data and context. I appreciate that they share the underlying scientific concepts within their analyses of the data from their massive WorkTrends™ survey. 19,000 workers in 26 countries, a cross-section of industries, all major job families, and thousands of organizations responded to the 2013/2014 survey. Watson is all about slicing and dicing data and he came up with some interesting, although not surprising, conclusions about the impact of multiple channels of employee recognition.

In a point in time when organizations are grasping at any reasonable straw to increase engagement, decrease turnover and compete more successfully in the talent market, new approaches to employee recognition appear to be providing significant outcomes and ROI. Legacy recognition programs that attempt to reward employees for sticking around for 3, 5, 10 and 15 years have long ceased to motivate engagement or even longevity. Who would stick around one day longer to ensure they got an ebony clock on their 5 year service anniversary?

This survey analysis, How do I recognize thee, let me count the ways, explains concepts like Reinforcement Theory, ERG Theory and Social Exchange Theory to provide context for these findings that link recognition to engagement:

  • Employees who receive recognition are more likely to be engaged at work. The engagement level of employs who receive recognition is almost three times higher than the engagement level of those who do not.

  • Workers who receive recognition are less likely to quit. Without recognition, about half (51%) of surveyed employees say they intend to leave, with recognition just one quarter (25%) say they intend to leave their organizations.

  • Employees whose organizations use multiple communication channels for recognition are more likely to feel appreciated and show a higher level of employee engagement. The more channels used for recognition, the higher the employee engagement level.

  • The findings imply that technologies such as social and mobile could be strong candidates for the effective delivery of recognition as they offer interactive, frequent and immediate communication via multiple channels.

When voluntary quits in the U.S. are at their highest levels since early 2008, and the number of open jobs are at their highest level since 2000, it’s no wonder that employers are increasingly turning their attention to strategies that encourage employees to engage more and leave less. And because employers spend around 1% of their total payroll on reward/recognition programs, many are beginning to look at the ROI of that spend – and are frankly willing to spend more to increase their ROI. A negative ROI on 1% of payroll isn’t a good investment. But a positive ROI on 2% of payroll? That requires a new context and solid evidence that the investment will pay off. Data analysis like that found in this report, helps organizations create the appropriate business case for moving reward/recognition programs into the 21st century.

The important takeaway from this analysis is that one channel of recognition communication doesn’t cut it anymore (if it ever did). The data clearly suggest that multiple technology-enabled channels including social and mobile increase the financial and engagement ROI of recognition programs. Years of service awards don’t move the needle any more.

IBM SWI Recognition whitepaperAt a point in time where nearly half of employers are considering implementing new or additional recognition programs in the next 12 months, adopting approaches that use multiple technology-enabled channels appear to be the smart way to go. At least that’s what Watson thinks.

 

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Filed under Big Data and HR, China Gorman, Data Point Tuesday, Employee Engagement, Employee Recognition, IBM Smarter Workforce, Watson

Working in the “Gig Economy”

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Last week I introduced you to Mary Meeker’s Internet Trends 2015 report which I suggested should be required reading for HR. This report, which really should have been titled, The Internet in 2015 Is All About HR, shared important data points and analysis relating to basic HR functions and the impact the internet is having on basic organization functions.

This week, I’d like to point out the McKinsey Global Institute’s new report, A Labor Market That Works: Connecting Talent With Opportunity in the Digital Age. Even if you only the read the Executive Summary, this is worth your time. It’s full of employment-related data from the major global economies as it links those statistics to the growing impact of online talent platforms – and their potential, in the gig economy, to transform both the employer/employee relationship and how workers find work and build economic opportunity. It’s important information and their analysis of (mostly) Linkedin data are arresting.

The report is organized into three broad topics: Better, fast matching; Economic impact; and Talent management for companies. All three topics could sustain a full report on their own, but I’ll focus on the second: Economic impact. The gig economy powered by online talent platforms, by their analysis, will be contributing $2.7 trillion to global GDP by 2025. They do the math by analyzing three channels of impact:

  • Increasing labor force participation and hours worked among part-time employees
  • Reducing unemployment
  • Raising labor productivity

McKinsey Exhibit 13 June 9 2015

This adds 72 million workers to the global workforce and adds a full 2% to the projected world GDP for 2025. The largest impact, $1.3 trillion, come from great labor participation and more hours worked. Shortening job searches and creating matches that would not have been otherwise will lower unemployment rates, creating the second biggest impact at $805 billion. The third biggest impact is the increase of productivity through higher quality job matches and a shift to formal employment from informal grows global GDP by $625 billion.

But their analysis also shows that the positive impact of the gig economy is greater than dollars as 540 million people (nearly 70% more than the current population of the United States) will benefit from these new ways of connecting workers to work. That’s big, right? And that’s only 10 years from now.

McKinsey Exhibit 14 June 9 2015

As an HR leader, are you concerned about the talent pipeline? Having trouble filling your current open positions? Wondering if the use of internet based solutions will produce better results? The real question may be, “how fast can I start implementing online talent platform solutions in order to connect workers to the work we have available?”

The report continues to make the economic case for the positive impact of internet enabled platforms by predicting their use could reduce public spending on labor market programs, allocating as much as $89 billion/year from unemployment benefits savings to education and vocational training programs to ensure a skilled talent pipeline. McKinsey also predicts that online talent platforms may increase innovation, strengthen productivity and generally “improve the development of human capital across economies.”

This is Big Data at its best: boiled down to useful constructs. The full report is 100 pages. I recommend that you download it and take it section by section. I think you’ll be glad you did.

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Filed under Analytics, Big Data and HR, China Gorman, Data Point Tuesday, Gig Economy, HR Analytics, HR Data, McKinsey, Online Talent Platforms

HR Priorities and Business Value

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SHL has published their fascinating yearly report on global assessment trends.  SHL, acquired last year by CEB, is an assessment company, so reporting SHL Logoon assessment trends is right up their alley.  The survey data are interesting and the conclusions are worth noting by anyone in HR.

But their questions and conclusions go way beyond the use of assessment instruments for employment selection and employee development strategies and practices.

SHL Top 5 HR Priorities 2013The first part of the report reviews  organizations’ talent management focus and landscape.  For example , the authors compare the top five HR priorities in emerging economies to the top 5 HR priorities in established economies.

The lists are similar but not identical. You can see that four of the priorities show up on both lists, although prioritized differently; and that succession planning shows up in the top 5 on the established economies list, while training shows up in the top 5 on the emerging economies list.

Other findings relate to HR’s use of big data and the perception that there is room for improvement around the world in using objective data to make workforce decisions. In fact, less than 25% of the survey respondents reported that their organizations have a clear understanding of workforce potential.

The second part of the report focuses on the assessment of talent – both for hiring and for employee development.  Interesting findings in this section include the desire by nearly 75% of respondents to improve talent measurement and the practice of linking pre-hire and post-hire testing to specific business outcomes.

The third part of the report focuses on technology in testing, with a specific focus on mobile devices and social media. The key findings here include data showing that emerging economies want to use mobile technology assess candidates – both recruiters and candidates want this capability; and social media data is becoming less critical to hiring decisions. (See last week’s Data Point Tuesday.)

The report concludes with four recommendations for HR in 2013:

  • Big data presents HR with a unique opportunity to demonstrate business value

  • Only the right data will lead to the success of talent initiatives

  • HR should embrace innovation that improves how talent is recruited, but with caution

  • Mobile technology should be considered for competitive advantage, not to follow the crowd

The data in this report are presented in a way that is easily understood and useful.  At 30 pages, it’s worth the 45 minutes it will take to scan it and then hone in on the impactful sections.  I especially appreciated the selected references at the end, as well as the key findings lists from the same survey reports for 2009 – 2011. I’m always interested in the evolution of these kinds of lists.

But the bottom line for me is that here’s yet another source of global HR data shining a light on HR’s need to figure out how to demonstrate its business value. Whether you prefer this report to others you’ve discovered here at Data Point Tuesday — or you prefer other sources of HR data and analysis, the drum beat is the same:  aligning HR strategy (and tactics) to business outcomes is the only way to demonstrate business value. And the only way for HR professionals to be seen as business leaders.

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Filed under Big Data and HR, China Gorman, Data Point Tuesday, Employment Screening, HR Data, SHL, Talent Assessment, Talent Management