Category Archives: Analytics

August 1, 2017 · 4:00 am

Are HR And Finance Finally Going To Be Friends?

How on earth can it be August 1st? For that matter, how on earth can it be 2017? After taking off three weeks for vacation and business travel commitments, it’s time to be back considering new research and data of interest to the HR community. And here’s an interesting report from Oracle and MIT Technology Review.

My friends at Oracle sent it to me, and I’m glad they did. The report might signal the start of a new era of respect, cooperation, and, dare we say, organizational friendship between HR and Finance. Finance and HR: The Cloud’s New Power Partnership is a recent publication providing interesting data, analysis, and commentary about the Cloud’s opportunity to drive greater partnership, cost savings and actionable data through a strong HR and Finance relationship. It’s a quick read at 14 pages, and it’s based on survey data collected from 700 respondents that included senior managers and their mid-level management counterparts from Finance, HR, and IT, as well as more holistically inclined C-level executives. Organizations participating were from the Americas, Europe, the Middle East and Africa, and Asia. About 75% of the participating companies generate annual revenue between $250 million and $1 billion from a range or industries. So a global sample of large employers. Just the segment that would be wrestling with the Cloud opportunities. And just the segment that would see value in a closer relationship between HR and Finance.

Among businesses that participated in the survey, 35% plan to create a shared finance and HR function within a year… 42% of respondents say they are motivated by improvements in productivity and performance. Respondents view closer finance and HR collaboration in the cloud as a strategic necessity, promoting operation excellence and accelerating innovation.

Wait a minute. I think I felt the ground move! Shared Finance and HR function? Within a year? That’s an eye popper right there. Not because it doesn’t make sense:  it really does. HR is becoming more data driven every day. And Finance has been data driven for years. Getting them together to analyze people data’s impact on the business and its growth plans is critical. And getting HR and Finance together through a technology bridge makes sense. Both HR and Finance need more and more data to manage the business. More and more analytics capabilities. More and more ability to predict the future based on today’s data. It’s fascinating to me that technology may be the puzzle piece that finally brings HR and Finance together. And the results – so far – look substantial:

These outcomes, though pretty generic, show an enormous upside opportunity for HR and Finance to migrate jointly to the Cloud. When do we ever get these kinds of outcome ratings on large-scale organization change initiatives? Or just on HR projects? Or just on Finance projects? It seems as if organizations are succeeding in generating real benefits from moving to the Cloud by creating teams from natural adversaries. And how interesting it is that the IT team is the attractor beam bringing HR and Finance together.

The report shares a few short case studies – from the education, energy, and financial services sectors – that underscore the benefits of integrated ERP-HCM Cloud systems deployments. (Note: this is a white paper. Underwritten by Oracle.) There are a number of interesting graphs in the report all underscoring these benefits. And they’re interesting to think about.

This is a quick read and it could help inform your thoughts about moving more HR functions to the Cloud. And to get you thinking about the inevitability of working more closely with your colleagues over in Finance. Better to have some informed opinions before your CEO, CHRO, CFO, or CTO starts asking questions…

 

Advertisement

1 Comment

Filed under Analytics, Big Data and HR, China Gorman, Cloud Migration, Data Point Tuesday, HR Data, HR Technology, MIT Technology Review, Oracle, Technology Implementation, Workforce Management

Tagged as , , , , , , , , , ,

June 6, 2017 · 10:56 am

Internet Trends and the USA’s Income Statement

Every year, I look forward to two reports:  The Deloitte Human Capital Trends report and the Kleiner Perkins Internet Trends report. I’ve written about the Deloitte report here and here; and the Internet Trends report here and here. The 2017 Internet Trends report was published last week and it’s a doozy!

Ever year Mary Meeker, from Kleiner Perkins, takes a vast look at what the Internet is doing to the world. How it is changing everything. While it’s never about HR, per se, HR is in most nooks and crannies of the data that are shared. And it really should be required reading for all business leaders. Including HR. This year, at 355 pages, the report is a blockbuster. And worthy of more than one Data Point Tuesday post.

This year’s report looks at these areas:

  • Global internet trends
  • Online advertising (and commerce)
  • Interactive games
  • Media
  • The cloud
  • China Internet
  • India Internet
  • Healthcare
  • Global public/private Internet companies
  • Some macro thoughts
  • Closing thoughts

To be honest, I always start with the macro thoughts section. This is where she drops the aha! moments. Well, at least for me. And this year was no different. Part of the macro thoughts are about USA, Inc. and she leads off with the USA’s income statement comparing F1986 through F2016. If you think of the USA as a business, this is eye popping.

What does this have to do with HR? Well, nothing and everything. Understanding income statements is a requirement for any business leader today. Including those in HR. Understanding how our businesses spend and make money is key to being able to successfully lead and invest in people.

Understanding our nation’s income statement should be a requirement for every citizen. Knowing how the taxes we pay are used, where our money is being invested, and the state of our debt are all important things for us to know. Be honest:  have you ever really thought about our nation’s finances from an income statement perspective? Even if you have, this ought to be under a magnet on your refrigerator door!

Knowing the state of our nation’s finances will put into perspective the state of your employer’s finances – and the choices that are being made nationally as well as at work. This is called perspective and it’s a valuable thing to have.

Next week, we’ll take a look at another aspect of the Internet Trends Report for 2017 that may have a more direct connection to the world of HR. In the meantime, you might take a look at it here. (And the 355 pages are PowerPoint pages, so they’re pretty easy to move through.)

3 Comments

Filed under Analytics, Big Data and HR, China Gorman, Data Point Tuesday, Deloitte, Demographics, HR Trends, Internet Trends, KPCB, Mary Meeker

Tagged as , , , , , , , , ,

January 10, 2017 · 4:00 am

Is “HR Analytics” an Oxymoron?

data-point-tuesday_500

As we head full-on into 2017, a friend sent me the link to a survey analysis that is extremely eye-opening. Still Under Construction:  The State of HR Analytics 2016, conducted and published by the New Talent Management Network, has some compelling data about how HR functions are deploying higher level analytics and how successful they are in deriving actionable insights. I was somewhat surprised at the findings. Maybe you will be, too.

According to the report, the perceived revolution in human resources inspired by the promise of relevance through data-driven insights is largely unrealized. No surprise, right? It’s a quick read, with reader-friendly graphs and charts that should make you feel more normal if your HR analytics investment and programs aren’t moving ahead as quickly as you’d like – or as quickly as you perceive your competitors’ are!

There are three primary findings:

  1. Big Promise; Small Reality

Essentially, there’s lots of talk and very little action. “…most organizations are using the same tools that existed years ago to produce the same analyses companies have always produced.”

  1. Backstabbing Data

“It turns out that the data is dirty – inconsistent, scattered, unreliable and sometimes just plain inaccurate.”

  1. Lean, Green and Unloved

Surprisingly, the HR data analytics push doesn’t seem to be helping: “more companies said their people analytics team hinders their analytics work than helps it.”

All three points above are discussed with interesting data points to support the conclusions. Point three is explained with the following:

still-under-construction-1

The takeaway here is that when there are dedicated people analytics teams in place, they are relatively small, relatively inexperienced, and not being very successful in changing the people practices of the organization. And therefore, confidence in these teams’ output remains low.

Perhaps experience and longevity will help. Or perhaps we’ve gotten the cart before the horse. This paragraph from the report really struck home for me:

“Only basic people analytics are being performed by most organizations, undercutting the popular narrative that companies are rapidly advancing in this space. The only rapid advancement seems to be in adding an HR analytics function, not getting deeper, more meaningful insights from it.”

This report generates real food for thought. I think you’ll find it interesting.

 

3 Comments

Filed under Analytics, Big Data and HR, China Gorman, Data Point Tuesday, HR Analytics, New Talent Management Network, Talent Analytics

Tagged as , , , , , ,

September 6, 2016 · 4:00 am

Davos and HR Data

data point tuesday_500

You’ve heard of “Davos,” the annual meeting of the global movers and shakers of business, held in Davos, Switzerland. But you might not be aware that the convener of that event, The World Economic Forum, is committed to “improving the state of the world and is the International Organization for Public-Private Cooperation.” “Davos” gets lots of press, but the ongoing work of the organization provides a trove of data, analysis and information for any leader, in any organization, anywhere in the world.

I recently downloaded a January, 2016 report, The Future of Jobs:  Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution, and had a great time wandering through the massive (167 pages) report. Don’t let the length deter you from downloading and skimming the content. There’s something there for everyone who is thinking about and strategizing the future of their workforce.

The analysis in the report is from a survey of CHROs, other CXOs as well as functional HR leaders representing 13 million employees in 15 developed and emerging economies. A total of 371 companies from 9 broad industry groupings are represented in the data.

The report is organized into two parts:

Part One:  Preparing for the Workforce of the Fourth Industrial Revolution

  • The Future of Jobs and Skills
    • Drivers of change
    • Employment trends
    • Skills stability
    • Future workforce strategy
  • The Industry Gender Gap
    • The business case for change
    • Gaps in the female talent pipeline
    • Barriers to change
    • Women and work in the fourth industrial revolution
    • Approaches to leveraging female talent

Part Two:  Industry, Regional and Gender Gap Profiles

  • Industry profiles
  • Country and regional profiles
  • Industry gender gap profiles

The Drivers of Change section is a primer on what employers are facing from a demographic and socio-economic perspective, as well as from a technological perspective. I talk to HR leaders all the time who have a hard time balancing strategic responses to these two drivers of change. This chart shows the global top drivers in each of these two buckets and how they rank with the survey respondents.

WEF Fig 2

This is just one of a number of useful analyses in the the report.

And an analysis such as this wouldn’t be complete without recommendations for action. The short term focus areas for action are not surprising:

  • Reinvent the HR function
  • Make use of data analytics
  • Talent diversity – no more excuses
  • Leverage flexible working arrangements and online talent platforms

Everyone performing research and analysis, as well as writing about macro trends in the talent space agrees with these four areas of immediate focus.

The longer term recommended actions are not quite as well socialized, and in many ways, are the most critical strategies we can and should begin to deploy NOW:

  • Rethink education systems
  • Incent lifelong learning
  • Accelerate cross-industry and public-private collaboration

This report came to me via Facebook, of all places. WEF posts a continual stream of global reports, videos and links to data and analysis of value to HR and leaders in all functions. Check them out.

1 Comment

Filed under Analytics, Big Data and HR, China Gorman, Data Point Tuesday, Davos, Global HR, Human Capital, World Economic Forum

Tagged as , , , , , , ,

August 30, 2016 · 4:00 am

Quality of Hire and Data

data point tuesday_500

“Quality of Hire” is one of those terms – like “engagement” – that we all use and all mean different things when we use it. And there is no standard definition. Directionally, we’re probably all in the same ballpark. But there is no precise, function-wide, commonly agreed-upon, global definition.

That’s why I read with interest Joe Murphy’s Quality of Hire:  Data Makes the Difference. It was published by Wiley in the Summer 2016 issue of Employment Relations Today.

Joe believes that Quality of Hire is not an abstraction or a myth. He believes that “It is a practical measure, comprising core talent acquisition processes and hiring outcome variables. Its factors can be identified, tracked, and reported in both qualitative and quantitative terms.” And then he shows how.

There’s a wealth of critical information in this article if you are not really comfortable with analytics – including predictive analytics. It breaks it down simply. I like the Talent Analytics Maturity Model and the way it is introduced:

Shaker 1

There are 4 phases in the model that progressively advance in terms of the analytics

Primitive

“Primitive analytics is the use of simple methods to organize random, text-based data.” Like that from a resume.

Evaluative

“Evaluative analytics is the mathematical analysis of relevant data.” Assigning numerical values to experience, or skills, or employers and adding them up.

Speculative

“Speculative analytics involves the complex analysis of largely random data and some element of relevant work-related data.” Like that from analyzing “verbal responses, converting spoken words to text to explore patterns and relationships.”

Predictive

“This method is characterized by experiment design and the conducting of correlational analysis with two or more sets of highly structured, job-relevant data.” These can be collected through work product samples and surveys about experience and work style.

The bottom line is this:

The growing use of data and analytics in all stages of the hiring process helps companies make more educated decisions about the people they hire and lessen the randomness of personal judgement in making these hiring decisions.

Moving beyond trying to make sense of random data (like resumes, LinkedIn profiles and notes from an interview) to using relevant data and advanced analytics really will make a difference in hiring outcomes and improve the quality of your hiring. Take a look at this article. Joe does a great job of making the case for the use of analytics to improve quality of hire – and to do it consciously and continuously.

Leave a comment

Filed under Analytics, Big Data and HR, China Gorman, Data Point Tuesday, Hiring, HR Analytics, HR Data, HR Trends, Joe Murphy, Quality of Hire, Recruiting, Shaker

Tagged as , , , , , , , , , , ,

August 9, 2016 · 4:00 am

Gender Equity And The Great Manager Divide

data point tuesday_500

Gender equality in the workplace is a topic much discussed today:  politically, socially, economically and demographically. Women everywhere wonder “what’s it going to take?” to be paid on par with men for doing the same work. Visier’s new Viser Insights™ Report:  Gender Equity gives some new insight into the demographic and economic side of this situation. It’s great data and will give you some new avenues to pursue as you lead your organization to more equitable compensation outcomes.

The analysis started with a subset of Visier’s database of anonymized, stardardized workforce data, representing over a million active employees. The subset included:

  • 165,000 U.S.-based employees
  • 31 Blue Chip companies
  • 11 of which are Fortune 1000

The organizations included are from a range of industries, such as Energy, Financial Services/Insurance, Healthcare, Manufacturing, and Technology with employees ranging from less than 999 to 50,000 employees.

The key findings broaden the context from a purely social context and include the following:

  • There is an increase in voluntary turnover and a pronounced dip in the percentage of women in the workforce between the ages of 25 and 40 (from 43% to 39%), the same age range in which women commonly have childre

  • The gender wage gap widens at age 32, starting with women earning 90% of the wages of men, and decreasing to women earning 82% of the wages of men by age 40

  • Women are underrepresented in manager positions from age 32 onwards – the same age at which the wage gap between men and women broadens

  • Manager wages are, on average, 2 times that of non-manager wages

  • Having the same representation of women in manager positions as men would reduce the gender wage gap to 10% across all age groups – an improvement most notable for the age 32 and older population

The graphs lay out this argument beautifully and are easily understood. For example,

Visier 1

What the analysis shows is that the gap in promotions/hiring to manager-level positions starts to widen at around age 32 between women and men. And this is exactly when women start leaving the workforce to focus on family and children. Makes total sense. This is what Visier has dubbed the Manager Divide. And, according to Viser’s data, the Manager Divide is a primary driver of wage inequality.

Visier 2This is a pretty clear picture of the divide. Conclusions include:

  • Removing the Manager Divide would reduce the gender wage gap by just over one third for workers over age 32

  • Removing both the Manager Divide and removing gender pay disparity in manager positions would cut the gender wage gap by one half for employees over age 32

The report continues by discussing the reality that even if organizations paid men and women equally for like positions, but had a lack of gender equity in filling manager positions, gender pay equity would not be reached in the aggregate. What follows is a convincing discussion about the childcare years that starts with this data point:

“Between the ages of 25 and 40 there is a notable and steady decline in the percent of women in the workforce. At the same time, the percent of women (out of the total workforce) in manager positions declines steeply.”

I encourage you download this report and get a broader understanding of the key factors impacting the wage gap. I think Visier is on to something important through the analysis of the data. The Manager Divide is real. It’s not just about women leaving the workforce to care for children. It’s most certainly also about gender equity in managerial promotion opportunities.

 

 

3 Comments

Filed under Analytics, China Gorman, Data Point Tuesday, Gender Equity, Pay Equity, Visier

Tagged as , , , , ,

June 7, 2016 · 4:30 am

Your People and Global Internet Trends

data point tuesday_500

Data Point Tuesday’s mission is to find reports and impactful data sources that most HR professionals would never find and serve up some of their more interesting data points for consideration. Usually the reports come out of the Human Capital Management arena:  academic papers, vendor survey analyses, white papers, etc. There’s a ton of data flowing in our space that the average HR person would never have the time to find. It’s what I do here. But sometimes the best data and analytics sources don’t come out of the HCM arena. And the annual Internet Trends reports is one of those sources.

I have been waiting with bated breath for Mary Meeker’s Internet Trends 2016 report – and it’s here! Last year, I suggested that the report really should have been titled The Internet in 2015 Is All About HR. I wrote about it here. This year, I think the report should be titled How the Internet is Just Beginning to Change Everything at Work. Again, it should be required reading for HR professionals everywhere.

The annual Internet Trends report that Meeker publishes is certainly not an HR report. But it contains critical information and data that HR people need to know. It’s all big picture stuff that relates to the Internet, but it also all has impact on people – and most of it has impact on people at work. In the U.S., in Asia, in Europe – all over the world. I encourage you to flip through the report – it’s in PowerPoint – even though it’s really long. This is the outline – and I defy you to not find the majority of it interesting and relevant to your HR work, your workforce planning and your role in setting business strategy.

Here are the topics covered in this year’s report:

  1. Global Internet Trends
  2. Global Macro Trends
  3. Advertising/Commerce + Brand Trends
  4. Re-Imagining Communication – Video/Image/Messaging
  5. Re-Imagining Human-Computer Interfaces – Voice/Transportation
  6. China = Internet Leader on Many Metrics
  7. Public/Private Company Data
  8. Data as a Platform/Data Privacy

Every single one of these topics has an impact on how you interact with your people, your people strategy or your people policies. Seriously.

For example, as you think through your internal communication strategy, this graph might be helpful:

Internet Trends 2016 1

Think it’s useful to know that 64% of Baby Boomers cite the telephone as their most preferred contact channel vs. 12% of Millennials? (It won’t be shocking, I hope, to note that Millennials prefer – by 48% — social media and internet/web chat channels.) While you might instinctively know this, seeing the hard data puts the need to rethink employee communication into a different perspective, doesn’t it.?

The advent of using microphones instead of keyboards to interface with computing is in very early days, according to Meeker. However, in 2013 35% of smartphone owners used voice assistants (think Siri) and 65% used the voice interface in 2015. Adoption is rising fast among smartphone owners of all ages. Even if the majority of voice commands are about calling and navigating home, the use is skyrocketing. And as the Boomers age, think of the impact – at home and at work – of not needing to use a keyboard to utilize technology. Is your organization prepared for this radical shift?

In the US, the reasons for using voice interface and the locations we are using it are not so focused on the job. But the trends are pretty clear. What can you do to anticipate and leverage this and enhance productivity, knowledge transfer and the employee experience?

Internet Trends 2016 2

So if calling mom and dad, and navigating (literally) home are the current most often uses of using voice for computer activation, then the charts above make an inordinate amount of sense. But if you keep the oldest demographic of the workforce in mind when reading these charts, you can see that a sea change could be on the very near horizon. What if the oldest demographic of the workforce isn’t going away in the next 10 years? Even more, what if enabling/convincing the oldest demographic of the workforce to stay in the workforce was the key to your workforce plans over the next 10 years? And what if the newest/youngest demographic of the workforce was already using voice for computer interaction nearly 100% of the time as they enter the economy?

Interesting data. Interesting questions. See what I mean about non-HR sources of data?

And just to leave you wishing for the good old days, there’s this graph comparing the attributes of technology use among the emerging Gen Z cohort to the Millennials:

Internet Trends 2016 3

As my dad used to say, “If that doesn’t make your hair curl, I don’t know what will!”

The workplace and workforce planning implications of this report put the future in new light. A good light, I think. A challenging, but good light. And a light you need to focus. What do you think?

3 Comments

Filed under Analytics, Big Data and HR, China Gorman, Data Point Tuesday, Employee Demographics, GenX, GenY, GenZ, Internet Trends, KPCB, Mary Meeker, Millennials

Tagged as , , , , , , , , , , ,

January 12, 2016 · 4:30 am

Quality of Hire: A Vaguely Valid Metric?

data point tuesday_500

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.

5 Comments

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

Tagged as , , , , , , , , ,

November 17, 2015 · 4:30 am

Should You Care About Worker Happiness?

data point tuesday_500

Universum has just published another fascinating survey analysis that should be required reading for any leader wondering about the engagement of their employees, humanity in the workplace, or whether or not their workforce is happy. The summary is available here and it introduces the Universum Global Workforce Happiness Index™.

The survey covered 250,000+ professionals in 55 markets in order to set country- and industry-level benchmarks. The Universum Global Workforce Happiness Index is calculated based on:

  1. Employee satisfaction in their current job,

  2. Likelihood of recommending their current employer, and

  3. Their stated sense of job loyalty.

Starting off with a simple four-box model of work happiness, the four quadrants are simple to understand because of their common sense approach:

Universum Happiness 1STRANDED employees feel dissatisfied in their current jobs, but are unmotivated or unwilling to make a change. SEEKERS are dissatisfied at work and looking for a change. RESTLESS employees require immediate attention because even though they are satisfied and likely to recommend their employee, they are open to changing jobs. FULFILLED employees are satisfied, feel positive about their employer as a place to work and aren’t interested in changing jobs. This construct is simple and makes it easy to relate to these four types of workers.

If you are leading a global business, then the Global Workforce Happiness Index By Country chart will give you some interesting data to chew on:

Universum Happiness 2If you have global expansion plans should you prioritize those countries whose workers are Restless? Or countries whose workers are Seekers? Or do you go right for the Fulfilled worker countries? Maybe it isn’t enough to be looking at skills availability – maybe the availability of hearts and minds should also be a factor.

This report summary packs a great deal of insight into just 17 pages and I’ve just skimmed the surface for you. In the final section, every employer would do well to follow this recommendation: separate “attraction drivers” from “retention drivers.” Do the characteristics that attract high quality candidates to your organization retain them for the medium- or long-term? For organizations battling it out in the talent wars around the globe, this is the next tough question to answer.

The implications of workforce happiness around the world – especially with GenY and GenZ becoming the dominant generations at work – are beginning to change how every organization relates to its people. We’re re-thinking lots of fundamental people processes, policies and behaviors. Factoring the happiness of our people is just one of the ways things are changing.

This is a super report. It gives just enough analysis to be useful, while creating the case to get the full report. I liked it a lot.

2 Comments

Filed under Analytics, China Gorman, Data Point Tuesday, Employee Engagement, Global Workforce Happiness Index, Happiness at Work, HR Data, Universum

Tagged as , , , , , , ,

June 9, 2015 · 4:00 am

Working in the “Gig Economy”

data point tuesday_500

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.

1 Comment

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

Tagged as , , , , , , , ,