Information is the New Sugar

pie. by chad glenn. (=)
pie. Photo courtesy of chad glenn.

On Pi Day, are you able to resist temptation?

The bright colours?

The sweet flavours?

Maybe once a year it’s good for you.  But what if you were force-fed sweets every day?  That’s what’s happening today with information.

In an article in Wired, author Zynep Tufekci makes a comparison to food when describing the addictive power of information.

“…within the next few years, the number of children struggling with obesity will surpass the number struggling with hunger. Why? When the human condition was marked by hunger and famine, it made perfect sense to crave condensed calories and salt. Now we live in a food glut environment, and we have few genetic, cultural, or psychological defenses against this novel threat to our health.”

The author compares our food behaviours to our current addictions to highly processed data:

“Humans are a social species, equipped with few defenses against the natural world beyond our ability to acquire knowledge and stay in groups that work together. We are particularly susceptible to glimmers of novelty, messages of affirmation and belonging, and messages of outrage toward perceived enemies. These kinds of messages are to human community what salt, sugar, and fat are to the human appetite.”

There was a time when humans desperately needed food and new information.  Once these needs are satisfied the ability of industry to exploit our lingering sense of need and push unhealthy variants and volumes became the next big threat.

With food, it is helpful to seek out existing traditions in which things have been figure out already.  Healthy people eat in a manner that resembles the cuisine of their grandparents, rejecting processed foods and fad diets alike.  To quote Michael Pollan, the food writer, “eat food, not to much, mostly plants.”  So, if we were to seek healthy and viable traditions in the free flow of information, where would we turn?

Pi Day is a great place to start.  In the late nineties, I stayed at the home of a family friend named Larry Shaw, a science educator at the San Francisco Exploratorium.  During this trip Larry handed me a slice of pie on March 14.  I didn’t figure out until years later that he was the creator of Pi Day.  Larry looked like a hippie, and he had a great sense of fun.  But he was closer-at-heart to a serious movement to empower people to disagree with those with power, and express disagreements through free speech.

We watched a brief documentary about the Freedom of the Speech Movement.  In 1964 a man named Jack Weinberg was arrested for distributing political materials on the Berkeley campus.  Students encircled the police car Weinberg was in.  There was a 32-hour stand-off during which activist Mario Savio gave a compelling speech, saying:

“There’s a time when the operation of the machine becomes so odious — makes you so sick at heart — that you can’t take part. …you’ve got to put your bodies upon the gears and upon the wheels, upon the levers, upon all the apparatus, and you’ve got to make it stop. And you’ve got to indicate to the people who run it, to the people who own it, that unless you’re free, the machine will be prevented from working at all.”

In the era of social media and big data we are experiencing this same problem, but in reverse.  In decades past, government and industry asserted legal power and made threats against the publication of some news.  Coercion-narrowed perspectives whipped the public mood into compliance.  When protests break out today, we know about it through social media in minutes, without the support of broadcast media.  This should be the golden era of free speech.  But it’s not.

Nowadays when you see news it is unclear if you are receiving something accurate.  And if you are the one posting the video Tufecki asks “…is anyone even watching it?  Or has it been lost in a sea of posts from hundreds of millions of content producers?”  It’s not the case that accurate news is reaching the broadest audience, and it’s not the case that you as a citizen can make your voice heard.

Social media offers a community experience that is equivalent to shopping for groceries at a convenience store.

Tufekci notes that the world’s attention is overwhelmingly funnelled through Facebook, Google, YouTube, and Twitter.  These entities

“…stand in for the public sphere itself. But at their core, their business is mundane: They’re ad brokers. …they sell the capacity to precisely target our eyeballs. They use massive surveillance of our behavior, online and off, to generate increasingly accurate, automated predictions of what advertisements we are most susceptible to…”

The author makes the case that freedom of speech is not an end in its own right.  Rather, it is a vehicle through which we achieve other social goals, such as public education, respectful debate, holding institutions accountable, and building healthy communities.  Consider Savio’s “bodies upon the gears” speech and you know he wasn’t in this so you could look at food porn or cat videos.

We shall seek the best possible recipe for our knowledge.  We need to read books, watch well-produced documentaries, and talk to trustworthy friends who are knowledgeable on the right topic.  We must be skeptical of those in power but even more skeptical about friends who coddle us with complacent views.  Seek information that is healthy and fulfilling, and guard it like a borrowed recipe from your grandmother’s box of index cards.

And yet, enjoy small amounts of rumor and gossip, like the indulgence in a favorite slice of pie.  You still get to have fun, once in a while.  You’re still human.

Not Too Shocking – Those High Numbers from AI Job Disruption

Shocked. By Mark Turnauckas.
Shocked. Photo courtesy of Mark Turnauckas.

Can you think of a time you took advantage of a new technology, and in the process got way more work done?  We’re all going to need more stories like this in order to stay ahead of the game.

I’ll never forget my first exposure to a pirated version of Microsoft Excel.  I was in graduate school in 1994 and a young woman in my class, Bev, handed me a stack of eight floppy disks held together with a blue elastic band.  She told me Excel was way better than what I was using.  Six months later I had finished an entire graduate thesis based on clever charts and tables I had created using new software.  Six months after that, I was at a firm in one of the towers in Toronto’s downtown core with experienced consultants lining up at my cubicle, waiting for some solid analysis.  My mind had co-evolved around the technology, and I was valued.

For many months I was the only analyst on a team that had four consultants.  When new technologies are brought in, sometimes one person can do the work of several peers.  And this appears to be a concern today with incoming technologies, such as artificial intelligence, internet of things, and analytics.

There has been some excitement lately about McKinsey’s report that 800 million jobs will be eliminated worldwide by technology.  Reading the content of the report – not just the media coverage – I can assure you that it’s far less dramatic.

First, the 800 million jobs was the upside of a forecasted range, and the authors recommend considering the mid-point of the range, which is 400 million jobs.  Those 400 million jobs are proportional to 15% of current work activities in the global labour market.  These job losses are not expected to be immediate, as this is a forecast into 2030 – twelve years from now.  This means the forecast is closer to 30-35 million jobs lost per year, which seems far more modest on a planet with 7.6 billion inhabitants.

But it gets better.  Of the 400 million jobs lost, only 75 million jobs will be eliminated altogether.  The remaining job losses will be in cases where parts of our jobs will be eliminated.  About 30% of “constituent” work will be automated for 60% of occupations.  That is, there will be bots taking care of the more mundane parts of our jobs.  It remains to be seen whether this shift will result in 30% less employment, or if our outputs will just be more efficient.  There may be a line-up at your own desk, with senior people increasingly reliant on your own unique, human-machine hybrid.

This technological revolution will have more dramatic impacts on industrialized economies such as Canada, the U.S. and Europe.  New technologies have a cost of implementation, and cost savings are needed to justify the investment.  A lot of cost savings can be found in eliminating expensive jobs.  But in the developing world, wages are lower and the gains of the new technology won’t always outweigh the cost.  The trade-offs between hiring people and bringing in new technology often tips towards employing people in those places where wages are low.  It’s in the industrialized world where we will see the most change.

In my opinion (not necessarily McKinsey’s), this will have an impact on political optics.  Jobs will appear to be eliminated in industrialized economies and then magically reappear in the developing world.  But the back-story is that technology allows work to be done with fewer employees and more machines in industrialized countries.  And those western workplaces will have competition from countries where it is not optimal to bring in new technologies.  The jobs created in developing countries will look like the same jobs that used to exist in the West.  But that’s not what’s going on.  Developing economies are just briefly immune to the more-expensive technology, for as long as those countries have low wages.

McKinsey also reviewed the history of technological change and found that there tends to be a net gain from new technologies.  The technology benefits someone — the buyer, investor, or some new profession or trade.  That someone spends money in a manner that creates different jobs, often by taking advantage of yetanother new technology.  Those 400 million lost jobs are likely to be the downside of a net-gain from technology.

This raises the difficult issue of things getting better on average.  As I described in an earlier post, if one million jobs are eliminated and a million-plus-one jobs are created, this is a net gain of one job.  In the minds of economists, this is considered progress.  However, looking at the blow-back from voters in industrialized countries, it appears that we must now pay very close attention to the millions who were on the downside of this net-gain.  And perhaps you know some of these people.

McKinsey was all over this issue:

“Midcareer job training will be essential, as will enhancing labour market dynamism and enabling worker redeployment.  These changes will challenge current educational and workforce training models…  Another priority is rethinking and strengthening transition and income support for workers caught in the cross-currents of automation.” (p. 8)

Within the human resources crowd, we are experienced at either enduring push-back from unions, or anticipating labour’s response with meaningful policies and initiatives.  But regardless of whether you are sympathetic to the underclass, or just trying to implement a new technology as quickly as possible, you can see that society’s success at adapting to this change will hinge on the personal experience of those who have lost.

Looking around us, it seems like we are all trying to get our footing, trying to figure out for that one special thing that sets ourselves apart.  You might not be told ahead of time what that thing should be.  In fact, you might need to figure it out entirely by yourself.  But those who are always working on their angle will have a better shot than those who are relying on prior wins.

Sure, there might be an employer who is loyal enough to set you up for success, or a program or union that will help with the job transition.  But as we take turns eliminating each other’s jobs, you might want to hold onto a dash of selfishness.  If you can bot-boss your way into a superior level of productivity, you might have a shot at being that one valued employee on the upside of a turbulent net-gain.

Either as a society, or as an individual, you need to write yourself into a story where you reached for the power cord and taught the corporate machine to work for you.

Cheap Labour May Soon Be a Thing of the Past

Migrant Worker Style. By Matt Ming
Migrant Worker Style. Photo courtesy of Matt Ming.  (In communist China, being a labourer is considered dignified, hence they often wear nice coats)

What would happen if the world ran out of cheap labour?  It’s a threat, or an opportunity, depending on your perspective.  But it could happen in our lifetime.  In an earlier post I described how unemployment was low but wages weren’t rising.  If job growth were to continue all around the world, we could soon be surprised that there are few people left on earth who will work for low wages.

In a January 2018 New York Times article from January 2018, the article points to a global economic up-swing.  The reason why the economy is improving is different in every country.  The global economy has been recovering for a decade, since the 2008 recession arising from the sub-prime mortgage fiasco in the U.S.  This time around, the thriving economy is different.  Economists note that because the growth is broad-based, there are fewer star performers.  If any one country slips into a recession, the rest of the global economy could keep things going strong.  The world economy is forecast to grow by 3.9 percent in 2018 and 2019.  This growth includes a lot of developing countries.

However, this may be a house of cards about to come crashing down once you factor in the “Lewis turning point.”  The Lewis turning point describes when a developing country grows enough and creates enough jobs that there is no more surplus labour.  That means that in order for businesses to grow they must offer higher wages than other employers to draw people away, such that economic growth causes wage growth.  Before the turning point, investors grow their businesses taking for granted an unlimited supply of cheap labour.  After this turning point, the country sees notable changes in their society.  People suddenly stop working in the very lowest-paid jobs.  Employers offering benefits and job-permanence develop an edge over the competition.  Workers get picky about where they want to work.

In this interesting article on a website called The Diplomat, researcher Dmitriy Plekhanov looks into the speculation that China’s era of cheap labour has come to an end.  The methods of measurement are complicated and confusing, but in brief:

“No matter which indicators are employed, they all point out that wages have more than doubled since the year 2009. Such a pace of growth obviously has serious implications for the Chinese labor market and its international competitiveness in terms of relative wages.  The pool of cheap labor has definitely dried up.”

These changes narrow the wage gap between Chinese labour and the rest of the world.

There has been an active debate for some time about whether China has reached, or is about to reach, the Lewis turning point.  One paper from 2011 asserted that it had already happened.  Over at the International Monetary Fund a paper in 2013 estimated that the turning point “will emerge between 2020 and 2025.”  The paper notes that demographics will be a major issue.  Due to the aging of the population and their drop in fertility a few decades ago, China’s labour market is now at its peak size and will start to shrink in the near future.

It’s important to consider China in the context of the global economy.  For some time, globalization has been perceived to be a phenomenon of manufacturing job disappearing in the industrialized world and then re-emerging in China.  Yes, there were other low-wage countries to relocate to, but China was the big kahuna.  If this low-wage option disappears for investors, they must suddenly look to other countries with fewer workers.  Switching countries for a second, an article from January 2017 notes that India needs to create 16 million jobs to reach the Lewis turning point.  The article interprets that this is a lot of jobs, but that’s almost nothing in the global scale.  We’re not very far away from both China and India running out of surplus labour.

This means that investors must go farther afield.  The Times article describes a major investment being made in Rwanda, which might have been a no-go zone in years gone by.  In those cases where investors stick with their domestic populations, they need to change their perspective and seriously consider hiring ex-convicts, people with limited education, people with disabilities, and those who have experienced prolonged bouts of unemployment.  Employers can find contractors in the gig economy, but those contractors can also become scarce given that gig workers are part of the labour market.

All around, it is employers themselves that must put on a good show at the selection interview.  So if you ever thought human resources was a support function, think again.  Your competitive pay position, the quality of the employment experience, and the effectiveness of your recruiting function might become critical to business success.  Oh, and by the way …don’t tell the unions.

HR Technology – Get Ready For the Big Shake!

Day 119 - Shake it all about. By JLK_254
Day 119 – Shake it all about. Photo courtesy of JLK_254.

Looking back, it feels like 2017 was a big crazy dog that we watched playing in the water.  That dog has now come out of the water, it’s coming right at you and… get ready for the shake.  There’s never a dull moment in the world of technological disruptions in human resources and workforce analytics.

It’s becoming clear that the disruptions of the near-future will rely increasingly on human resources departments.  Items such as workplace learning, change management, and leadership development are being increasingly flagged by leaders outside of HR as critical to success in their own fields.  Meanwhile, and the ground level looking upward, employees are getting blunt about their expectations for career growth, workforce diversity, and a sense of organizational purpose.  Organizations trying to get on top of these issues without saying “human resources” are running out of euphemisms.

With a new year ahead of us, Josh Bersin of Bersin by Deloitte has published his forecasts for 2018.  In this case Bersin’s forecast is a list of emerging trends in human resources technology, a narrower focus than in the past.  Nonetheless, as everyone grips for emerging technological disruption in a variety of fields, it makes sense for us to consider how technology will disrupt human resources itself.

In my two subsequent posts I will describe how these innovations imply a different workplace culture and  leadership style, and increase the importance of qualitative information and our interpretations of the employee context.  For now, just consider that all work can change, and the people helping workplaces adapt to change are also changing themselves.  HR is just getting a double dose.

At-a-glance, Bersin’s top ten trends are as follows:

  1. A Massive Shift from “Automation” to “Productivity”
  2. Acceleration of HRMS and HCM Cloud Solutions, But Not the Center of Everything
  3. Continuous Performance Management is Here: And You Should Get With It
  4. Feedback, Engagement, and Analytics Tools Reign
  5. Reinvention of Corporate Learning is Here
  6. The Recruiting Market is Thriving With Innovation
  7. The Wellbeing Market is Exploding
  8. People Analytics Matures and Grows
  9. Intelligent Self-Service Tools
  10. Innovation Within HR Itself

For the uninitiated, Human Capital Management (HCM) cloud solutions (#2) is the technology that delivers databases known as human resources management systems (HRMS) on a fee-for-service basis through off-site cloud-based servers.  It’s disruptive because previous systems involved the purchase of an application which was stored on in-house servers alongside the data itself, with everything being owned and modified by the buyer.  Switching to cloud solutions means that you must steward and cultivate data carefully to allow it to dovetail with the rented solution, like a millwright, but with data.  These solutinos allow employers to take full advantage of all configurations in the latest version of the software.  There is far more functionality.  But the increased functionality won’t work unless your data is good and you figure out how to use the new modules.  This change has large implications for human resources, information technology, and daily users of the database.

Prior to now, most People Analytics (#8) was a combination of advanced analytics interpreting data that comes off the core database, plus a bunch of emerging data coming out of engagement analytics (#4).  But now, those two items are just the major platforms.  There are systems that used to be fringe players in HR but are now increasingly critical… and they need their own enabling technology.  Some of the technology hinges on the HRMS, but some of it does not.  For example, workplace learning (#5) and wellbeing initiatives (#7) used to be something that you could operate off an Office suite using a research-based model that followed the best literature in pedagogy or public health.  The best content would be distributed face-to-face, with limited need for software to make the difference.  Now the technology can help out so much more, and tools are becoming available to empower the traditional delivery methods to be more effective, more targeted, and better connected to analytics.

To some extent, everything is being disrupted in a manner that obliges us to think less about the technology itself and more about general productivity (#1).  Those delivering generalist human resources services are also seeing innovations in their own area.  Recruiting (#6) and performance management (#3) are being improved by technology, and a variety of self-service tools (#9) are automating operational tasks such as case management, document management and employee communications.  First we must obsess about the technology to get it to work for us, then we can clear that obstacle and get into new challenges.  Breaking new ground every day will give people in HR a lot of mojo, but only if we keep moving forward.

Bersin brings it all together by noting that it’s not just the purchased solutions that are transforming human resources teams.  In-house HR departments are disrupting themselves (#10), regardless of help from vendors.  Then they ask for help and the vendors themselves are struggling to keep up with clients.  When dealing with complicated case-work and finicky databases, in-house staff sometimes have a home team advantage.

Digging the Gig – Are Temporary Workers Really Happy?

Skydiving, by Joshua M
Skydiving.  Photo courtesy of Joshua M.  This activity is only fun when voluntary.

Why don’t we all just quit our jobs and go freelance?  Good question.  There’s not a really good reason why we should not.  Gig work improves job satisfaction, opens up work opportunities that might have normally been unavailable, and appears to have few negative impacts.

There is an interesting report on the gig economy available online, entitled “Independent Work: Choice, Necessity, and the Gig Economy.”  It’s a big report, so I’ll summarize the key findings for you.

In this October 2016 report, McKinsey Global Institute finds that about 20 to 30% of the working-age population in Europe and the US engage in some form of independent work.  The report explores whether gig work is truly a voluntary arrangement, and whether the work is lucrative or satisfying.

What is the Gig Economy?

McKinsey defines independent workers as having a high degree of autonomy, payment by assignment (not hours), and a short-term relationship with their employer.  Independent work connects a large pool of workers with a large pool of customers, on a scale that can be global.   The workers and customers link up for efficient matches via the internet and cell phones.  Only 15% of independent workers are using online marketplaces, implying there is potential for significant growth.

In my opinion, if the arrangement is truly independent, gig workers are businesses and not employees. This is a complication because independent business operators tend to be dropped from formal labour market statistics.  This makes the gig economy bewildering to the human resources field.  Also, these businesses are often too small to be measured by those tracking major corporations, such as stock markets or auditing firms. That means that independent workers are also not fully understood by experts in finance and accounting.

All the cool stuff happens at the boundary between categories, and nowhere is this more true than in the gig economy.

Is Temporary Work Truly Voluntary?  Is it Satisfying Work?

In conversations about the gig economy, there is a recurring question: how is this work any different from the contingent workforce of under-paid service employees?  McKinsey overcomes this confusion by placing  independent workers into four segments:

  • Free Agents do independent work by choice and get most of their income from this work.
  • Casual Earners choose this life but their gigs are supplemental income.
  • Reluctants get their primary income from independent work but would prefer a permanent job.
  • The Financially Strapped get supplemental income from gigs and do so out of necessity.

The free agents in the top tier “report greater satisfaction with their work lives than those who do it out of necessity.”  The fact that they could choose independent work had a greater impact on job satisfaction than geography, age, income bracket, or education level.

The higher job satisfaction of free agents reflects several dimensions of their work lives including satisfaction with their choice of their type of work, creativity, opportunity, independence and empowerment, hours of work (amount and flexibility), and atmosphere.  Independent workers like their boss more, that is to say, yes they do like themselves.  Some satisfaction indicators are equal to regular employment, but there were no job dimensions where free agents were less satisfied.

Free agents perceive that they make about as much money as they would in a permanent job.

Amongst the Reluctants and Financially Strapped, temporary work does not drive low job satisfaction.  Those who do any work out of necessity report a similar level of job dissatisfaction, regardless of whether they are independent or have traditional jobs.  It’s an important distinction: people who are forced into temporary work are dissatisfied, but the main driver of dissatisfaction is the phrase “forced into,” not the word “temporary.”  It sounds about right to me, considering how strong the human spirit is in resisting coercion.  And some of the temporary-ness is circumstantial and not attributable to a specific negative entity.

While it is notable that some people are “stuck” in these precarious roles, I personally think it is open to debate whether workers would be better-off with the absence of such arrangements.  That is, the supplemental income might truly make a difference, with no adverse impact on job satisfaction.  And it is not entirely clear whether the gigs can be converted into permanent jobs.  There may be cases where the elimination of gigs would simply result in the elimination of an income stream.

Opportunities and Threats in the Gig Economy

Digital links between workers and customers can be global in reach, and since only 15% of gig workers are connected to a digital platform, things could open up and grow substantially.  For the economy on the whole McKinsey notes that a growing gig economy “…could have tangible economic benefits, such as raising labor-force participation, providing opportunities for the unemployed, or even boosting productivity.”  There is the additional advantage that some services could be provided in a more flexible manner, improving the buyer or consumer experience.

I think there is a trade-off for the common citizen, that sometimes a less secure employment situation can be mitigated by a more beneficial arrangement for that same person acting as a consumer.

McKinsey rightfully identifies that there are challenges posed by the gig economy, including needs for training, credentials, income security, and benefits.  That is, if we are shifting towards a touch-and-go economy it will be harder to ensure everyone can be a winner, or even be able to get by.  There’s an increased demand for social supports coming from all quarters, including consultants at McKinsey.

Where’s Waldo in the Job Applicant Pool?

Where's Waldo. By David Trawin
Where’s Waldo. Photo courtesy of David Trawin.

How do you find that one special thing in the middle of all this big data?  It depends on what you’re looking for.  Machines can help you find things, but first you have to teach the machine to understand what you want.  With recruiting data, a few simple formulas evolve into something far more complex.

This article from, by Sharon Florentine summarizes how Artificial Intelligence is revolutionizing recruiting and hiring.  Long story short, if you have really good data about who your high-performers are and what the process was like to recruit them, you can reverse-engineer the recruiting to predict which applicants will perform well after hire.

I hate to imply that it’s so-last-month, but the basic concept is straightforward.  Collect large amounts of data, fine-tune its quality, run a statistical analysis to determine causation, and make a forecast.  That’s what it looks like in a lab environment.  But the good stuff is in the war stories of how this kind of experimental analysis plays out.  The article names a few hot-points worthy of more discussion.

Where’s Waldo: Finding the Best-Fit Candidate in the Middle of Big Data

Citing Glen Cathey of Randstad, the new job search is similar to the “Where’s Waldo?” book series.  “…it’s not difficult to search anymore, what’s of greater importance now is a data problem.”  That is, you have good applicants, but you have to identify that one great fit.  Cathey describes three types of search that make this viable.

  • Semantic Search, which seeks to understand a searcher’s intent and the context in which the search is being made. (Remember, good fit is circumstantial and conceptual)
  • Conceptual Search, which creates a basic concept from just a few key words.
  • Implicit Search, which pushes information to you making assumptions about what you’re trying to accomplish “…much like how Google automatically pushes restaurant recommendations in your local area…” I have to admit, I’m always impressed when Google knows that I only want local

Dark Matter: The Missing Job Applicant You Don’t Know You’re Missing

In spite of his faults, former Defense Secretary Donald Rumsfeld did pioneer an important concept called “unknown unknowns.”  That is, there are unknowns that you are somewhat aware are a risk factor, but there are deeper unknowns where you just have no idea information was lacking in the first place.

As it relates to recruiting, Cathey notes that “…you’re excluding people with those [machine-driven] searches.  Doing it this way means you’re actually looking only for the best of the easiest candidates to find.”  So, they use Artificial Intelligence and machine learning to find overlooked candidates.  A strong candidate might have done a mediocre job customizing their resume to your posting, but still have exceptional virtue.  They might use the wrong key words.   They might have special skills that your organization needs but it’s not on the job posting.  And your recruiting expectations might be biased towards a certain type of white male, or white males generally.  The modified formulas can open-up the under-used areas of the candidate pool.

So, while it’s great if the machine gets you to a great candidate quickly, you can also get the machine to do the tedious exercise of finding the diamonds-in-the-rough.

While it’s true that some of this work can be done with basic statistical tools and a good data set, that’s actually an ambitious starting-point to get to in the first case.  The advanced class is that you must create new data from scratch, revise the model on an iterative basis, and eventually run the model off live data such that the predictions change as the ground underneath the data shifts.

But that’s only if your attempt to do this kind of thing matches the business context.  The big challenge is when the work is incompatible with organizational strategy, or the initiative needs a compelling business case to shift resources, or you need to win-over new people who are in the middle of a leadership change.  At that point you will get sucked back into the complex world of humanity and empathy.  So much for robots making our lives easier!