Forget About Strategy. Reality is a Mosh Pit

Posted on by Stuart Murray
CROWD S U R F E R. By Keami Hepburn
CROWD S U R F E R. Photo courtesy of Keami Hepburn.

Strategy is not superior to tactics.  At best, strategy and tactics can be integrated as equals.  In this day and age it is looking increasingly unlikely that a senior leader will come up with one brilliant idea from the top of the organization and cascade it downward through the chain of command.  Rather, we live in a world where ground-level employees determine business success; information is diffused through friends and cube-mates; and the best ideas move diagonally through the organization’s subject-matter experts with minimal regard for the org chart.

A classic example of the disputed importance of strategy is the difference between Workforce Analytics and Strategic Workforce Planning.  I routinely use Workforce Analytics to help a variety of managers and professionals adapt to an unpredictable array of questions.  Workforce Analytics has a kind of “older sister” business practice called Strategic Workforce Planning which has been around for a little longer.  Strategic Workforce Planning is the practice of using analytics in the formal process or organizational re-design.  The re-design is intended to align human resources to internal and external context, a forecast about the future, and organizational strategy.  It makes perfect sense on paper.

In my opinion, there are three major frustrations with strategic alignment.  First, it makes a presumption that organizational strategy in your organization is in its prime.  If your org strategy is in its final approval stage or a complete re-write of that strategy is about to begin, then alignment to that strategy is a dubious effort.  Second, if any of the organization’s major leaders are in transition (both incoming and outgoing) their personal enthusiasm for the formal strategy could be in play.  To some extent, strategy is a debate amongst executives, and that debate can shift as the players are in flux.

Third, forecasting is a moving target.  In the middle of the Strategic Workforce Planning process there is an attempt to identify a future state and assess scenarios where a different staff composition would prepare the organization for that future.  However, society is changing so quickly and in so many ways that speculation about any likely future state has the shelf life of about a month.  Try writing down your predictions about the future on a piece of paper and then come back to it in 30 days.  With the passage of time you will either be humbled, or you will assert that it’s been doctored and you couldn’t have written something so clueless.  As such, alignment to strategy is brief, making the overall process less tangible and less relevant.

A good example of the struggles of strategic alignment is Uber.  Uber appears to have been built around a culture of rules-breaking on taxi licensing, grey-ethics exploitation of private information about a customer’s physical location, and a backroom culture of dot-com, locker-talk bravado.  With just a little bit of blowback from the public, Uber has been obliged to change senior leaders and reverse elements of the very organizational culture that made it great.  Good luck identifying what their sector will look like in two months, what this week’s executive team is going to do about it, and calibrating staff accordingly.  They might be fine in the near future, but we won’t really know until after the fact.

Consider by contrast an impactful tactical change which adapts to emerging evidence.  There is evidence that an equitable and inclusive work environment fosters better commitment and idea sharing.  There is evidence that workplace incivility has a dramatic impact on general productivity.  There is evidence that customer engagement is hyper-sensitive to employee engagement.  It is possible to develop a supposition that millennials are quitting at a higher rate, only to discover evidence that this is more nuanced and is really about career advancement at all ages.  These insights can have a dramatic impact on an organization’s opinion about what their core function should be, how managers should treat employees, and what kinds of employees and managers you should be hiring or promoting.

Then you would need to double-down and anticipate that even more disruptive evidence will continue to arrive at an even faster rate.  And if you did not adapt in this manner, you can bank on the fact that this adaptation is happening at rival organizations.  This brings us back to the possibility of even more leadership change and yet another re-vamp of organizational strategy.

If you are a manager, a human resource leader, or an analyst you might need to abandon all delusions that you can chart a clear path.  Rather, you are in the mosh pit of life, and your prime directive is to keep moving and not get hurt.  Keep your tempo, have fun, and follow the mood.  You cannot simply obey the directives of those with money or rank.  You must arrive at work fresh and rested, and play hard.  Every day.

How to Repurpose Leftover Turkey and Leftover Code

Posted on by Stuart Murray
Turkey
Turkey.  Photo courtesy of  Jeremy Keith.

Canadian Thanksgiving has come and gone, and several households are struggling with a conundrum.  What should you do with the leftover turkey?  There are downsides to having this carcass.  It hogs fridge space, you will be eating turkey for days, and some people just hate leftovers.  I know people who are tempted to throw the whole thing in the garbage.  But don’t. Leftover turkey is a great opportunity to whip up some butter turkey or turkey noodle casserole.

When there’s nothing left other than bones, it’s time to make turkey stock. Boiling down a turkey carcass into stock is one of the great wonders of household management.  While the stock simmers, filling your home with great smells, you can accomplish something else.

With workforce analytics this kind of thing happens all the time.  Once you get on top of a major headcount puzzle, you will have spreadsheets and a few pages of code that are available for more than one purpose.  Like turkey leftovers, be bold and repurpose them.

My favorite experience was when I built an entire hierarchy of jobs in order to identify when people had been promoted.  In large organizations it can be ambiguous which job movements are upward or downward.  Often, promotions are not categorized as promotions, especially if they change departments, leave and come back, or get a job temporarily prior to being made permanent.

To get past this obstacle we created a simple reference table that identified where someone was in a hierarchical career ladder, assigning a two-digit code to 1,200 job descriptions.  It was hard and tedious work that was entirely for the benefit of the back-engine of our promotions model.  But we eventually got the promotions model to work at a level of high accuracy, after which the client was able to use the information to influence high-level decisions.  That was the full turkey dinner.

Shortly after we finished this promotions model, we got new demands for work which took advantage of the back-engine.  Our happiest client was the one who just needed the list of rank indicators for the 1,200 job descriptions.  They needed to send emails to a small number of high-ranking people, and with our organizational complexity and some turnover at the top, it was hard to identify who was senior.  What they needed was a rules-based way of identifying who should get their emails.  Looking at our rank tables, they were able to choose seven rank categories and let the code do the work for them.  In the process they uncovered that one executive had been previously overlooked.  Now they were able to get the information out to the right people.

This client got the analytics equivalent of turkey soup.  They just needed the bones from inside — the promotions query — to be boiled down and combined with a few fresh ingredients to create a new, repurposed product that met their needs.

Do you have the opportunity to repurpose your own big wins?  That time you got on top of a major health concern, did you also develop healthy habits that improved other parts of your life?  If you overcame a difficult business relationship, did you also learn what your triggers are, and how to regulate them in future?  At the end of a big project, did you go for drinks afterward and end up with a few new friends?

Sometimes it seems like you’re just working hard to make other people happy.  But if you accomplished nothing in the last year except healthy habits, self-awareness, and more meaningful relationships, would you even recognize that this counts as success?

So put on your wool socks, turn the TV to your guilty pleasures, and curl up with that bowl of turkey soup.  It should feel good.  So take a deep breath and enjoy it.

Fold the Towels First

Posted on by Stuart Murray
Towels, by Michael Coghlan
Towels.  Photo courtesy of Michael Coghlan.

This is a quick productivity tip for anyone who feels overwhelmed by the over-abundance of information and obligations.  Fold the towels first.  I first developed this metaphor when I figured out how to “get around to” folding the laundry for my family of four.  There was a big intimidating pile of laundry that I didn’t want to start working on.  So, I just walked up to the pile and pulled out all of the towels, folded them all, and put them away in about five minutes.  I came back to the pile two hours later, and it was about half as big as the last time I looked at it.  There, not so intimidating. Let’s finish the rest of this work.

Similarly, I was able to apply this metaphor to large volumes of errors in spreadsheets full of workforce data.  You see, there is a high likelihood that if you look at all of the problems you need to solve, there is typically one big problem that can be solved really quickly. Think of this as a strike-attack against the intimidation factor.  Just wrap up one big problem then step away from your desk for an hour or for the day.  Come back to your list of woes, and the remaining work should seem far easier.  It works with laundry. It works with big data. And, it could work for you.

Unsubscribe to your biggest spam provider, request a deadline extension on your most unreasonable task, ask for help with that thing that is beyond your ability, or send a courtesy note to that one person you’re worried that you might have offended. It doesn’t always work out this way, but when it does work, it’s incredibly empowering.

Who Created Racist Robots? You Did!

Posted on by Stuart Murray

Reinventing Ourselves

If robots just did what we said, would they exhibit racist behavior?  Yes.  Yes they would.

This is an insightful article in the Guardian on the issue of artificial intelligence picking up and advancing society’s pre-existing racism.  It falls on the heels of a report that claimed that a risk-assessment computer program called Compas was biased against black prisoners.  Another crime-forecasting program called PredPol was revealed to have created a racist feedback loop.  Over-policing in black areas in Oakland generated statistics that over-predicted crime in black areas, recommending increased policing, and so on.

“’If you’re not careful, you risk automating the exact same biases these programs are supposed to eliminate,’ says Kristian Lum, the lead statistician at the San-Francisco-based, non-profit Human Rights Data Analysis Group (HRDAG).”

It’s not just the specialized forecasting software that is getting stung by this.  Google and LinkedIn have had problems with this kind of thing as well.  Microsoft had it the worst with a chatbot called Tay, who “learned” how to act like everyone else on twitter and turned into a neo-nazi in one day.  How efficient!

These things are happening so often they cannot be regarded as individual mistakes.  Instead, I think that racist robots must be categorized as a trend.

Workforce Analytics and Automated Racism or Anti-Racism

This racist robot trend affects workforce analytics because those attempting to predict behavior in the workplace will occasionally swap notes with analysts attempting to improve law enforcement.  As we begin to automate elements of employee recruitment, there is also the opportunity to use technology-based tools to reduce racism and sexism.  Now, we are stumbling upon the concern that artificial intelligence is at risk of picking up society’s pre-existing racism.

The issue is that forecasts are built around pre-existing data.  If there is a statistical trend in hiring or policing which is piggy-backing on some type of ground-level prejudice, the formulas inside the statistical model could simply pass-along that underlying sexism or racism.  It’s like children repeating-back what they hear from their parents; the robots are listening – watch your mouth!  Even amongst adults communicating word-of-mouth, our individual opinions are substantially a pass-through of what we picked up from the rest of society.  In this context, it seems naïve to expect robots to be better than us.

So, we must choose to use technology to reduce racism, or technology will embolden racism absent-mindedly.  Pick one.

A major complication in this controversy is that those who create forecast algorithms regard their software and their models as proprietary.  The owner of the Compas software, Northpointe, has refused to explain the inner-workings of the software that they own.  This confidentiality may make business sense and might be legally valid in terms of intellectual property rights.  However if their software is non-compliant on a human rights basis they might lose customers, lose a discrimination lawsuit, or even get legislated out of business.

We are in an era where many people presume that they should know what is really happening when controversial decisions are being made.  When it comes to race and policing, expectations of accountability and transparency can become politically compelling very quickly.  And the use of software to recruit or promote employees, particularly in the public sector, could fall under a similar level of scrutiny just as easily.

I hope that police, human resources professionals, and social justice activists take a greater interest in this topic.  But only if they can stay sufficiently compassionate and context-sensitive to keep ahead of artificial intelligence models of their own critiques.  I’m sure a great big battle of nazi vs. antifacist bots would make for great television.  But what we need now are lessons, insights, tools, and legislation.

Workplace Incivility Drags Workplaces Back to Stone Age

Posted on by Stuart Murray
neanderthal-museum-by-clemens-vasters.jpg
Neanderthal Museum. Photo courtesy of Clemens Vasters.

How important is good manners?  Really, really important.  And it extends much further than knowing what an oyster fork looks like.

Incivility weakens health in areas such as cardiovascular disease, cancer, diabetes, ulcers, and of course mental health.  For reasons of reducing health care claims alone, mistreatment of staff should be curtailed.  However, preventing workplace incivility is actually a bigger deal than originally thought.

In fact, there is significant research that shows being outright rude to colleagues is a major killer of workplace productivity.

In my jurisdiction, there was legislation brought in a few years ago that obliged employers to curtail bullying and harassment.  The legislation goes beyond the long-standing human rights legislation preventing harassment on prohibited grounds, such as sexism or racism.  The new rules say that if we are to compel others to action we must not be aggressive, humiliating, or intimidating.

Uncivil Workplace Culture Adversely Affects Productivity

According to her research, Christine Porath found that for those treated rudely by their colleagues:

  • 47% intentionally decrease the time spent at work
  • 38% deliberately decrease the quality of their work
  • 66% report that their performance declined
  • 78% said their commitment to the organization declined
  • 80% lost time worrying about the uncivil incident
  • 63% lost work time in their effort to avoid the offender

In addition to the reduced productivity of those who stick around, there is also the consideration of those who quit.  Twelve percent of those treated poorly leave the job because of the incident and, by contrast, those who are treated well by their manager are more likely to stick around.  What is interesting from an analytics perspective is that those treated poorly don’t tell their employers why, making it a blind spot in the data.  We know this from other sources; it’s always okay to say that you’re leaving for a better opportunity elsewhere.  But employees usually quit because of their manager and refuse to talk about it in exit interviews.

In addition to those directly treated in an uncivil manner, those who observe someone else being treated in such a manner are also affected.  “You may get pulled off track thinking about the incident, how you should respond, or whether you’re in the line of fire.”  Those who witness incivility see their performance halved and they “weren’t nearly as creative on brainstorming tasks.”  It makes sense that behavior is social and contagious, and that we feel for those around us.  That includes emotional pain.

The impact is not just contagious between employees, but it also spreads to customers.  In research conducted with two colleagues form the University of Southern California, Porath found that “…many customers are less likely to buy from a company they perceive is uncivil, whether the rudeness is directed at them or other employees.”  When customers witness an uncivil episode between employees, that customer makes generalizations about the company.  This has happened with Uber; customers who perceive a toxic environment have turned to competitors.

It’s more evidence of an emerging business model I refer to as double engagement.  That is, that it is engaged employees who attract and retain engaged customers, causing the revenue flow that marketing and finance want so desperately.  The days of investors and marketing teams driving a product or service into the hands of witless customers is long gone.  We live in a world where being human dictates business strength.

But before we put this all in the hands of the worker, we should note that the main source of an organization’s emotional tone comes from its leadership.  Simply put, when leaders treat their team fairly and well, they are more productive.  The team goes above and beyond.  They have more focus, better engagement, more health and well-being, more trust and safety, and greater job satisfaction.

For leaders, the new bottom line must also now include compassion, emotional sensitivity, and engagement.  You must step away from individual heroics and reverse your sense of who is important.  Why? Because way down at the bottom of the pecking order there may be someone who is not treated so well.  Whether you’re a caveman or a gentleman, if you are stronger and more powerful it is your job to carry them.

Where’s Waldo in the Job Applicant Pool?

Posted on by Stuart Murray
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 CIO.com, 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!