Forget About Strategy. Reality is a Mosh Pit

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

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

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.

Data Will Drive Your Car. Oil, Not So Much

Oil Rig. By Soliven Melindo.
Oil Rig. Photo courtesy of Soliven Melindo.

Are cars no longer fueled by gasoline because they are now fueled by data?  Consider how driverless cars, electronic vehicles, and Uber are changing the outlook for the future.  And reflect on how the in-vehicle computer has increasingly changed you safety, your comfort, and your ability to manage the vehicle’s maintenance.  Gasoline is so last century; today it’s all about the data.

A Financial Review article from July 2017 by Mark Eggleton plays with the idea of data as the fuel of the future.  For a century oil ruled our world, influencing geopolitics, urban design, and decisions about where to work and travel.  Today, it is data that is significantly changing our world.  However, we cannot just obey data on blind faith.  We need to look up from the GPS, so to speak, and decide for ourselves if the data we are being fed is relevant and appropriate.

We need to consider data in the context of trust.  Take banks for an example.  Although banks could do lots of things with our personal financial information, they operate within the context of trust that has built over centuries.  Regardless of whether we trust their profit motives in society overall, we do indeed trust that the information they hold will be handled in a responsible and diligent manner.  Banking is deeply immersed in a human context, regardless of whether it always seems that way.

I personally think that in workforce analytics, there is a similar concern about trust.  We have at our fingertips sensitive information that could be used for good or evil.  So let’s ask, are human resources departments actually good? Perhaps we need some time establishing ourselves, to give a better sense that when we’re wrapped up in industrial conflict and individual terminations, that we’re sincerely doing what is expected of us.  If we collect accident statistics and attendance lists for mental health workshops, do employees bank on us only using this information to make people well?  Have we truly established that the employment equity data we collect will be used exclusively for its intended purpose?  When we survey employees on their engagement experience, is the information used to create a better workplace, or are there attempts to punish those who express low motivation?  While we closely guard peoples’ confidential pay data, do we have the correct attitude towards employees discussing their pay amongst themselves?

I think it’s high time we subordinate data to the human context.  After all, if big data peaks, we are probably into the human economy.   If data is going to change the world, we need to ensure it dovetails with our history, the geography, the people and their culture.  If we get this wrong, it will be a dystopian science fiction movie come true.  That’s kind of what happened with oil.  So let’s get it right this time.

(Hat tip to KMPG’s Hugo van Googstraten for sharing the original article with me)

Is Workplace Culture the Right Kind of Revolution?

The wall of plexiglass, by ebt47563 (=)
The wall of plexiglass.  Photo courtesy of ebt47563.

Exactly how do you change organizational culture?  This is a good HBR article from June 2017 about attempt to change corporate culture from the bottom-up.  It’s a story about Dr. Reddy’s, a global pharmaceutical company based in India and led by G.V. Prasad.  The authors are Bryan Walker from IDEO and Sarah A. Soule from Stanford Business School.

Dr. Reddy’s process of culture change began with significant ground research to find out what their staff, providers and investors needed when dealing with customers.  They brought their goals down to four simple words that brought it all together: good health can’t wait.  Instead of selling the slogan through posters and speeches they chose to demonstrate their purpose through actions.  The initiative named projects in packaging, sales, and internal data to advance the new vision.  There were some immediate impacts.  One scientist broke a number of company rules and produced a new product in 15 days, having prioritized new efforts to match the vision.

The comparison to social movements is important, because movements start with an emotion rather than a call to action.  Movements start small, “with a group of passionate enthusiasts who deliver modest wins.”  Momentum builds through networks, penetrating power structures and leadership.

There are also “safe havens,” places where activists can behave differently from the dominant culture and discuss their goals.  In innovative organizations, research labs are often built as separate mico-organizations that cultivate change as prep-work for the larger organization.  This story resonates with me, because disruptive workforce analytics will occasionally fall of deaf ears.  The analysis needs to be created in a manner which partially ignores pre-existing agendas or presumptions of how things would normally be done.  The decision of whether to apply new ideas might belong within a more formal process, but when experimenting with messy new ideas, to be sequestered is ideal.

Beyond the HBR article two additional models are appropriate to discuss the nature of change.

Innovating Technology and Trends Through Social Networks

The first model is the diffusion of innovations as described by Everett Rogers.  In this model, there is a small avant-garde of weirdos who just get into stuff that is new and interesting.  That crowd of innovators will not have the full opportunity to make money or make it big.  But their new findings diffuse through social networks, based on peoples’ network connections and their readiness to consider new ideas.

There are several hold-outs, such as the laggard crowd who resists change until it is impossible to do so.  The biggest difficulty is the early-stage challenge of “Crossing the Chasm” where the new idea has won-over a small crowd of early-adopters who are about 13.5% of the population.  The challenge is that sometimes there’s something about the new idea that doesn’t mesh with the next crowd, the early majority.  Some examples might be that the new technology has a difficult user interface, or the social trend is incompatible with the conventional lifestyle of those in the burbs.

In my opinion, the classic example of this challenge is the hands-free bluetooth headset that you see people wearing when they’re talking on the phone while walking down the street.  The technology has been in public for more than fifteen years and our first instinct is still that we want the caller to get professional help.  And that’s if you’re not also angry at them about a misunderstanding.

Using Social Disobedience Tools to Change Workplace Culture From Within

Another compelling cultural change model is the Spectrum of Allies model from George Lakey of Training for Change.  Lakey is highly experienced in training social justice activists in civil disobedience.  I attended a couple of workshops with Lakey when I was part of the labour movement, and his spectrum model is eye-opening.

The key diagram is a semi-circle, kind of like a half-order of a large pizza with six or eight slices.  The idea is that everyone can be categorized according to their level of enthusiasm for, or resistance to, an agenda or new idea.  Then you lay these wedges out in order, with the most supportive categories on the left and the most resistant on the right.  Your goal is to shift all of society one wedge to the left.  That is, the biggest hold-outs still get your attention, you’re just trying to convince them to become only moderately opposed.  Those in the middle, you can tip towards you slightly.  Those who are with you from the start, those can be your strongest advocates.

What really holds the model together is that you are shifting the entire social culture towards your way of thinking, resulting in culture change.  Everyone is a big deal, everyone receives the attention they deserve.  It’s very different from that us-against-them stuff that we’re accustomed to seeing during elections.  And it is very different from the notion that the main difference in the key players is their place on the org chart.

What this means for workforce analytics, is that you will require several different vehicles to bring meaningful information into human resource decision-making.  While there will be those who are hungry for the information, there will be others who need to simply be sold on the notion that it is not a threat.  While innovative findings might be compelling amongst an in-crowd, getting the information through cliques and interests will require bridging links and data translation.  You can build new ideas in self-imposed isolation, but at some point you step into public and advance it your ideas through the audience.

But before you step out, please put away your Bluetooth headset.

Data Democracy, For Better or Worse

Vote Here. By Andrew DuPont
Vote Here. Photo courtesy of Andrew DuPont

Do you wish that there was more equality in our access to information?  I do.  In the past (i.e. a few decades ago) it used to be far more common for information to be more tightly-held by those with power.  However, major employers are pushing data downward into the hands of more people within their organization.

Here is an interesting article about data democratization, a buzzword that warrants some clarity.  Author Bernard Marr, in his July 2017 article in Forbes, describes data democratization through general themes.  An organization’s internal data is no longer “owned” by the Information Technology department, rather the data is put into the hands of diverse users.  Everyone has access to the data and there are no gatekeepers creating an access bottleneck.  People from varied ranks and diverse professional backgrounds can use the data to advance their goals.  There are down-sides, including redundant efforts by distributed users, concerns about data security, the fact that some data still exists in silos, and misunderstandings by those who don’t deal with the data every day.

It’s important to take this phenomenon seriously as a trend that is building steam, and which is probably here to stay.

In my opinion, the word “democracy” is problematic.  For example only those with digital literacy who are inside the organization can take full advantage.  Those with more power can use the new information more significantly to their advantage.  There also tends to be a winner-takes-all outcome, where the person with the best information and the most sophisticated ability to use it tends to come out ahead.

While you might think that these phenomena imply data is undemocratic, guess again.  Electoral democracy, although pure in spirit, tends only to involve between one-half and three-quarters of voters who cast a ballot.  Those who are powerful (i.e. business owners and property owners) have a strange ability to get more out of elected governments than others.  And those who are the best at politics will tend to win all of the power, leaving others in the dust.  Much like parliamentary democracy, data democracy works best for those who have the upper hand.  In both cases, the system is a pseudo-democracy of established interests choosing amongst themselves who they will share power with.  I think that’s called aristocracy.