What’s with all this bold talk from millennials? Don’t they know to keep hush about their outlandish opinions? In a recent article from Lisa Earle McLeod the author submits an open letter (closer to a manifesto) that explains why millennials have the opinions they have.
She has two key points. First, employers are tolerating poor performers, and those poor performers drag everyone else down, including highly-motivated millennials. It’s not so much that millennials are unreasonably ambitious and over-eager, it is that their enthusiasm is the correct attitude and lower-functioning colleagues should not be setting the pace. Fair ball.
Secondly, we must give our work purpose. Organizations that have “a purpose bigger than money” have better business results. This purpose-driven organization is reminiscent of Simon Sinek’s Power of Why although McLeod’s critique is closer to a sense of Noble Purpose amongst the sales team, a major concern of hers.
This focus on enthusiastic front-line staff is consistent with other critiques. Josh Bersin notes that many organizations are flipping their hierarchy to place priority on engaged employees first, who then attract and retain customers who, in turn, keep the profits alive. If it works, go for it.
Can big data reduce crime? Yes it can. This is a great TED Talk by Anne Milgram about using analytics to improve the criminal justice system. The talk from October 2013 describes how Milgram successfully attempted to “moneyball” policing and the work of judges in her role as attorney general of New Jersey. Hers is a great story, and has many features in common with the Moneyball book and movie.
The speaker describes how she built a team, created raw data, analyzed it, and produced simple and meaningful tools. Her most impressive outcome is a risk assessment tool that helps judges identify the likelihood a defendant will re-offend, not show up in court, or commit a violent act. She and her team have successfully reduced crime.
Baseball players and police officers alike have a culture of bravado and confidence which may be critical when handling conflict, intimidation, and credibility. Yet what police officers and baseball players often need is a safe space to question their assumptions, assess whether they could do better, and decide that they will do better. These types of vulnerable moments don’t play out well when a player is at bat, or when an officer is handling complaints from the perpetrators.
In Milgram’s talk, where others see cool math tricks, I see a change in mindset and demeanor. The speaker expresses curiosity about the information, enthusiasm for unexpected findings, modesty about baseline effectiveness, a lack of blame, and a can-do attitude about trying to do more and do better.
It’s a great metaphor for business. In those workplaces where managers fiercely claw their way to the top, there may be a reduced willingness to talk about shortcomings in a manner that requires trust and collaboration. Yet making exceptional decisions require that leaders choose an entirely different mood and posture while they explore an uncharted area, allow information to out-rank instinct, and aspire to a more subtle kind of greatness. Put posture aside, and just do good work. The way things are changing, those are the only kinds of people who will stay on top.
Employers are becoming increasingly frustrated that they can’t find perfect job candidates. And they can’t get perfect information prior to decision-making. Yet there is an abundance of people and information. What’s up?
The Paradox of Choice is a book and a TED talk by Barry Schwartz that describes the downside of having too much choice. Researchers found that consumers presented with more choices in the purchase of jam reduced the likelihood they would buy any jam. The more mutual funds an employee could choose for their pension plan, the lower the rate of participation in the plan itself. In these abundant environments after we make a choice we end up less satisfied with our decisions. It’s too easy to imagine a world where we could have done better. It makes us miserable.
Schwartz recommends that we consider lowering our standards. The concept of “sufficing” is key; that we should make choices that are good enough to meet our needs. If you later discover you could have done better, don’t worry about it.
This attitude is critical to workforce analytics. Trying to get that one quick hit of novel information should be enough for now. Just keep the dream alive that you can make progress every day. Become a little smarter, make a slight improvement, do a fist-pump, and then move on. Lower your standards, cover more ground, and always move forward.
How much can we talk about people without talking about people data? Not very much, it appears. Those dealing with employees of all types must know more about their hearts and souls than ever before. And if you make one false move with a data point, your most brilliant philosophical insights can be taken sideways.
In December 2016, author Simon Sinek was interviewed on Inside Quest on the topic of Millennials. I am a big fan of Sinek, having changed my approach to work based on his influential TED talk on how to Start With Why. The Inside Quest interview (20 minutes long) is also great because it covers many key topics.
Sinek posted a follow-up video days later to clarify much of what he had to say. There was a dramatic change in body language. In the first video he seemed calm and knowledgeable. However, in the follow-up video (from what appears to be his dining-room) he is a little sheepish, making clarifications, imploring people to keep the conversation alive with constructive criticism. The first interview had gone a tad viral and he got a lot of feedback.
During the Inside Quest interview he made piercing social criticism and attributed a lot of what was happening in society to the experience and context of millennials. In what should be described as “a good problem to have,” he understated the importance of his critique. You see, the things he said were true for many of us regardless of generation.
His critique? We must learn to wait. We must put time and years into our greatest accomplishments. We are lonely because we are embarrassed to talk about our disappointments and frustrations. We need to talk through our difficulties. We must aspire to engage in sincere conversations. We must help others. Look up from your phone and be human.
In my opinion these are all massive issues for workplace culture. Managers are struggling to learn how to compel their staff to work hard without being coercive or demeaning. Everyone who takes benefits costs seriously is now hyper-sensitive to whether employees can talk openly about mental health and wellbeing. Executives worried about people quitting are stumbling onto growing evidence that people want to thrive and grow. And still, the dream persists that we can all succeed.
I think that these topics entered the mainstream concurrent with the rise of the millennial workforce, not necessarily because of them. The analytics that identify turnover trends happened largely because of emerging technology; the de-stigmatization of mental illness was popularized by baby-boomer medical professionals; smart phones have been improving for decades; and teachers have been pushing anti-bullying efforts for some time. These things came sharply into focus when millennials first started to speak their minds in the workplace.
Based on his dining-room talk, it appears that Sinek’s feedback came from many non-millennials who want in on the broader discussion. This is important from a social perspective. But the social perspective is the flip-side of a data issue. That is because he got tripped up by a data-labelling error. You see, he casually referred to millennials has having been born approximately 1984 and after. He didn’t specify a 20-year generational cohort. He left it open-ended, like there was an unlimited supply of this generation being born every day. This is problematic because we need good definitions to determine if there are clear differences between clear categories. If the definition is muddy, then the identification of differences will be muddy as well.
I have had the pleasure of working with clearly defined data where I described millennials as those born from 1976 to 1995. By getting specific about date of birth, you will find that each year you look at the data the findings can shift. Age and generation are not the same things, and if you look at the two separately you might find, for example, that millennials as a generation do not have different quit rates. Or you might find that concerns about career advancement are widespread (more on that in a future post).
For me this is an excellent example of how workplace analytics and workplace culture are never that far from one another. To love humans is to wish the very best for them and their data.
This is a great TED talk by Dan Bricklin, the inventor of the electronic spreadsheet. The application he created – VisiCalc – is the original precursor to Excel, one of the easiest to use number-crunching tools when doing workforce analysis and just about everything else. He tells a great story of the disruption that his new tool caused its first audience, the MBA class he attended at Harvard, and the first conference where he revealed it.
This is a story of invention, so watch for key words like error, daydream, magic, imagine, prototype, feedback, find solutions, new capabilities, and marketing. This is a story of iterative design, where they figured out what they were creating part-way through the exploration.
In this fascinating TED talk by Hannah Fry, the speaker describes three mathematical algorithms that explain love. One of her findings was that the number of romantic advances someone receives is improved if there are polarized views of whether they are attractive. That is, you will approach someone if you think that many people other than you will think they are unattractive. How would we apply this to human resources? I would point out that the Oakland Athletics baseball team was successful at snapping-up rookies that they knew were great and everyone else had passed over. Using better metrics, the team found high-performers who seemed wrong, but were factually very good. In many cases the Oakland A’s got first dibs on diamonds-in-the-rough. Can you apply this insight to your own workplace?