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.
Soccer Practice. Photo courtesy of woodleywonderworks.
Does life get in the way of your workplace productivity? Typically, it’s the opposite. Your personal life determines how you show up. When colleagues talk about life, and make their work meaningful to their lives, that’s when they become a team.
This is a great story from a colleague of mine from graduate school. Alyssa Burkus describes the time she was working on a project for an organization (Actionable.co), and started seriously to consider an offer to work for them full-time. During a team check-in about people’s weekend she announced to team members that she had achieved a milestone anniversary in surviving cancer. There was an outpouring of sympathy and support. She felt it. She had found her tribe.
If you listen closely in your own workplace, you might hear other moments like these. Some moments are better than others. When people “have a specialist appointment” how much time do we give them? When people have a death in the family, do they tell us, and do we have their back? When two people talk about their kids having learning disabilities, how long are they allowed to talk? At my current employer I had to delay my start date because there was a minor complication with a scheduled surgery.
The reason these scenarios are powerful is that many personal topics are simply more important than work. As an employer you don’t so much own people, you just borrow some of their time. When employees develop a sense of self-respect and a pride in their contributions, they willingly rise above what is expected from them in the job description. I love going above and beyond for people whom I respect, and who have respect for me. This feeling is stronger when employees forget about their salary, which is the dream of every well-informed compensation team.
The ability to have these conversations is part of a healthy workplace culture. It turns up in employee surveys as a determinant of workplace engagement. It drives turnover statistics and the amount of steam people put into discretionary effort. Missteps in these areas are often at the root of conflict, harassment, and grievances. When an employee expresses physical or emotional discomfort, the degree to which others care and take action is a major factor in accident claims, absenteeism, and long-term disability costs. With equity and inclusion the emerging practice is to bypass categories and go deeper into individual perspectives. With employee communications, people mostly read the personal stories. And the best source of information for leadership development inthe eyes of the employees who are following your lead.
I do a lot of math about workforce analytics and I can confirm for you that according to my calculator, emotions are the boss.
I think the reason vulnerability and compassion are so powerful is that it’s really hard to fake it. You can tell when people mean it, and you can tell when people don’t. As Alyssa puts it, “…this isn’t a call-to-action to start creating ‘meaningful moments’ initiatives, where the word from the top is leaders need to be more personal, or where HR tracks ‘connection point KPIs.’” It’s about authenticity. Perhaps we need to develop metrics to guage that.
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?
Stuart Murray Speaks 