Elephants Before Unicorns: Emotionally Intelligent HR Strategies To Save Your Company

Remember, what got you here won’t get you there. If you pride yourself on decisiveness and intuition, you must still recognize how those instincts could be crippling your hunt for true unicorns. Be aware of your own unhelpful tendencies and get second opinions. Putting the brakes on and reexamining the way in which you hire could be the most helpful action you take toward attracting and retaining unicorns.

If your unicorn isn’t performing the way you’d hoped, or you sense he is dissatisfied or unhappy, he may be in the wrong role.

You may balk at this. “How could he be in the wrong role? It’s the job we hired him for!” Or, “I know he has the skills and can deliver on what we need. He’s perfectly able to do the job. So how could it be the wrong role?”

This is where we come back to what we talked about in Chapter 8: You must know your direct reports. It’s up to managers to have frequent conversations with their direct reports—both professional and personal in content—in which they learn the details of their lives: preferences, concerns, significant life events that may be on the horizon, etc. As a people leader, these conversations are never just idle chitchat—they are part of your job. You cannot hope to manage effectively and lead your team to new levels if you don’t know who they are as individuals.

You may have noticed that your unicorns can be divided into two different camps. One group is hungry for every opportunity you can throw at them, asking about advancement from day one. I call these people “astronauts.” Light the fuse, and they’ll overshoot the moon.

The second group is content to stay in their roles. They value detail and mastery, becoming absolute experts in their area and setting the standard for everyone else. You can’t run your organization without them; they build the foundation. I call this group “architects.”

Architects are absolutely solid in their role; they have little or no interest in expansion and advancement. This could be because they have a lot going on at home, so they don’t want to add more to their plates at work. Or maybe they have outside interests that are important to them—training for a triathlon, for example. For whatever reason (or no reason at all), architects are dedicated to their current role.

By contrast, astronauts are on a steep growth trajectory. They want every opportunity you can throw at them. If they don’t advance quickly after coming aboard, they’re likely to grow restless and begin looking for another position. They are “all in” from the beginning; it’s up to their manager to give them appropriate opportunities to stretch their wings and take off.

CASE STUDY
Finding the Perfect Mix of Unicorns

Jeffrey, the principal data science manager at a major tech corporation, was in charge of assembling a team to develop and implement a mixed-reality application to measure how people use the company’s tools in the workplace. He knew he wanted data scientists on the project from its inception, but that posed a problem: How do you bring data scientists onboard when there isn’t yet data?

Jeffrey knew he would need team members with a diverse set of skills for the project to succeed. He needed five core people for his team as well as a project manager. He could have taken the usual route, which would be to choose four team members deeply versed in the subject material—e.g., four machine learning engineers or four experts in data visualization. But Jeffrey wanted the team to be able to solve the puzzle posed by the project. He wanted people who could imagine what would be useful to learn from the data and then figure out how to measure it. The project would most benefit from having the team members viewing that puzzle through four separate (yet compatible) lenses, so Jeffrey was after people with diverse backgrounds and expertise.

So he set about assembling his team. One team member came from an operations research background and had strong statistics and coding skills. Another was very new to data science but had a deep mathematics background and ten years of experience as an educator. A third was a designer specializing in visualization who also held advanced degrees in computational biology. Two data engineers rounded out the crew—one with experience “productionizing” data and the other with a background in “fuzzy data mining.”

The most important skill Jeffrey was looking for in his team members was the ability to tell a story. To create a compelling product, he knew he needed people with strong communication skills. The interview he created contained questions to evaluate how well the candidates could communicate. Jeffrey asked them to explain algorithms to him “as if I were in fifth grade.” Could they break down complex processes and operations so they were understandable to people with absolutely no experience in the subject? How did they deploy analogies? Was the picture the candidates painted vivid and enticing?

Even though Jeffrey’s job is all about data, he resists being labeled as “data-driven,” finding it devoid of humanity and lacking room for the ineffable qualities that exist behind the data. He says it’s easy to see what is happening—what’s difficult is understanding why. Jeffrey prefers “data-informed,” meaning that the team is reflecting on the significance of the data they’re gathering.

That’s why assembling a team of storytellers was so important to him. Jeffrey wanted emotionally intelligent team members who could deploy their empathy and understand the intent behind the questions. He wanted people who could get to the root of a problem and ask, “What is it you’re really curious about? Never mind about the data—we’ll figure out the quantitative part later.” Thus, his team would use their imaginations to design a product that would eventually yield them valuable information.

Jeffrey joined a leadership team that was fully formed with the exception of the data role he filled and was tasked with assembling his team in a mere eight weeks. In that time, he reviewed hundreds of resumes, which he eventually culled down to five. He had to find his team members and prepare them for the task under an incredibly tight deadline.

Fortunately, the rest of the leadership team was already sold on the value a data team would bring. It wasn’t a question of “Why do we need that?” but “When do they get here?” Thus, Jeffrey could focus solely on building his team for those eight weeks. The pressure was immense, but he was fully supported in his mission and able to attract the unicorns he needed.

So what can we learn from Jeffrey’s experience?

Jeffrey had a clear vision for what he wanted from his team: storytellers with diverse backgrounds and experiences who could imagine the role of data from the inception of a product. He knew exactly what he was looking for, so he wasn’t dazzled by fancy degrees or job titles. Depth of experience and emotional intelligence were the most important factors. He was clear on his needs, and he communicated those with his team. He was also committed to their development—how everyone on the team might work together to increase one another’s skill sets. Jeffrey set expectations from the beginning so the data scientists wouldn’t grow frustrated with the early lack of data.

And what greased the wheels, so to speak, for the successful assembling of the team? The backing of leadership, who gave Jeffrey the directive to focus solely on finding the right people. With enough support, stellar teams can be created—even in a hurry.