Photo by Scott R. Kline

Vivienne Ming is only 45, yet her resume already includes more positions than most people hold in a lifetime.

She earned a PhD in psychology and theoretical neuroscience from Carnegie Mellon University but her work spans several industries, including life sciences, financial services and tech.

She currently chairs two companies, one of which she co-founded with her wife, and advises five other companies. In the past, she has co-founded three more companies and held leadership roles at countless others, including one that she founded in 2008 and led until its acquisition in 2011.

If there’s anything that unites Vivienne’s many experiences, it’s a love of data and an even deeper love of human potential. Indeed, several of the companies that Vivienne advises are trying to harness the power of data and technology to match job seekers with the right opportunities.

“If an employee doesn’t have the Hollywood leading man look, they are often undervalued.”

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Her work with these companies led her to notice a disturbing trend. Qualified female and minority candidates were frequently denied jobs because of what she determined were managers’ and recruiters’ implicit biases. Even once hired, these workers often did not receive credit for the value they provided the organization.

Vivienne is now out to fix what she calls the “systematic undervaluation of workers.” She believes that the answer to this problem lies in computing people’s “lifetime employee value.”

Here, Vivienne discusses the concept of lifetime employee value and how it could change the future of business.

What is lifetime employee value?

Fundamentally, it is all of the subtle and pervasive impacts a person has on the company they work for.

Why do we need lifetime employee value?

If an employee doesn’t have the Hollywood leading man look, they are often undervalued. In particular, women in leadership or people who look different or have an accent are discounted.

“I can use data to measure employees’ value and convince business leaders to invest in them.”

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These employees are providing real, measurable value, but we don’t see that.

But if I can use data to measure employees’ value, I can convince business leaders to invest in them. I can show that the more a company invests in its workers, the more the workers put back into the company’s bottom line.

I’m happy to let the math do the work and prove that this is true, not only for highly skilled workers but also for temporary, low-margin workers.

How can lifetime employee value be measured?

Let’s say someone comes up with a great idea, and it trickles up through the company until no one is sure where it came from.

We can figure out the source with AI. AI can track social connections within a company by reading emails and messages sent through channels like Slack.

We can also track people’s location in real time via bluetooth on their phones and recorders that turn on every 10 minutes and record 30 seconds of audio.

This tells us who employees speak with, what they talk about and how they change their language when they’re around certain people. From there, we can build the “social map” of a company.

“I can track how one person indirectly influences hundreds people throughout the company.”

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With this map, we can determine an individual’s impact on their own team and on other teams. For example, we might be able to see that after two people chatted at the water cooler, one of their teams became much more productive. We can assign real credit back to actual people.

Are there privacy concerns with collecting all of this personal data?

The ethical concerns are genuine and real. Collecting this data can be very intimidating and intrusive.

On the other hand, managers are systematically undervaluing certain employees. That can lead to less productive teams and decreased company revenue.

I’m a big believer in collecting as much data as possible, but privacy and issues of respect certainly come into play. I definitely don’t think we should track everyone all the time in real time – that’s very scary. There needs to be a happy medium.

What does this happy medium look like?

I asked myself: What is the minimum amount of social data that we need to collect in order to make informed conclusions?

I determined that we can draw conclusions without measuring anything on the individual level. In fact, there’s no unbiased way for anyone to look at raw, individual data, and you should need a ruling from a judge in order to access it.

“Data, machine learning and AI can help us break through our biases about who is a high-value worker.”

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All of the key performance indicators that I look at are at the team level.

AI can analyze an individual’s data anonymously and spit out highly actionable statements and how companies can maximize revenue growth and productivity over time.

All of this data already exists somewhere on the company’s servers. Whether a company is small and only uses Gmail to communicate or large and uses a messaging service like Slack, those channels provide data that can show how ideas are created and shared.

For example, I’m beginning some projects with a number of organizations including Salesforce, a large company whose employees rely on Chatter, an internal chat product somewhat like Slack. Based on my previous research, I can track how information flows through their social network and how one person indirectly influences decisions of hundreds people throughout the company.

What do you say to people who still believe that this use of their data is an invasion of privacy?

I think of what I do as “theoretical HR.” I consider what would be possible in a magical world where anything was realizable.

Photo by Scott R. Kline

 

Revolutionary technology is always scary and clunky at first, and then we figure out how to turn it into something respectable.

Fifteen or 20 years ago, we were drilling holes in monkeys and even people to try to understand the brain. We paralyzed people – people who were very ill and were willing to be guinea pigs in the name of science.

The ultimate goal was never for people to live with a giant array of wires jammed in their heads, but experiments like this were necessary in order to develop the sophisticated robotic limbs that we have today.

“Revolutionary technology is always scary and clunky at first.”

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My research will follow the same path as all of the pioneering research before it. Eventually, we will find a way to accomplish our goals without inflicting collateral damage.

How can companies use lifetime employee value?

Measuring lifetime employee value definitely has a role in transforming the hiring process. Right now, we tend to think of people as static value propositions. They either do or don’t have the skills necessary for the job. For example, we ask, “Do you know how to program in Python?”

But with lifetime employee value, we can think about how people can contribute over the course of their employment with the company. How will they grow and how will their value change? How can we maximize it?

The goal is to devise a process that can be used to answer these questions about potential employees:

  • Where in the company will they be most successful?
  • What support will they need in order to be successful?

Once these questions are answered, recruiters can make an informed decision. They might decide that the company isn’t currently able to give someone the support they need, or they might decide to hire them

“People aren’t static value propositions.”

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How else are companies hurt when they fail to see an employee’s true value?

They miss out on opportunities to hire and retain the people who will benefit the company the most.

I had a labmate when I was an academic who was an amazing guy. He was very smart and earnest and knew everything about our field. He would ask the tough questions that made everyone on his team better at their jobs.

Even so, our company let him get away because his personal metrics didn’t look good. For example, he didn’t publish many papers and never managed to move into a tenured position. He also had a heavy accent.

For those reasons, the company didn’t accurately assess his value as a teammate.

Team value is a very common idea in sports. Coaches don’t care whether you score points or block shots. They care whether the team wins when you’re in the game. I want to bring this philosophy to business.

How can lifetime employee value and the technologies used to measure it transform society at large?

“Diversity delivers clear benefits, and we can prove it by measuring lifetime employee value.”

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Data, machine learning and AI can, in theory, become a part of our culture. They can help us break through our biases about who is a high-value worker. These biases are very real and very measurable.

For example, lifetime employee value can be used to justify the short-term costs that come with assembling a team that is racially, cognitively and experientially diverse. We’ve seen repeatedly that companies that displayed bias in hiring were more than twice as likely to be out of business five years later than those that did not. Diversity delivers clear benefits in the long term, and we can prove it by measuring lifetime employee value.

I took a bunch of existing research and created a system that could profile people’s biases. Unsurprisingly, people were commonly biased against women, who were perceived as less competent than men.

But people weren’t aware that they held this bias and would never have disadvantaged someone intentionally. Nevertheless, this bias is there, and it’s deep and it’s present almost as often in women as it is in men.

The result is that a woman works just as hard as a man, but he is given more credit, bigger bonuses and more promotions.

This is wasteful of people’s lives. It’s not morally excusable.

“We can all be better with just a little bit of computer help.”

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But if we are aware of this tendency, we can build a workplace culture in which we are hypervigilant about pinpointing where real value is created and how people are contributing.

We can all be better with just a little bit of computer help.

About Vivienne Ming

Dr. Vivienne Ming, named one of 10 Women to Watch in Tech in 2013 by Inc. Magazine, is a theoretical neuroscientist, technologist and entrepreneur. She co-founded Socos, a cutting-edge startup which applies cognitive modeling to create adaptive, personalized educational technology. She is also a visiting scholar at UC Berkeley’s Redwood Center for Theoretical Neuroscience pursuing her research in neuroprosthetics.

In her free time, Dr. Ming also explores augmented cognition using technology like Google Glass and has been developing a predictive model of diabetes to better manage blood glucose levels.

She sits on the board of Our Family Coalition supporting LBGT families and speaks on issues of LGBT inclusion and gender in technology.

Follow Vivienne Ming on Twitter @neuraltheory.

About the author

Aviva Schmitz

Aviva Schmitz

Aviva is a content marketing intern at UpCounsel and student at Tufts University in Medford, Massachusetts. She has served as an editor and contributing writer for publications such as The Culture Trip, the Tufts Daily, and satirical magazine The Zamboni.

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