According to Ben Waber, the founder and CEO of Humanyze, this is all just the tip of the iceberg. I first met Ben in Madrid, where we were both
speaking at a conference. Ben got his PhD at Massachusetts Institute of Technology in the Human Dynamics group and has studied behavioral analytics for many years. His company creates badges (just like employee
ID badges) that employees wear at work. Except these badges are different.
They are equipped with a variety of sensors, such as radio‐frequency ID that allow the badges to act like true ID badges, Bluetooth that measures someone's location in an office, infrared that can tell who you are facing, and a microphone that measures not what you say, but how you say it and how much time you spend speaking. These are things that actually measure human behavior, which according to Ben is something that most
organizations don't measure.
This type of data can be used to help organizations understand things such as whether marketing is talking to engineering, whether the manager of a team actually spends time with his or her people, what the top‐performing employees in certain roles do differently, and how the most successful salespeople speak with their customers. Although organizations oftentimes do A/B testing for customer‐facing initiatives, this type of approach is rarely done inside of organizations simply because the behavioral data doesn't exist, but eventually it will. This will allow organizations to optimize and improve everything from how teams are structured to how compensation packages are created. Imagine being able to A/B test how work gets done regularly. Ben acknowledges that survey data is still useful and important to have, but it paints only a part of the picture. In the next decade or so, only a handful of companies will get to this level of behavior analytics.
In most organizations I have observed, the people analytics function sits in HR or sometimes it's a separate function. This makes perfect sense because HR typically deals with people. The challenge today is that many HR teams don't have this capability because it's a new skill set. HR has primarily always been about dealing with people and their interactions, legal, hiring, and firing versus actually analyzing people from a data science perspective.
However, as this area becomes more advanced, it is quite possible that it will grow into its own department that reports directly to the CEO.
There is, of course, a dark side to people analytics because data can be used to make decisions that either positively affect people or negatively affect people. For example, people analytics can be used for calculating mass layoffs or for determining ways to manipulate people. This is a delicate balance that organizations have to be careful of, not to mention the potential creepy factor of employees having data collected about their every move
and action! Not only that but people analytics models are designed by people which means they will be inherently flawed. In her wonderful book, Weapons of Math Destruction, Cathy O'Neil tells the story of a middle‐
school teacher named Sarah Wysocki who was let go from a job with the Washington DC school district because an algorithm decided that she was doing a poor job. The school district was determined to improve
underperforming schools by eliminating bad teachers. Although she got rave reviews from the principal and from parents, somehow she was
classified as being in the bottom 2% of teachers. It turns out the elementary school where Sarah's students came from was one of several schools under investigation for a high likelihood of cheating on standardized tests by teachers who were erasing the wrong answers and filling in the correct ones. They did this to help preserve their own jobs. This meant that when Sarah's students took standardized tests where no cheating was involved, their scores dropped considerably, thus making it look like they weren't getting the education they should have been. Naturally the teacher was to blame. In this situation the algorithm would have no way of picking this up and as a result Sarah and over 200 other teachers were fired. This story illustrates just how important it is for us to not place all of our decision‐
making eggs in the people analytics basket.
Today we are still at the very early stages of what's possible. Perhaps the biggest challenge for companies today is organizing, cleaning, aggregating, and standardizing data, a project that can easily take years, depending on the size of the organization.
With technology advances and the integration of AI, you will one day be able to use voice commands to ask a smart assistant (think Siri, Cortana, Watson, Viv, or Echo) things like:
“What's the employee turnover?”
“Who are the top three employees on my team at risk for leaving the organization?”
“How many contingent workers are we using, and how much are we paying them each year?”
“What are the top skills and weaknesses on my team?”
“Which teams are the highest performing inside of our organization?”
“I need to build a new marketing team in California composed of five individuals; which employees should I consider?”
People analytics is absolutely growing into a core business capability that every organization must invest in heavily and it's the foundation of
employee experience