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Class Takeaways — People Analytics
Five lessons in five minutes: Professor Amir Goldberg shows how machine learning can help leaders make people-related decisions inside an organization.
| by Kelsey Doyle
The first step in using data is understanding what data analytics can and cannot do. AI systems are powerful but are best used for prediction models. Your role as a leader is to use those predictions to inform your decisions.
In his class People Analytics, associate professor of organizational behavior Amir Goldberg teaches how leaders can utilize data analytics to make better decisions within their organizations.
Hi, my name's Amir Goldberg. And I'm a professor of organizational behavior at the Stanford Graduate School of Business, where I teach a class titled People Analytics. People analytics is the application of data analytics methods, especially machine learning algorithms, for the purpose of informing people-related decisions inside organizations. Here are five takeaways from that class.
1. Be an Informed Consumer of Data Analytics
Data are revolutionizing how people are managing inside organizations. Using data analytics doesn't mean that you need to be a data analyst. What you need to be is someone who understands what data analytics can and cannot do. And most importantly, understand how to interpret data analytics in terms of what it means about what's happening inside your organization.
2. AI-Driven Doesn't Mean AI Drives
It's really important for you to understand what algorithms can or cannot do. And people call it artificial intelligence, but machines are not intelligent in the way that humans are. What are they good at? Prediction, by which I mean looking at historical data and then making predictions about the future. Your role is to use those predictions as a means to inform your decisions. It is you who makes the decisions. It's the machines who make the predictions.
3. Be Skeptical
There's a lot of snake oil out there. If it sounds fantastical, it's probably no good. It's very difficult to predict human behavior. The most important thing for you to ask is, is this prediction predicting an outcome that I actually care about? If the person who developed the algorithm can demonstrate to you that it moves the needle on outcomes that are important for you, then it's worth considering. But if they can't, you should probably take a pass.
4. Use Algorithms to De-Bias Your Decisions
When it comes to making decisions about other humans, all of us are biased. We pay attention to other people's race, to their gender, to their physical appearance, to their accent. And those are often entirely irrelevant to the decisions that we need to make. So use your algorithms in a way that would help you overcome those biases and make your decisions better and more ethical.
5. Be Ethical
When it comes to making decisions about other people, those can be immensely consequential. They can relate to their livelihood, to their sense of worth, to their psychological well being. We can't take those lightly. And it's really important that when you make these decisions, you don't hide behind machines, nor do you outsource the morality of the decision to a machine learning decision maker. Ultimately, it's your responsibility. Machines are neither moral nor immoral. They do what you tell them to. And it's important that you think about the moral implications of your decisions. The good news, ethical decisions are also better managerial decisions. Good luck.
My mom once sat in a class and at the end of the class, she said to me, "Amir, I have no idea what you said, but you're a genius." Whereas my dad said, "That was very interesting, but you didn't teach them anything, did you?" So that gives you a good kind of summary of how my parents took the class.
Organizational Behavior , Big Data , Career & Success , Management
https://stanford.io/3MiWBZj
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