Born out of the MIT Media Lab, people analytics, using behavioral data to understand and manage organizations, has fundamentally changed how companies operate. This course will provide participants with a foundation in people analytics through discussion and hands-on exercises with real world data and tools. There are basic questions that have an impact on businesses that no one can answer. How much does the executive team communicate with engineering? Is a manager really spending time with their team? How often should a salesperson speak with a customer? The reason we can’t answer these questions is a lack of data. Surveys and consultants are useful, but their shortcomings are evident. They’re slow, subjective, and don’t actually measure what happens in the real world.
New data has changed this equation. We are constantly generating data about our behavior: e-mail, IMs, calendar data, and increasingly sensor data about the real world. This is people analytics: using behavioral data about how people work to change how companies are managed. In this course, we’ll first investigate what data we have at our disposal now and in the near future. We’ll also discover what behavioral metrics really matter and how can you communicate these metrics to other stakeholders. Next, we’ll focus on how these new metrics and data streams can rapidly increase the speed and quality of decision making. Similar to A/B testing in the online space, now we can A/B test how businesses are managed. Compensation, IT systems, real estate decisions, and even org charts can now be rapidly deployed and quantitatively tested by combining behavioral data and key performance indicators (KPIs). We’ll discuss examples from Fortune 500 companies that have successfully used people analytics to improve their organizations, as well as how they are transitioning to an A/B testing decision making culture.
- Understanding what behavioral data you already have
- Learning basic people analysis methods
- Identifying organizational problems that you can address with people analytics in the near term
- Understanding common roadblocks to implementing people analytics
- Learning the potential value of people analytics for your organization
Who Should Attend
This course is ideal for directors, senior managers, executives, and business leaders in all industries who have (or want to have) responsibility for improving organizational performance.
Laptops are strongly recommended for this course. Tablets will not be sufficient for the computing activities performed in this course.
This course runs 9:00 am - 5:00 pm on Monday, 9:00 am - 4:30 pm on Tuesday, and 9:00 am - 1:30 pm on Wednesday.
- Honest Signals and Social Physics
- People Analytics: Data and Tools
- Group Exercise: Understanding Complexity
- Exercise Discussion
- Moving from HBR Cases to A/B Testing
- Core Tool: Social Network Analysis
- Group Exercise: Rewire the Class with Lunch
- Group Proposals and Vote
- Rewired Lunch and Badge Data Collection
- Building a People Analytics Team
- Breaking it down:
- What problems do you have today?
- Badge Metric Review
- Group Exercise Data Analysis and Explanation
- Analysis Discussion
- Group Lunch Discussion: Implementing
- People Analytics in Your Organization
- Group Learnings
Links & Resources
- A Study Used Sensors to Show That Men and Women Are Treated Differently at Work. Harvard Business Review, October 23, 2017
- People Analytics, discovered in Cambridge. LinkedIn, July 30, 2017.
The type of content you will learn in this course, whether it's a foundational understanding of the subject, the hottest trends and developments in the field, or suggested practical applications for industry.
How the course is taught, from traditional classroom lectures and riveting discussions to group projects to engaging and interactive simulations and exercises with your peers.
What level of expertise and familiarity the material in this course assumes you have. The greater the amount of introductory material taught in the course, the less you will need to be familiar with when you attend.