As companies power up model development, data science teams are going into overdrive to better connect and communicate with business leaders. Firms are launching data literacy programs, conducting data science town halls, and hosting monthly learning sessions, among other activities to help business teams better understand what data science is and what it can do.
But as firms work to educate their business leaders on topics of data science, machine learning, and analytics, the big question is:
How can they most effectively help non-technical folks “get” data science?
According to Harvard University’s Yael Grushka-Cockayne, the “case method” of instruction—a core of Harvard’s teaching for almost a century—offers a powerful approach to engage business managers and help them understand data science in a business context. Yael is an award-winning teacher and named one of “21 Thought-Leader Professors” in Data Science. Her research and teaching activities focus on data science, forecasting, project management, and behavioral decision-making.
In this blog, we share an edited version of our conversation with Yael on the case method and how data science teams can use it.
Yael Grushka-Cockayne: The case method is a Socratic student-led system where students review a case (a story about how an organization faced a specific business problem) and have to think through possible solutions and what they would do in the same situation.
Unlike other teaching methods, there is no lecturing with the case method. We ask students pointed questions about the case, and learning emerges through the students’ discussion as they debate and test the different courses of action that could have been pursued. Harvard is the leader in using the case method to its fullest.
Yael Grushka-Cockayne: I believe the case method is one of the best ways to teach data science because you are directly tying data science to a business problem that the company is trying to solve.
Students come into the classroom embracing the conversation around what they think that the company should do. What data will they need? What models can solve the challenge? How do you use the insights from your analysis to push things forward?
It encourages brainstorming and real-world problem solving as participants have to figure out the best solution. And when participants discover together how to overcome a business problem, it becomes more memorable, and they will draw on those memories more readily once they return to the office.
It gives business leaders a safe space to test ideas and learn other perspectives from colleagues in the room. Often there isn’t a right or wrong. There’s going to be different ways to tackle a problem. There are some mistakes that we want to avoid, but there are a lot of good approaches to solving a business problem, and that’s all welcome in the classroom.
It helps them understand their role in the process and how they can contribute and interact with the data science team to progress the work. This includes everything from how they think about the business problem to what their part is in collecting data, testing hypotheses, and bringing models to production.
Finally, it helps participants communicate better when working with other disciplines. How many times have you been in a room with a business person and a coder, and they’re not getting anywhere? Effective communication requires some practice and skill, and the case method environment encourages more attention to how you interact with others, how you ask the questions, and how you answer those questions to build these skills.
Yael Grushka-Cockayne: The case method encourages what we call the four Ps, and these are crucial to the problem-solving process:
Yael Grushka-Cockayne: There’s a number of ways data science teams can apply these ideas:
These case review sessions can either be led by internal staff or an external instructor. Having an external instructor offers some benefits because they’re often viewed as impartial so they can push the conversation a little bit in ways that someone internally wouldn’t be able to do, but it’s not a requirement.
At Harvard, we use Domino as an entry point for collaboration. Students log on to the Domino platform to get their assignment and case. They can launch a Python or Jupyter notebook from within Domino to work with some starter code that we provide them. Even if they do nothing else but one starter code, they will get insight into the business problem.
We also use Zoom in class so participants can share their screens as they discuss the case and run code. They see firsthand through the process how dynamic data science is and how together, with everyone’s breadth of experiences, they can get new insights that they may not have gained on their own.