Two weeks ago, I had the chance to conduct a workshop at The University of California, Berkeley’s Haas School of Business as part of Professor Gregory La Blanc’s Data Science and Strategy class for MBAs and business leaders. This meant showcasing a subset of the comprehensive Machine Learning capabilities of the BigML platform such as Models (Decision Trees), Logistic Regressions and Ensembles while solving some example use cases centered around the predictive use cases of disease diagnostics and credit risk analysis. The best of it was that those in the classroom got to replicate those use cases in their own BigML accounts instead of passively observing.
According to the syllabus, the objective of the Data Strategy course is to provide an understanding of the role of data and statistical analysis in managerial decision-making with a specific focus on the role of managers as both consumers and producers of information, illustrating how finding and/or developing the right data and applying appropriate statistical methods can help solve problems in business. As such, the main focus areas are developing literacy within the potentially intimidating field of quantitative analytics and the ability to assess existing business models from that analytical prism.
As an MBA that has followed a career trajectory spanning highly data-driven roles such as marketing analytics, software product management, and business intelligence I have consistently the beneficiary of following an empirical approach informed by insights based on business data harvested from various systems of record.
After the workshop, I’m very encouraged to have seen the conviction and the resolve from tomorrow’s MBA candidates to own up to the “In God we trust, but all else bring data” mentality. In addition to that broader impression, I’d like to share some findings from an informal survey shared with the attendees.
- The class had a good mix of those with technical degrees (engineering, math, etc.) and non-technical degrees.
- Based on survey feedback, more than two-thirds of the class did not have any prior experience with Machine Learning whatsoever. The remaining ones had some limited exposure in the form of self-learning or a related class they took as part of their former technical education. With that said, none had practiced Machine Learning in their prior careers. All in all, they were newbies to Machine Learning.
- On a very positive note, after the workshop, most respondents thought Machine Learning can be described as a more advanced form of analytics while some opined that it’s also increasingly a must-learn skill set for any white-collar professional. Interestingly, no attendees mentioned that Machine Learning is too complex and confusing or “overhyped” even though those were also offered as attitudinal choices. We’ve been observing this new behavior for multiple years now. Some refer to it as the Citizen Data Scientist movement even though I don’t much fancy that phrase but am fully in support of the core concept it represents.
- Perhaps the most interesting feedback was related to the main motives in learning Machine Learning. Almost all respondents agreed that they would like to be able to better communicate with Machine Learning specialists or Data Engineers in their future jobs by having a good grasp of the core concepts of Machine Learning ( e.g., cut through ‘hype’ or jargon) as well as being self-sufficient when it comes to discovering insights in business data they have direct access to. Following those top two reasons was the perception that Machine Learning has become a highly desirable skill by employers potentially giving them an edge when re-entering the job market. Close behind that third motivation was the fact that some find Machine Learning intellectually stimulating regardless of its implications on their future career. I suspect those were skewed to the left-brained ones with technical degrees.
- Last but not least, almost everyone in the classroom thought that they were likely to use BigML especially when they are considering a new predictive use case where they have access to relevant business data.
I predict future business leaders will follow in the footsteps of examples like NDA Lynn such that they won’t be afraid to autonomously initiate and execute their search for new business insights with or without help from scientists and/or researchers in their organizations. We’ll keep tirelessly promoting the promise and potential of Machine Learning and see how far we can take this prediction.