AI and Analytics for Business
Student Perspective: Advice from Greg Caiola, WG’16
There are a few questions any incoming MBA student needs to be prepared to answer. Besides hometown and undergraduate university, most introductory conversations with MBAs end up containing two core questions:
- “What did you do before school?”
- “What do you want to do after graduation?”
For many MBAs these are fairly simple questions, and the words “investing, technology, or consulting” come up in a majority of answers. For me, it wasn’t that simple. As for the first question, my job title was “statistician” and my company was Neustar – but for most people that answer doesn’t really contain a lot of valuable information. As for what I wanted to do next, I wasn’t really sure, but I knew I wanted to stay involved with Data and Analytics. Again, that answer is not particularly helpful for most audiences. In the end, when faced with these questions, I would often just throw the most relevant buzzwords at people “Market Research, Ad Tech, Data Science” and then add detail if it seemed that the other party was interested in more than a cursory answer.
However, after two years at Wharton, I am convinced that the nuance of my answer (and answers for other students with similar backgrounds) will become not only more relevant, will become better understood as time goes on. During my time at Wharton, I have been working very closely with both the MBA Data and Analytics Club, and the AI and Analytics for Business Initiative, and one message both groups stress is that in the field of analytics, there are plenty of people that can build models and manipulate data, but there is a severe lack of individuals that can really understand that process and have the management chops to lead companies. Allow me to offer a visual explanation of the current talent landscape, after all, any self-respecting MBA needs to be good at drawing a 2X2 matrix:
The right quadrant is where there is a gap, and I believe that as the industry develops, young statistical analysts will start to identify a desire to fill that gap. MBA programs, ideally the one here at Wharton, will be a prime education destination.
During my second year here at Wharton, I had the privilege of being an MBA Career Fellow – a position where I do my best to advise first-year students on how to pursue their career goals. I have learned a lot of lessons over the last two years, and I am happy to share some common feedback I have given new students interested in conquering that top right quadrant.
Pick an Industry
I think it is important to remember that analytics is a function, not an industry. What someone does with analytics within finance (quantitative trading) will be entirely different from what someone might do with analytics in tech (developing recommendation algorithms, analyzing site usage metrics). Not only will the day-to-day features of the job be different, but the atmosphere, work/life balance, and recruiting norms will be very different. Even the most advanced data scientist won’t be able to get a job at a fintech firm without a solid understanding of the core principles of trading. An MBA offers great opportunities to learn about different industries. Take advantage of those opportunities, and narrow your search to something you are really interested in.
Build a Technical (Coding) Skill
Even if your intention is to be more on the “Business Acumen” side than on the “Technical Expertise” side of the house, it is vital that you have the technical skills to be taken seriously. The good news is it has become incredibly easy to learn the basic technical toolkit for analytics. For those interested, I would start with these:
- R – Open Source Software for all data manipulation, modeling and data science techniques
- SQL – Common language for relational databases and data extraction
- Tableau – Visualization software (free for students)
In addition to a wealth of free online resources for these tools, AIABI sponsors workshops for MBA students to get introduced to all three. In addition, multiple courses in the Wharton statistics department are taught with assignments that require these languages. It is a great way to force yourself to learn. Also – once you have learned the basics, put it on your resume (with a “beginner disclaimer”)! That signals to your employer that you are serious about this, and qualified to talk to more technical staff.
If you come from a background where you have these skills – great! Now learn another!! (Python, SAS, Matlab, Hadoop, LaTeX, etc.)
Mess Around With Real Data
Classes and training can give you a solid first step to learning this material, but nothing will better prepare you for the real world (or real interviews) than working on a real project. AIABI and WDAC sponsor a series of events where you can engage in case competitions with real companies looking to learn something with real data. If this doesn’t work for you, then think of a problem, contact a company, and get to work. Early stage companies are especially craving smart people to analyze their customer data as founders are understandably focused on building products and selling into new customers. You can even just think of a problem that you are particularly interested in (ex. drafting fantasy football players) and force yourself to work through the process. In academia, data is clean, and takeaways are apparent. In the real world, data is messy and takeaways are opaque. The real skill is learning how to thrive in that complicated environment.
Leverage University Resources
Wharton offers world-class facilities in this field, from the entire staff at AIABI to the top notch professors, to the experienced advisors in the MBA Career Management office, and the student network and events organized by the undergraduate Data and Analytics club. If you are not at Wharton, find the appropriate channels at your university – there is no need to go it alone!
Be Patient and Persistent
The recruiting landscape for data and analytics is still developing. Companies are still figuring out what skill sets they need, and how to identify these skill sets in a diverse group of applicants. An MBA offers a unique perspective, but it is not always one that a company knows how to integrate (or has an opening for). Unlike other MBA careers, a career in data and analytics will require significant research and networking on your end. That being said – the jobs are there and the connections are there, you just have to put in the work to find them.When I first came to Wharton, I was initially concerned with my inability to answer the two career focus questions posed earlier. I kept thinking to myself – maybe I should have gotten a degree in statistics or computer science? Now, two years later, I am so happy I made the choice I did. I believe that an MBA, particularly one at Wharton can be such an enriching experience for those studying advanced analytics. I believe that I have positioned myself well to succeed with a combination of technical experience and business acumen, and I believe that as time goes on, Wharton will only become better equipped to guide students like me to successful futures.
When I first came to Wharton, I was initially concerned with my inability to answer the two career focus questions posed earlier. I kept thinking to myself – maybe I should have gotten a degree in statistics or computer science? Now, two years later, I am so happy I made the choice I did. I believe that an MBA, particularly one at Wharton can be such an enriching experience for those studying advanced analytics. I believe that I have positioned myself well to succeed with a combination of technical experience and business acumen, and I believe that as time goes on, Wharton will only become better equipped to guide students like me to successful futures.