Should you choose a MBA or a MBAN?

Should you choose a MBA or a MBAN?

 Imagine, you’re sitting in your room nervously waiting for admission decisions to come out. Suddenly, a flurry of emails pop-up: you look at them and gasp. You’ve gotten in everywhere you wanted: Stanford’s M.S. in Data Science, Harvard’s MBA, and MIT’s Masters in Business Analytics.

Now, you face the question. Which program should you do? How can you choose between an MBA or a masters in analytics?

Over the past few years, the number of applicants for business analytics programs has skyrocketed. In parallel, since 2015 the number of MBA applicants in the US has declined yearly. These contrasting trends should make applicants think critically about which program they choose.

At Silk Road Education, where we focus on helping students in Asia enter business analytics careers, we use a three-part framework for choosing between these programs. Prospective students should consider these factors when deciding on the type of program to apply for.

Consideration #1: What you do (Content of the work)

Before choosing an MBA or M.S. in analytics/data science, you should think about what type of work you enjoy.

A recent report from Forbes showed that data scientists spend 60% of their time on data cleaning and organizing. What that means in practice is that when you become an analytics-focused practitioner, you spend a lot of your time working on detailed, coding work.

More broadly, as an analytics practitioner, you think about how to use data to answer business questions. That means that you’ll focus on what algorithms and which data sources you need to make progress.

As a recent MBA graduate, your focus is often more strategic and conceptual than a data analyst. For most recent graduates, the focus of the work is higher level. As you advance in your career, you’ll likely work more as a manager, directing and leading teams.

Of course, as an MBA graduate, you still need to use data to answer questions. However, you will work with the data itself less– setting goals for the analytics and seeking outputs. But, you may not understand the details that go into creating these analyses.

Before choosing an MBA or M.S. in analytics/data science, you should think about what type of work you enjoy. PC/GaudiLab@ShutterStock

As we’ll discuss in consideration #3, we should not mistake having an MBA as being a manager. As a data scientist you can lead teams and advance in an organization. In a similar vein, having an analytical background does not condemn you to only working as an analyst. Rather think about the level of detail that you enjoy working in. Do you want to understand what is driving the analytics? Or would you rather stay high level and strategic?

Consideration #2 How you are rewarded (Compensation):

When choosing what to study consider the finances. In the case of an MBA vs. a Masters in Analytics, which pays more after graduation? Data scientists are one of the most well-paid professions. However, MBAs are known to command a high salary.

To understand this trade-off, let’s look at two cases of this choice. The first case is Harvard (a top school equivalent) and the second is the national average (an average school equivalent.)

At Harvard, a 2nd year MBA student after graduating can expect to make $140,000. They can also expect to get roughly a ~$25,000 signing bonus. Roughly, ~30% of the graduating class went into consulting, another ~20% went into finance, and another ~20% went into Tech.

Now let’s compare that to Harvard’s equivalent data analytics degree: the Harvard’s School of Engineering and Applied Science Masters in Computational Science*.  In 2018, graduates of this program went on to work at places like AirBnB, Facebook, and Google. On average, the programs reports graduates earned between $80,000 - $140,000. So, let’s say on average about ~$110,00 per year.

For top schools, the financial advantage of getting an MBA probably outweighs that of getting an MS in analytics. However, what if you’re not looking at Harvard? Rather, you’re looking at an average school across the US. In this case, let’s hop across the river from Boston to Boston University. 

In this case, the compensation advantage for MBAs decreases dramatically. A graduate of the MBA program at Boston University should expect to receive a starting salary around $90,000. Meanwhile, the graduates from BU’s master of analytics, which can be taken as an online program, receive an average salary of $80,000

Add on that the BU MBA program typically had five years of work experience and the difference in salary narrows even farther. When it comes to less highly ranked programs, the salary advantage of an MBA almost entirely goes away. 

Consideration #3:  Where you can go after (Career prospects)

Beyond compensation, what does your career look like after these two degrees? Where can you work and what can you look forward to in ten or twenty years?

In terms of the type of company this degree can give you access to, a Master’s in Analytics likely wins. Mckinsey Global Institute reports that by 2024 there could be 240,000 unfillled data analytics roles in the US.   need for an analytics skill set. That means that top employers like (Google, McKinsey, GE, etc.) are desperate for top analytical talent. Because there are fewer technical talents, competition is less intense and so the opportunity to join top companies is higher.

Take the case of New York University’s data science program. Graduates from the data science masters ended up in employers like Apple, Uber, & Facebook. Whereas MBA graduates had much more limited access to employers like these. Most ended up in impressive, but traditional companies like Deloitte, Morgan Stanely, & PwC. 

What about after you’ve gotten a job? What path can your career take?

For an MBA, the path to top management is clear and well-worn. Though that means there is significant competition, it also means that it is doable with a combination of luck, skill, and hardwork.

For analytics practitioners, this path up the career ladder is less clear.

On one hand, companies are desperate to get technically savvy leadership. In 2011, only 12% of Fortune 500 companies had Chief Data Officers. In 2019, that number will increase to ~90% . For many leaders, analytics was their ticket to the c-suite.

On the other hand, traditional companies can often limit data scientists and analysts advancement into leadership. Most companies leadership took the traditional MBA route. As a result, when they think of future leaders, they often think of people that look like them. Frustratingly, this can lead to an implicit form of discrimination against analytics professionals, typecasting them as “data nerds” who don’t understand business problems.

In many ways, the variance for an analytics professional is higher than that of an MBA. Because the path is less well worn, there are more opportunities for individuals to succeed. Simultaneously, however, it also means that there are fewer role models to follow down this path.

For the first time, there is a true challenger to the MBA – the MS in analytics or the “Masters in Business Analytics.” For you, think critically about the three criterion: what do you want to do? How do you want to be rewarded? And where do you see yourself in 5-10 years?

*Harvard last year began a Masters in Data Science, which is roughly identical to this program. Interestingly, the dean of Harvard SEAS, where the program is held, reported that the new data science program led to a 2% increase in total applicants to the school.

執行編輯:張詠晴
核稿編輯:林欣蘋

Photo Credit:GaudiLab@ShutterStock

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