Startup Life

Technology, Culture, and Why MBAs Should Stop Learning to Code

Almost exactly a year ago, I sat in a room with about 150 of my soon-to-be classmates. On campus nearly three weeks ahead of our first official day of classes, we had all arrived for what we affectionately called ‘math camp,’ a week-long crash course in statistics that would prepare us for our heavily quantitative first semester as full-time MBA students at Berkeley Haas.

Having studied International Relations and German as an undergraduate, I knew that I needed to brush up: P-value? Confidence interval? R-squared? That last time those concepts had crossed my mind, Obama was in the oval office and my iPod had a click wheel. Still, I was surprised to see that over half of my 270-person MBA cohort felt the same. 

Several weeks later when I sat down to take the first exam in Haas’s required Data and Decisions core course, it began to make sense. The exam was filled with statistical concepts in contexts I hadn’t expected: riding in an Uber, buying real estate in San Francisco, predicting economic recessions, and even rolling out a new flavor of White Claw hard seltzer. The message that we were being given was clear: in order to run a successful business in any industry, you must be able to confidently work with data.

In keeping with this trend, data science courses were among the first to bid out the following semester. My classmates begged and bartered for seats in Marketing Analytics, while secondary markets cropped up for courses that taught intro to HTML, SQL, and Python. 

What my fellow MBAs and I were picking up on is the value of data fluency in a modern business landscape. Data fluency is the ability to understand or interpret data using a shared language and set of tools that allow the user to distill actionable insights from a raw data set. 

At most companies, data fluency still requires some level of technical expertise -- friends of mine entering leadership training programs at Amazon and Google were among those clamoring to learn the basics of SQL last spring -- but this system isn’t sustainable. Data volume is expanding rapidly, in fact ninety percent of the data that exists in the world was created in the last two years alone. It is unsurprising then that the growth of enterprise data has far outpaced the capacity of analytics teams, creating a frustrating operational bottleneck effect. A recent report by Forrester Research shows that a whopping 67% of enterprise data is never used for analytics. Even for organizations that do have sizable analytics teams capable of keeping up with business requests, building dashboards and reports can take days, even weeks using existing business intelligence platforms. For modern managers who need to respond to changing marketplace demands in real time, this lag time is debilitating.

Whether it be maximizing marketing campaign results, optimizing supply chain efficiency, or better understanding consumer spending habits, leaders across all industries are under increasing pressure to make faster, smarter decisions backed by data but are limited by their own inability to perform the analytics. The future of business therefore lies in the ability to make data-driven insights more widely accessible and available faster. In other words, remove the BI bottleneck. A recent study by the Harvard Business Review found that the most forward-thinking organizations are both empowering and equipping their frontline workers to access relevant data and make decisions in the moment. Companies that have implemented this strategy are already seeing results. Canadian Tire, one of the largest retailers in Canada, recently made business insights available to 4,500 of its employees and saw sales jump 20%, despite 40% of its stores being temporarily closed due to the pandemic.

In its report, HBR identified two primary factors that contribute to a successful business intelligence transformation: technology and culture. Companies must adopt the digital tools that allow users to ‘ask questions and get answers in a dynamic way with an easy user experience,’ and commit to the use of these tools at all levels of the organization. 

When I joined ThoughtSpot for my MBA internship last spring, I very quickly realized the transformational potential of the technology. By enabling business users to easily search their own data to surface insights, ThoughtSpot is unlocking the power of data for millions of new users. The product speaks for itself, but it is the company’s endless drive for selfless excellence that makes it possible. In my first three months at ThoughtSpot, I experienced a company culture that empowers all employees to make a difference and inspires collaboration. Even in a virtual environment, the energy is palpable. This is in large part due to ThoughtSpot leadership, who simultaneously act as industry leading experts and hands-on managers, guiding both enterprise executives and frontline employees through a digital and cultural revolution. 

I’ll return to Berkeley campus this fall and rejoin my classmates in our data science and business analytics courses. But when we open our Jupyter notebooks and ‘Select *’ I’ll smile knowing that in ten years MBAs won’t need to calmor for technical skills, they’ll be using ThoughtSpot.

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