How to leverage your fundamental analytics toolbox to succeed in any companies
Ten months ago, I decided to finally head back to school and get a Master’s degree. Now, this might be a normal path for most students, it was out of the ordinary for me.
I will be honest here, as someone who disliked school for the better half of my life, school work and tests are just not my forte. I did not enjoy school mainly because I was not passionate about the topics and I did not feel like the materials that I was learning in class could be applied to real-world situations. I picked Business Management Economics as my undergraduate major as it was the only thing that I have an interest in. Using my economics and management knowledge, I landed an Operations Manager position with a bank. Although I’ve picked up a tremendous amount of experience in that role, it clearly wasn’t my first choice as I did not stick in that field for long. I was simply bored with what I do on a daily basis. It was not until I stumbled upon the world of Data Analytics that I had a personal paradigm shift. I fell in love with a subject that impacts everyday decisions. However, the career shift was not easy.
The data analytics hype had just started (a degree in Business Analytics was not a thing!) when I first learned about all the fancy things that a business can do with data. As the book, “Analytics Body of Knowledge” by James J. Cochran, puts it
these changes have driven a surge in demand for analytics professionals, and universities are creating departments, curricula, and new program offerings to fill the gap.
Of course, I did not know the field that I fell in love with will be named the “sexiest job of the 21st century.” I went for it anyway.
With a lot of luck and many months of self-learning, I landed my first analytics role with a tech startup. The job was about electric scooters (the sexiest job in the sexist industry of the year!). I was ecstatic, but dreadful at the same time. My fear was not only because I knew nothing about scooters, but I also did not have any practical experience working as an analyst. Although my resume may have looked good and performed well during the interviews since I “knew” all the fancy data terms, I knew I was only good on paper. I quickly realized that I had to keep learning and apply what I learn into practice. Four jobs later and here I am continuing my learning journey through the MSBA program and applying what I learn in my day job. This journey has turned me into a true believer in the value of practical experience.
According to an article on Harvard Business Review, “5 Essential Principles for Understanding Analytics by Thomas H. Davenport, in business,
recognizing the problem and framing it the right way is the most critical parts of a good decision process.
Through the experience that I gained from my analytical jobs, I found that although different companies face different problems and have different use cases with their data, the underlying data analytics fundamental skills remain the same. The most important part about data analytics applications is to use what I already know and apply it to different situations to inform good business decisions. I have applied this principle in recent years as I navigate through my previous jobs and now in the MSBA practicum project with the Mondavi Performing Arts Center. They all may be in completely different industries, I’m able to utilize the same analytics toolbox and practical experience to overcome new challenges.
Unlike most MSBA programs, UC Davis provides us with the unique opportunity to apply our learnings in a real business. This is what excites me the most about the program and what made it an easy choice for me to go after an MSBA degree as I found something that I enjoy studying. In addition to deepening my fundamental analytical skills through the program, I also wanted to work with a group of like-minded individuals to work on a practicum project in yet another industry to see if the same analytical tools could be applied in the arts industry. (Hint: it does!)
This program did just that. Most importantly, the experiential learning component of the MSBA program further confirmed my perspective on why data analytics is an applied skill that works across industries. Unlike my previous data-centric analytics firms, Mondavi is a decision-centric analytics organization that relies heavily on subject matter expertise to make decisions. This experience allows me to work in analytics from a different perspective and a chance to use data to convince decision-makers who are functional experts, and not just data scientists.
Source — Analytics Body of Knowledge by James J. Cochran
Mondavi has many operations complexity due to the ongoing pandemic and as a result, we have the luxury of working closely with many of the functional leads to come up with the best course of action for our deliverables. For instance, one of our main goals for the year is to identify the ideal seating allocation method and a price prediction tool for the Mondavi team to prepare for a reopening plan that is safe for the customers, meet the minimum revenue requirement for the Mondavi Center, and at the same time worthwhile for artists who want to perform at the Jackson Hall venue. We worked as a close-knit team to refine our proposals and delivered our finished products. The main lesson that I learned from this is that just because our decision-makers aren’t data nerds, our team was still able to replicate the same data science lifecycle process that the data science team at my current work uses to satisfy our decision-focused clients. Although the problems are not at all the same, the methods under the hood can be replicated seamlessly.
As we are toward the tail end of the MSBA program and the practicum project with Mondavi, my perspective on data analytics had not changed. Data analytics is a skill set that you can bring to any organization and drive impacts immediately. Whether it’s through formal education or self-learning, the most crucial part about applying data analytics in an organization is not knowing all the statistical methods and machine learning algorithms. It is how you identify the problems and apply the right skills and approach to drive valuable insights to solve business problems. And of course, training makes perfect. (pun intended!)
