As part of the UC Davis Master of Business Analytics program, students get the opportunity to work with industry partners from different industries and utilize analytical or machine learning techniques to help businesses solve problems and drive insights. Aligning with the goals for the MSBA program, practicum projects are intended to help students get more hands-on experience and get the opportunity to work with complex scenarios while understanding how to strengthen their soft skills through cross-functional work. My area of interest is around data analytics, in particular, the practicum project that I’m currently working on has helped me to accumulate the required skills for the analytical field that I’m interested in.
The full lifecycle of using data analytics to drive business decisions is relatively simple to understand. Businesses under different conditions may require different conceptual frameworks to design the analytical process. In our example, the industry that my industry partner in is the financial research distribution industry, where there are upstream analysts who publish reports and use our platform to distribute those to the downstream readers. The conceptual framework that we adopt is Data-Centric Analytics, which means that we preprocess all relevant data and put it together to analyze, draw as much as insights from it, and serve up those insights to the decision-makers to make final decisions. This kind of metric fully utilizes the potential of big data analytics and extracts all possible insights from the data that companies have. Understanding the foundation of how companies solve problems is interesting for students like me who are interested in learning different ways to approach problems in the big data world.
Identifying Business Problems & Design Workflows
“A technically-skilled data scientist may be effective, but a technically-skilled data scientist with a strong sense of business is unstoppable.”
We sometimes hear that business sense matters for people who are working in the analytical field. The reason why business sense plays an important role in the analytical context is that it will help you to spot insightful opportunities in the company’s data. In other words, having a strategic understanding of how business works and what are the goals will help mapping data projects in a more impactful way. How does my practicum project help me develop business sense? There are several aspects. The first aspect is to understand the organization, knowing in-depth insights such as what is the long-term vision, who are the customers, how does the company make money, what are the products, etc. For my practicum experience, there are multiple resources to get these information through connecting with employees, looking up the company’s website, and searching for recent news. Another deeper aspect is to do the analysis of competitors, what culture the company has, and how the company sells its products. Knowing this layer of knowledge will further help with mapping project directions. One example is when our team is designing a dashboard for the Sales & Marketing team, we look at the training videos provided by the sales team and understand how they sell products, think about what aspect of insights we could provide to them so that they can get more clients purchasing premium plans. Developing business sense takes longer since it requires an understanding of both the business and the project itself, but it’s beneficial in the long run.
The Utilization of Softwares and Advanced Analytics
Another side of the analytical world is all about your technical capabilities. Some of the common requirements for an analytics position include intermediate knowledge of Python, advanced level of SQL, as well as a good understanding of statistics. Technical skills are something you can learn before you start working on projects, but the problems that I’ve encountered in real-world situations are not usually taught by any specific course. The difference here would be 70% learning through practicing instead of purely depending on learning inside of classrooms. The biggest lesson that I learned from the practicum project is to take the initiative to explore potential solutions for particular problems. Every company has its uniqueness in the database setup and situations that they’ve encountered, it is our responsibility to explore opportunities and design company-specific solutions to help with the decision-making process. Through the hands-on practice of building machine learning models and performing data wrangling processes, I have learned to always think about n+1 possibilities. Another insight from my practicum experience is to think critically about the designing phase, such as how to deal with edge cases, or reverse logic. Having the technical skills plus a well-understanding of business goals will help you win more than half of competitors in the job market, one additional step will be to have the capabilities to work cross-functionally.
Communication & Prioritization
The very last key skills are to quickly adapt to the working environment and know how to work cross-functionally in terms of prioritizing tasks and getting projects delivered on time. Communication is the main objective under this context. This type of skill is rarely taught by anyone because you never know what kind of situation you’ll encounter in real life or who you are dealing with. In our practicum experience, we found it to be relatively difficult to balance the suggestions from the sales, marketing, product, and design team at first since we are trying to take into consideration everyone’s opinion thus forgetting about the true goal of the project itself. In this case, understanding who are the primary stakeholders and prioritizing all the feedback helped us a lot in terms of assigning tasks to make sure we can deliver on time. It will get tricky when more and more people are involved in the project throughout time, it’s the team’s responsibility to locate primary and secondary audiences and spend enough time on the feedback provided by those people.
To summarize, the main objective is to share some commonly required characteristics for people who are interested in working in the analytical field and how these requirements aligned with my practicum experience. Three key aspects shared include developing business sense, strengthening technical skills, as well as working cross-functionally. Though it’s not easy to grasp these skills quickly, learning through practicing is always a good choice.
Reference:
Keenan, P., Owen, J., & Schumacher, K. (2018, September 26). Introduction to analytics. Retrieved April 19, 2021, from https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119505914.ch1
Holcomb, S. (2019, June 13). How to be a data science unicorn: Developing your “business sense”. Retrieved April 19, 2021, from https://datasciencecareermap.com/2019/06/13/how-to-be-a-data-science-unicorn-developing-your-business-sense/
