Jack O'Donoghue reflects on his internship at Hotel Trader
The following blog post contains personal opinions and viewpoints expressed by the author. The author's views are their own and do not represent the official stance of Berkeley IEOR.
Job Title: Business Intelligence Manager
Company: Hotel Trader
Can you share a specific project or task you worked on, or are currently working on, that is advancing the knowledge you learned while you were in the Berkeley Analytics classroom?
Right now, my role is highly focused on data engineering – laying the groundwork for further data analytics, machine learning, and sophisticated modeling. Two classes I took at Berkeley, with Prof. Stewart Liu and Prof. Ilan Adler, provided the theoretical foundation that I now lean on in my professional role.
INDENG 215 (Analysis and Design of Databases) enhanced my understanding of relational databases, complex querying, and data normalization. This knowledge is crucial in my job as I work with data from multiple cloud and database warehouses with diverse architectures. INDENG 240 (Optimization Analytics) reinforced the importance of maximizing efficiency while solving business problems. Today, I apply these principles to enhance efficiency in our data warehouse and BI environments, directly impacting the bottom line by optimizing data operations.
In the real-world, the tasks involved in data engineering can often be messier and more complex than in the academic world. Yet, the solid foundation from my coursework at Berkeley has been instrumental in guiding me through these challenges. These courses have not only prepared me to grapple with the intricacies of the real world but also instilled in me the ability to continually adapt and learn in the face of new challenges.
Has your experience influenced your perspective on the industry or field you are in? If so, how?
Definitely, my experience so far has really influenced my perspective on the travel sector and data science space in general. I've always believed that the potential of analytics is universal - it can revolutionize any industry, not just the traditionally data-rich sectors like finance and healthcare. My past experience with data in politics and sport, as well as a range of projects at Berkeley, have underscored this. Now in my role at Hotel Trader, I've seen first-hand how impactful and transformative data-driven decision-making can be.
But equally, I've come to appreciate the importance of data infrastructure maturity. As eager as we may be to harness the power of AI and cutting-edge data science, we must first solidify data foundations. In a classroom environment, we had the luxury of dealing with relatively clean datasets, with the primary focus being on modeling. However, stepping into the industry, the picture is a bit more complex with several more hurdles to cross before reaching the stage of high-performance modeling. This doesn't mean advanced analytics and machine learning are distant dreams, but it's a definite motivator to put in the groundwork that will enable future breakthroughs.