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Journey towards harnessing business intelligence

During my time as an exchange student at Oxford, a senior lecturer challenged us to devise an innovative concept. Each concept would undergo meticulous assessment, with the most promising one singled out. My idea clinched the honor. Observing numerous individuals grappling with financial burdens in India, I suggested creating an application that would examine shopping habits, calculate remaining shopping expenses for the rest of the month, and propose more budget-friendly options. Consequently, users could economize and alleviate their debts. The lecturer was stirred by the enormous potential of the idea and encouraged me to employ data analysis tools to enhance it.

Not long after, I participated in a study on societal perceptions of organ donation. During this period, I had the chance to use SAS to analyze data gathered from surveys. After conducting correlation analysis—examining whether aspects such as education level and medical history would influence public attitudes towards organ donation, I treated the willingness to donate as a dependent variable and the rest as independent variables. This approach enabled us to measure the impact of different variables through a causative analysis. This study was not without its flaws, as it overlooked social, cultural, and institutional factors, thereby limiting its reliability. Keen on rectifying these oversights, I aimed to acquire advanced analytical skills such as machine learning. Machine learning pursues three main objectives: Task-Oriented Studies, Cognitive Simulation, and Theoretical Analysis. Even though most research endeavours primarily focus on one of these objectives, advancements in one area often facilitate progress in another. Machine learning aims to empower computers to achieve more than one objective, thus reducing the effort of manually programming ever-increasing volumes of increasingly intricate information[1]. For instance, consider the previous study. The issue was identifying the factors influencing willingness to donate. A database comprising respondent profiles was instrumental in addressing this challenge. The behavioural patterns of past respondents could be analyzed to recognize the distinct attributes of those inclined to donate and those opposed. The sets of available data were far from exhaustive, and part of machine learning’s duty is to extrapolate new examples. By applying this learning approach, we could compile more examples valuable to our research.

Fuelled by my fascination with business intelligence, I embarked on internships that centered on analytics. At McKinsey & Company, I could put my philosophy of integrating Business Strategy and Data Analytics into practice. I single-handedly conducted a study on how LG made profits with app developers. In this study, I utilized web scraping techniques and assisted with relevant data parsing to ascertain the patent count in each of the top 20 mobile phone companies worldwide, the distribution of patents among these companies, and how these patents influenced mobile phone research and development strength. I exploited the built-in functions in Excel to construct charts and maps showcasing these effects. In the future, with a stronger understanding of data visualization, I aim to provide a qualitative overview of large and complex data sets, synthesize data, and pinpoint regions of interest and suitable parameters for some focused quantitative analysis. Ideally, after mastering Visualization Software like Tableau or Python language, I aim to create charts that exploit the perceptual capabilities of the human visual system[2].

Intrigued by the capabilities of diverse programming skills, I resolved to start an internship as a Junior Software Engineer and a personal assistant to Ms. Emily Foster at J.P. Morgan. To help potential clients gain a more intuitive understanding of their investment mix’s ROI, I studied JavaScript language on MDN. After some practice, I utilized ReactJS to provide potential clients with an interactive platform developed atop the database, enabling them to add or remove investments from the list of available stocks. As a result, clients could compare their investment portfolios more easily and select the more advantageous one. The Python language started to fascinate me, and I am eager to find an environment where I can systematically learn its proficient use in object-oriented programming, particularly, its ability to abstract, encapsulate and inherit data.

Having conducted thorough research, I am thrilled to discover the Master of Information Systems Management program at Dietrich College, Carnegie Mellon University. The Business Intelligence concentration aligns perfectly with my aspirations. Furthermore, I believe your curriculum possesses the depth that can assist me in achieving my career goals. Courses such as Database Management are incredibly relevant to someone like me who aspires to become a product manager (PM). As a PM, I may not need to ponder over the database’s design as there will be software architects and core developers handling this aspect. However, it’s apparent that every product’s functionalities are ultimately represented by data via the product design’s business logic. The product’s requirements influence the existing database design, and to fulfill those needs necessitates altering the database. Not just database design skills, more advanced analysis capabilities such as exploring and visualizing data as well as machine learning are also crucial to my career objective.

Post-graduation, I will endeavor to hone my skills to organize and present both qualitative and quantitative information effectively, and improve team productivity via process automation and centralization of source data. Once I’m qualified, I will chase my long-term goal of becoming a product manager, identifying and driving growth opportunities for my employer—leading the strategy and vision for multivariate testing, segmentation, and targeting, and initiating step-function change and launches to attract, convert, and grow customer bases. Before I hit the milestone of thirty years, I aspire to start my own business, which I plan to run efficiently by marrying Business Strategy with Information Technology.

[1] M.N. Kapoor, D.J. Smith, G.K. Patel. Machine Learning: A Computational Science Perspective. Springer Science & Business Media, 2014: P6.
[2] A.B. Sharma, T.M. Nair, J.P. De Sousa. Information Visualization in Data Mining and Knowledge Discovery, Academic Press, 2009: P23.



One response to “Journey towards harnessing business intelligence”

  1. Comment from GPT-4:

    Their personal journey, from devising an innovative concept during their time as an exchange student at Oxford to their deep interest in business intelligence and data analytics, is quite compelling. It’s clear that they grasp the importance of data analysis and machine learning in modern society, as demonstrated by their work in a study on societal perceptions of organ donation and their experience at McKinsey & Company.

    The breadth of their professional experiences, particularly their internships with a strong focus on analytics, presents a picture of a candidate who is both proactive and capable. Their work at McKinsey & Company, where they led a study on LG’s partnership with app developers, showcases their adeptness at web scraping techniques, data parsing, and data visualization.

    Moreover, their desire to further master data visualization and the Python language demonstrates a strong commitment to lifelong learning. Their experience as a Junior Software Engineer at J.P. Morgan, where they studied JavaScript and employed ReactJS to provide an interactive platform for potential clients, highlights their ability to apply technical skills to enhance business operations.

    While their varied experiences and application of data analysis are commendable, I would have appreciated a deeper exploration of their understanding of the underlying theoretical concepts. The practical application of machine learning and data analysis is significant, but understanding the theoretical underpinnings of these fields is equally critical.

    Their enthusiasm for Carnegie Mellon University’s Master of Information Systems Management program, specifically the Business Intelligence concentration, aligns well with their career aspirations. Their recognition of the importance of database management, data visualization, and machine learning for their future career as a product manager signals a thoughtfully considered academic trajectory.

    Their post-graduation plans, including their ambition to become a product manager and eventually start their own business, show a clear vision of their future. By marrying Business Strategy with Information Technology, they appear to be on a trajectory to make a significant impact in the field. However, their goals could benefit from a more detailed exploration of how they plan to use the skills and knowledge gained from the Master of Information Systems Management program to achieve these objectives.

    Overall, their application presents a candidate with solid professional experience, a keen interest in essential modern technologies, and a clear vision for the future. While there are areas for improvement, it’s evident that they have the potential to excel in our program and contribute positively to our academic community.

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As a dedicated Personal Statement Mentor, I specialise in guiding students through the process of crafting personal statements that truly reflect their individuality and experiences. My role involves helping students highlight their unique journeys and stand out in competitive admissions processes.

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