Self-Introduction
Hello everyone! My name is Alena Kusok, and I’m excited to embark on this journey with all of you in MIS 587. I’m currently a systems engineer at Raytheon, where I’ve been working for almost two years. I graduated from Eller with a bachelor’s degree in MIS in 2023, and I’m eager to deepen my knowledge and skills in Business Intelligence throughout this course.
As someone who enjoys applying information systems knowledge in my work, I see this course as an excellent opportunity to advance my career. I’m particularly interested in how BI techniques can uncover valuable insights from both internal transactional data and external “Big Data.” I hope to gain hands-on experience with tools like Tableau and Google Analytics to develop key performance indicators and enhance my understanding of web analytics.
I’m excited to see how the knowledge gained from this course will help me not only in my current role but also in potential future opportunities in corporate positions.
In my free time, I love spending time outdoors with my black lab mix. We enjoy hiking, camping, and exploring the beautiful landscapes around Tucson, where I’ve lived for almost six years after moving from Orlando. I’ve been fortunate enough to travel for work, recently spending three weeks in Guam for customer training, which has broadened my perspective on global business practices. I've also had the opportunity to visit Singapore, Malaysia, Morocco, Japan, and various places across the United States. I truly enjoy traveling in my free time, and I’ve explored many parts of Europe, with Paris being my favorite city. I was lucky enough to spend a whole month, immersing myself in the culture and sights, and I can’t wait to go back someday soon!
Lecture Summary: Introduction to Big Data and Business Intelligence
In this lecture, we explored multiple key topics relating to big data and Business Intelligence (BI). The first topic covered is related to zettabytes and in 2023, the volume of global data generation was around 125 zettabytes, and with projections showing that for 2024 nearing an astounding 150 zettabytes. This exponential growth in data is driven by a variety of sources, including social media platforms (Instagram, TikTok, Twitter), online interactions through reviews on shopping websites and blogs, and technologies such QR codes. A Key concept in the lecture was “datafication”, which illustrates how virtually every action we take whether it’s a social media post, a health tracking entry on an apple watch, or an online or review or purchase (e.g. Amazon.com) can become a valuable data point that organizations can analyze. The lecture emphasized the three essential characteristics of big data: volume, velocity and variety. Together, these elements enable companies to leverage data for informed decision-making and enhance customer experience.
The lecture also examined the evolution of BI from traditional methods that relied primarily on transactional databases to modern approaches that integrate both internal and external data sources. The evolution of Big Data highlights the critical role of BI as a framework that enables organizations to effectively leverage data, transforming raw information into actionable insights that enhance decision-making and improve outcomes across various business operations. The BI lifecycle involves a series of somewhat interactive steps, starting with data collection and processing, which enable businesses to not only measure performance but also identify areas for improvement. Following this, visualization and reporting help communicate findings clearly, while the development of predictive models allows organizations to anticipate future trends, ultimately refining their strategies and enhancing overall operational efficiency. As the volume of Big Data continues to grow, this expansion will create new job opportunities and enhance job security in the field of Business Intelligence. Ultimately, by embracing advancements in Business Intelligence and effectively utilizing Big Data, organizations will be better positioned to remain competitive and responsive in a constantly evolving business landscape.
Applied Analysis of Lecture Concepts
A concept that I found interesting was "datafication", which emphasizes that actions we take online from social media likes to heath tracking is now a data point that organizations can analyze. This paradigm shift enables companies to create personalized experiences (e.g. personalized advertisements) for consumers based on their behaviors and preferences. A prime example of this is Amazon's recommendation engine which is data-driven approach to personalize the customer experience (Mpeshev, 2023). Amazon's recommendation engine analyzes purchase history and browsing behavior of the consumer to suggest relevant products, which overall enhances customer engagement and driving Amazon's sales. Amazon has been able to combine data-driven decision making and a customer-centric approach to not only capture value but capitalize on it. Site data is a major factor in how Amazon collects extensive information, but in addition Amazon also leverages extensive data generated through customer interactions from Alexa and Echo. These points of data collection drive improvements for the site, algorithms and overall sales for the customer and sellers using Amazon (Walter, 2018). These readings showcase how Amazon utilizes Big Data to benefit its corporation and the consumer. With extensive data resources and application of machine learning Amazon has become a leader in product forecasting. The need for data scientists in organizations like Amazon illustrates the growing importance of combining technical skills with business awareness to harness Big Data effectively (Forbes, 2021). This illustrates not just the potential for business growth for Amazon and other corporations but also the societal impact of effective data usage.
Personal Opinion on Application
In my role at work, understanding these concepts will be valuable as we navigate the complexities of projects where data-driven decisions can significantly impact outcomes. The ability to analyze large volumes of data and derive meaningful insights can lead to improved decision-making and operational efficiency. I believe that the BI lifecycle discussed in the lecture: collecting data, cleaning it, visualizing, and developing predictive models, is an essential framework for any data-driven organization. This process allows businesses to adapt and refine their strategies based on real-time insights. For example, if a predictive model does not yield expected results, revisiting earlier steps to explore different data can uncover new opportunities and enhance understanding in leveraging data effectively.
Supplementary Materials
To enhance my understanding of the topics discussed in the lecture, I found several relevant resources that apply to the topics covered:
Hi Alena!
ReplyDeleteWhat a fantastic introduction! I love how you shared your experiences and passions—it really gives us a great sense of who you are. It’s exciting to see someone so enthusiastic about diving deeper into Business Intelligence. Your insights into datafication and how it shapes consumer experiences are spot on! I completely agree that understanding these concepts can significantly impact our work and decision-making processes.
By the way, I also had the chance to travel to both Singapore and Japan, and I absolutely loved it! The cultures and experiences are just amazing. It’s great to hear you’ve been able to travel so much for work too—I think it really helps broaden our perspectives in this globalized business landscape.
Also, I work at Raytheon as well! It’s nice to connect with someone from the same company; maybe we can share some experiences and insights as we progress through this course together. Looking forward to collaborating with you and learning more about Business Intelligence!
Thanks,
Damian