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- All Subjects: Data Analytics
- All Subjects: Creative Project
- Creators: Department of Information Systems
This thesis includes three separate documents: a) a comprehensive document detailing the methods and analysis of the creative factors tied to series success, b) an hour long pilot script based on this data, and c) an industry-standard pitch deck for a TV show created with data insights. In a larger sense, the aim of this study is to take the first steps in remedying information asymmetry between streaming services and content creators. If streaming services were more transparent with their data and communicated to their creators what has been proven to work in the past, showrunners and staff writers could have a new tool to increase the competitiveness of their series and aid in show renewal each year.
Created predictive models using R to determine significant variables that help determine whether someone will default on their loans using a data set of almost 900,000 loan applicants.
Corporate buzzword terms like “big data” and “data analytics” are vague in meaning, and are thrown around by media sources often enough to obfuscate their actual meanings. These concepts are then associated with company-wide initiatives beyond the reach of the individual, in a nebulous world where people know that analytics happens, but don’t understand what it is.
The power of data analytics is not reserved for company-wide initiatives, or only employed by Silicon Valley tech start-ups. Its impacts are visible down at the team or department level, and can be conducted by the individual employees. The field of data analytics is evolving, and within it exists a rapid transition in which the individual employee is becoming a source for insight and value creation through the adoption of analytics based approaches.
The purpose of this thesis is to showcase an example of this claim, and demonstrate how an analytics based approach was applied to an existing accounting process to create new insights and information. To do this, I will discuss my development of an Excel based Dashboard Analytics tool, which I completed during my internship with Bechtel Corporation throughout the summer of 2018, and I will use this analytics tool to demonstrate the improvements that small-scale analytics had on a pre-existing process. During this discussion, I will address conceptual aspects of database design that related to my project, and will show how I applied this classroom learning to a working environment. The paper will begin with an overview of the desired goals of the group in which I was based, and will then analyze how the needs of the group led to the creation and implementation of this new analytics-based reporting tool. I will conclude with a discussion of the potential future use of this tool, and how the inclusion of these analytical approaches will continue to shape the working environment.
“InnovationSpace is an entrepreneurial joint venture among the Herberger Institute for Design and the Arts, Ira A. Fulton Schools of Engineering, W.P. Carey School of Business and the Julie Ann Wrigley Global Institute of Sustainability at Arizona State University. The goal […] is to develop products that create market value while serving real societal needs and minimizing impacts on the environment. Put simply, we seek to create products that are progressive, possible and profitable. At the same time, they must have a meaningful impact on the daily lives of ordinary people. InnovationSpace utilizes two fundamental strategies for creating sustainable innovation: a model of new product development known as Integrated Innovation and the emerging field of biomimicry.” — InnovationSpace program syllabus
The focus of the project outlined by Cisco is “to understand the needs of people who face physical, cognitive or sensory disabilities, and develop new products and services for them utilizing the potential of the new technologies called the Internet of Things.” In other words, I am challenged to leverage the Internet of Things technologies to develop a device that benefits individuals with disabilities.
The final product is an automated airport cart — Chariot. Based on stakeholders’ needs interviews, we find that visually impaired people experience difficulties navigating the airport when they need to travel. Many airports attempt to solve this problem by offering wheelchair. However, visually impaired people feel that they are treated unfairly and become dependent on the wheelchairs. Chariot strives to solve this problem by applying the same concept in autonomous vehicle to guide the users through the airport. The users receive their itinerary email that will link to the Chariot app on their phones. When they arrive at the airport, the users simply connect their phones with Chariot and information such as gate number and departure time will be updated in the cart so that Chariot can guide the users to the desired destination. Ultimately, Chariot aims to give visually impaired people more control over their lives.
The goal of this project is to develop a deeper understanding of how machine learning pertains to the business world and how business professionals can capitalize on its capabilities. It explores the end-to-end process of integrating a machine and the tradeoffs and obstacles to consider. This topic is extremely pertinent today as the advent of big data increases and the use of machine learning and artificial intelligence is expanding across industries and functional roles. The approach I took was to expand on a project I championed as a Microsoft intern where I facilitated the integration of a forecasting machine learning model firsthand into the business. I supplement my findings from the experience with research on machine learning as a disruptive technology. This paper will not delve into the technical aspects of coding a machine model, but rather provide a holistic overview of developing the model from a business perspective. My findings show that, while the advantages of machine learning are large and widespread, a lack of visibility and transparency into the algorithms behind machine learning, the necessity for large amounts of data, and the overall complexity of creating accurate models are all tradeoffs to consider when deciding whether or not machine learning is suitable for a certain objective. The results of this paper are important in order to increase the understanding of any business professional on the capabilities and obstacles of integrating machine learning into their business operations.