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- All Subjects: Data Analytics
- Creators: Department of Information Systems
- Member of: Theses and Dissertations
Sports analytics refers to the implementation of data science and analytics techniques within the sports industry. Several sports analysts and team managers have utilized analytical tools to boost overall team and player performance, often through the analysis of historical data. One of the most common techniques employed in sports analytics is that of data mining–the extensive practice of analyzing data in order to extract and deliver insights and findings. Data mining projects are frequently guided with the six-step Cross Industry Standard Process for Data Mining (CRISP-DM) framework. One such sport that has extensively used data science and analytics, and data mining specifically, is that of Formula One (F1). Given the sports’ reliance on technology, race engineers working for F1 constructors often develop statistical models analyzing historical race performance to derive insight of drivers’ success. For the purposes of this project, the perspective of a race engineer working for the F1 constructor McLaren was considered. As the constructor is seeking to gain a competitive advantage for the upcoming F1 season, race performance data concerning previous seasons was collected and analyzed as part of a larger data mining project utilizing the CRISP-DM framework. Statistical models, such as linear regression and random forest, were developed to predict the number of points scored by McLaren racers and the variables most strongly contributed to such scored points. The final results point to specific lap times having to be aimed for as the most important variable in determining the number of points gained, although specific locations also seem prone to McLaren race success. These results in turn will be utilized to develop race strategies for the upcoming season to ensure McLaren has high efficiency against its competitors.
As Clive Humby said, “Data is the new oil” and is becoming ever more important to every industry, profession, and business with incredible applications like artificial intelligence and machine learning. Looking specifically at the Small and Medium Businesses (SMB) market segment, there is a significant gap in the use of data analytics. Only 15% of SMBs have a “data-driven” culture. Companies that leverage data to drive decision-making have seen increased revenue, profit, and employee output. Despite the benefits, SMB owners run into three main issues. First, a lack of bandwidth as time and human capital are stretched thin. Second, technical expertise as many analytics tools require coding expertise or knowledge of systems and tools which many SMBs do not possess. Lastly, many SMBs lack the finances to invest in costly tools or subject matter experts. Enterprise-level organizations will continue to invest in analytics leaving SMBs behind and increasing economic inequality. Our solution is DataMate, a Data as a Service (DaaS) no-code, low-cost, and low-time intensive platform designed to provide end-to-end analytics solutions for SMB owners. The platform allows users to automatically pull data from sources (ex. point of sale, customer relationship management, etc.), store data in a centralized location, and lastly, visualize data through dashboards to enable SMBs with data-driven decision-making capabilities. Once at scale, we will be able to create models and deliver advanced predictive and prescriptive analytics. The global data-as-a-service industry market was valued at $5.5B in 2021 and is expected to grow at a CAGR of 36.9% until 2030. SMBs account for a minority of global revenue share but are expected to grow faster than large enterprises. The Total Addressable Market (TAM) for the data-as-a-service industry of small and medium-sized businesses in the United States is roughly $1.02B and the Serviceable Obtainable Market (SOM) is roughly $2.6M. The DaaS industry is highly competitive with high customer bargaining power and large growth potential. Some direct competitors to DataMate are FiveTran, Looker, Domo, and Alteryx. While offering similar data infrastructure services, no solution can achieve DataMate’s unique product value proposition. A fully operational platform will require considerable technical investment. Our go-to-market strategy consists of a manual and automated phase. To start, leveraging the expertise of data/business analysts to manually build end-to-end analytics solutions. Concurrently, we plan to build an automated platform. By starting to manually build, we can bring revenue on day one while solidifying template dashboards and ETL flows. Additionally, DataMate will start building data solutions only in the restaurant vertical given its large market segment and homogeneity of tools. Given the numerous variations in data needs between SMB industries, a step-by-step rollout allows for quality integration. Eventually, the platform will expand to all industries.
As Clive Humby said, “Data is the new oil” and is becoming ever more important to every industry, profession, and business with incredible applications like artificial intelligence and machine learning. Looking specifically at the Small and Medium Businesses (SMB) market segment, there is a significant gap in the use of data analytics. Only 15% of SMBs have a “data-driven” culture. Companies that leverage data to drive decision-making have seen increased revenue, profit, and employee output. Despite the benefits, SMB owners run into three main issues. First, a lack of bandwidth as time and human capital are stretched thin. Second, technical expertise as many analytics tools require coding expertise or knowledge of systems and tools which many SMBs do not possess. Lastly, many SMBs lack the finances to invest in costly tools or subject matter experts. Enterprise-level organizations will continue to invest in analytics leaving SMBs behind and increasing economic inequality. Our solution is DataMate, a Data as a Service (DaaS) no-code, low-cost, and low-time intensive platform designed to provide end-to-end analytics solutions for SMB owners. The platform allows users to automatically pull data from sources (ex. point of sale, customer relationship management, etc.), store data in a centralized location, and lastly, visualize data through dashboards to enable SMBs with data-driven decision-making capabilities. Once at scale, we will be able to create models and deliver advanced predictive and prescriptive analytics.
The COVID-19 pandemic’s unprecedented nature caused significant disruptions in the global supply chain industry, resulting in setbacks for supply chain operations. The repercussions of the supply chain challenges impacted various industries. This thesis seeks to investigate the impact of the COVID-19 pandemic on the supply chain industry, with a focus on how disruptions have affected the efficiency and resilience of companies within this sector. Data analytics will be leveraged to analyze these disruptions and improve supply chain operations.
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.
