Data everyday as community‐driven science: Athletes' critical data literacy practices in collegiate sports contexts

Clegg, Tamara; Cleveland, K; Weight, Erianne A.; Greene, Daniel; Elmqvist, Niklas

doi:10.1002/tea.21842

Cited by 8 publications

(2 citation statements)

References 54 publications

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“…Athletes may be uneasy about the sharing of their health and performance data, necessitating robust privacy measures to ensure data security and consent. The use of data science in sports analytics may unintentionally lead to certain consequences (Clegg et al, 2023). For instance, overemphasis on certain metrics might inadvertently contribute to biased decision-making or the neglect of crucial aspects not captured by data, such as intangible qualities in players.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

Data science in sports analytics: A review of performance optimization and fan engagemen

Ogugua Chimezie Obi,

Samuel Onimisi Dawodu,

Shedrack Onwusinkwue

et al. 2024

World J. Adv. Res. Rev.

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The intersection of data science and sports analytics has emerged as a powerful catalyst in revolutionizing the landscape of sports performance and fan engagement. This review explores the multifaceted role of data science in optimizing athlete performance and enhancing the overall experience for sports enthusiasts. In the realm of performance optimization, data science has become an indispensable tool for coaches, analysts, and athletes alike. Advanced statistical models, machine learning algorithms, and predictive analytics are employed to extract actionable insights from massive datasets encompassing player statistics, biomechanical data, and in-game dynamics. These insights not only aid in strategic decision-making but also facilitate personalized training regimens, injury prevention strategies, and the fine-tuning of game tactics. The integration of wearables and sensors further amplifies the granularity of data, enabling a more comprehensive understanding of an athlete's physical and mental well-being. Beyond the confines of the playing field, data science has significantly reshaped the landscape of fan engagement. Leveraging big data, social media analytics, and user behavior patterns, sports organizations can tailor content and interactions to create a more immersive and personalized experience for fans. Predictive modeling allows for the anticipation of fan preferences, enabling targeted marketing strategies and the creation of interactive platforms that foster a deeper connection between fans and their favorite teams. In conclusion, the symbiotic relationship between data science, sports analytics, performance optimization, and fan engagement is at the forefront of innovation in the sports industry. As technology continues to evolve, the integration of cutting-edge data-driven methodologies will undoubtedly redefine the way athletes train, compete, and captivate audiences worldwide. This review provides a comprehensive overview of the current landscape, highlighting the transformative impact of data science in shaping the future of sports.

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Section: Challenges and Future Directionsmentioning

confidence: 99%

Data science in sports analytics: A review of performance optimization and fan engagemen

Ogugua Chimezie Obi,

Samuel Onimisi Dawodu,

Shedrack Onwusinkwue

et al. 2024

World J. Adv. Res. Rev.

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show abstract

“…Employees at all levels should understand the importance of data, how to interpret data insights, and how to use data to inform decision-making. Training programs, workshops, and educational resources can help employees develop data literacy skills, enabling them to leverage data effectively in their day-to-day work (Clegg et al, 2023). Transparency and collaboration are essential components of a data-driven culture.…”

Section: Best Practices For Leveraging Big Data In Agile Transformationmentioning

confidence: 99%

Leveraging big data for agile transformation in technology firms: Implementation and best practices

Bayode Dona Simpson,

Ebunoluwa Johnson,

Gbenga Sheriff Adeleke

et al. 2024

Eng. sci. technol. j.

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The rapid pace of technological advancements and the increasing demand for innovation have compelled technology firms to adopt Agile methodologies, which promote flexibility, speed, and customer-centric development. Concurrently, the explosion of Big Data has provided these firms with unprecedented opportunities to enhance their decision-making processes, optimize operations, and gain deeper insights into market and customer behaviors. This review explores the integration of Big Data with Agile transformation in technology firms, emphasizing implementation strategies and best practices to maximize the benefits of both paradigms. Agile transformation involves shifting from traditional, linear development processes to iterative and incremental methodologies that facilitate continuous improvement and adaptation. Big Data, characterized by its volume, velocity, variety, and veracity, offers valuable insights that can drive more informed and strategic decision-making within Agile frameworks. The integration of Big Data into Agile practices can significantly enhance product development cycles, improve customer satisfaction, and streamline operations. The implementation of Big Data in Agile transformation requires a well-defined strategy that includes setting clear objectives, building a robust data infrastructure, and ensuring data quality and security. Establishing success metrics aligned with business goals is crucial for measuring the impact of Big Data initiatives. Building a scalable data infrastructure involves deploying advanced data collection, storage, and processing solutions that can handle the diverse and voluminous data typical of technology firms. Ensuring data quality and integrity is essential for deriving accurate insights that inform Agile processes. Integrating Big Data with Agile practices involves incorporating data analytics into Agile ceremonies such as sprint planning, reviews, and retrospectives. This integration facilitates real-time feedback and continuous delivery, allowing teams to respond swiftly to changes and improve their products iteratively. Developing data competency within Agile teams is another critical aspect, requiring investments in training, upskilling employees, and hiring data science talent to interpret and leverage data effectively. Best practices for leveraging Big Data in Agile transformation include cultivating a data-driven culture that encourages data literacy and promotes transparency and collaboration across the organization. Advanced analytics and artificial intelligence (AI) play a pivotal role in harnessing predictive insights and enabling proactive decision-making. Implementing advanced data visualization tools helps teams understand complex data patterns and trends, enhancing their ability to make informed decisions. An iterative and incremental approach to implementation is recommended, starting with pilot projects to test and refine Big Data initiatives before scaling them across the organization. Regularly monitoring and measuring key performance indicators (KPIs) ensures that strategies remain aligned with business objectives and allows for timely adjustments based on feedback and results. Collaboration with stakeholders, including cross-functional teams and customers, is vital for ensuring that Big Data insights are effectively integrated into Agile processes and that development efforts align with market needs. The future of Big Data and Agile transformation in technology firms looks promising, with emerging technologies such as the Internet of Things (IoT), blockchain, and AI poised to further enhance these capabilities. However, firms must also navigate ethical and legal considerations, including data privacy and compliance, to ensure responsible use of data analytics. Leveraging Big Data for Agile transformation offers technology firms a powerful combination of speed, adaptability, and data-driven insights. By adopting strategic implementation practices and adhering to best practices, these firms can achieve significant operational efficiencies, enhanced product development, and improved customer satisfaction, positioning themselves for sustained success in a rapidly evolving technological landscape. Keywords: Big Data, Transformation, Technology Firm, Best Practices.

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Contextualizing Everyday Data Literacies: The Case of Recreational Runners

Palsa

Mertala

2023

International Journal of Human–Computer Interaction

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Data everyday as community‐driven science: Athletes' critical data literacy practices in collegiate sports contexts

Cited by 8 publications

References 54 publications

Data science in sports analytics: A review of performance optimization and fan engagemen

Data science in sports analytics: A review of performance optimization and fan engagemen

Leveraging big data for agile transformation in technology firms: Implementation and best practices

Contextualizing Everyday Data Literacies: The Case of Recreational Runners

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