The Intersection of Data Analytics and Data-Driven Innovation

The Intersection of Data Analytics and Data-Driven Innovation

Marcus Tanque (Independent Researcher, USA) and Harry J. Foxwell (George Mason University, USA)
Copyright: © 2020 |Pages: 27
DOI: 10.4018/978-1-5225-9687-5.ch012
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This chapter discusses businesses, key technology implementations, case studies, limitations, and trends. It also presents recommendations to improve data analysis, data-driven innovation, and big data project implementation. Small-to-large-scale project inefficiencies present unique challenges to both public and private sector institutions and their management. Data analytics management, data-driven innovation, and related project initiatives have grown in scope, scale, and frequency. This evolution is due to continued technological advances in analytical methods and computing technologies. Most public and private sector organizations do not deliver on project benefits and results. Many organizational and managerial practices emphasize these technical limitations. Specialized human and technical resources are essential for an organization's effective project completion. Functional and practical areas affecting analytics domain and ability requirements, stakeholder expectations, solution infrastructure choices, legal and ethical concerns will also be discussed in this chapter.
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Applying the right knowledge to questions and challenges is vital for producing actionable data collection results. Data analytics is a process of parsing, purging, cleansing, moving, and visualizing information (OECD, 2015; Desjardins, 2017). The process further builds on the goal of assembling valuable and actionable data (OECD, 2015; Rankin, 2013; Maydon, 2017). The chapter also emphasizes data analytics methods and procedures used in the modern-day technology landscape. It underlines regulations, analytics methods, and the level of management oversight decision makers need to deliver successful project outcomes (Maydon, 2017). Data analysis is a term that describes the end-to-end domain process. Hence, this method applies to analysts and data scientists who have valuable capabilities to acquire, process, visualize, and interpret small and large datasets. The following segment focuses on data analytics’ evolution, challenges, techniques, opportunities, and trends.

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