Cybersecurity Data Analytics System Success: An Exploratory Study on U.S Government Agencies

United States government organizations are compelled to comply with federal, state, and local cybersecurity regulations by law. This includes robust reactive and proactive measures to protect against the ever-increasing cybersecurity threat landscape. Given the intense government regulatory mandates, cybersecurity data analytics systems play an important role in uncovering patterns, optimizing resource allocation, and stimulating innovation. This includes deploying cybersecurity data analytics systems (CDAS) to detect and counteract malicious activities proactively. As such, there is a need to empirically examine the organizational factors that influence the success of CDAS within U.S. government agencies. This study assesses the impact of Top Management Support (TMS), Internal Processes (IP), and Learning and Growth (LAG) from a competitive advantages perspective based on the resource-based view (RBV) theory. This study gathered survey data from cybersecurity and IT professionals and analyzed the results using a second-generation multivariate analysis technique. The findings indicate that TMS, IP, and LAG significantly influence Cybersecurity Data Analytics Systems Success (CDASS). The practical implications of this study promote the need for top management support, efficient internal processes, and ongoing learning and growth initiatives to increase a CDA system’s implementation success, thereby improving the cybersecurity posture of U.S. government agencies and, in turn, improving the state of U.S. national cybersecurity preparedness.