Mashey’s work in the 1990s aligns with Big Data Phase 1 (1970–2000), when database management and statistical analysis were central (Williams, 2024). During this time, structured data tools like relational database management systems (RDBMS), data warehousing, and online analytical processing became widely used. Mashey focused on the challenges of data scalability and infrastructure stress as businesses began handling larger datasets. His insights helped define the technical needs of organizations during the early stages of data growth. This phase laid the foundation for how structured information would be collected, stored, and analyzed.

By 2005, Magoulas’s definition of Big Data emerged during Big Data Phase 2 (2000–2010), which saw a shift toward web-based and unstructured data (Williams, 2024). This included social media content, user reviews, and blogs that could not be easily handled by traditional systems. Areas such as opinion mining, social media analytics, and large-scale web data extraction became increasingly important. The rise of cloud computing provided the scalability and flexibility needed to process these diverse datasets. As a result, organizations were able to analyze larger volumes of information in new and more powerful ways.

Eventually, Big Data Phase 3 (2010–present) began focusing on data from mobile devices, sensors, and real-time sources (Williams, 2024). Technologies such as location-aware apps, human-computer interaction tools, and real-time analytics became common. While no single individual is credited with shaping this phase, the rapid advancement of AI, the Internet of Things (IoT), and edge computing has driven its growth. Today, industries ranging from healthcare to marketing rely on these tools to interpret massive, fast-moving data. This modern phase supports real-time decision-making and allows for greater personalization, efficiency, and innovation.