That company has developed a massive big data analytics service for its online retail operations that can track and adjust prices based on internal and external data every two minutes.
Take note, this yet another example of the massive power of data analytics.
The field of human resources is no different, and we are seeing massive change in data, analytics and artificial intelligence.
For more than a decade, the rallying cry has been to turn to massive data collection and analytics.
Big data, analytics, and massive storage are important factors in this next frontier.
They have been organized in the following topical sections: big data analytics and massive dataset mining; security and privacy engineering; crowdsourcing and social network data analytics; sensor databases and applications in smart home and city; emerging data management systems and applications; context-based analysis and applications; and data models and advances in query processing.
This is increasingly fueled by new discoveries in computer science and data science, coupled with the quick-paced ubiquity and massive use of computational and data analytics within a variety of urban domains to address the complex challenges of sustainability and urbanization facing the city.
Yet, whereas data once required a large team of skilled analysts to be made useful, today there are a number of enterprise level tools for running high speed data analytics on massive amounts of data, as well as publically available free tools like Google Analytics that offer every business and entrepreneur the opportunity to incorporate data analytics when making decisions.
These real-time data streams are particularly interesting because they can be displayed on spatial dashboards, trigger real-time responses, and be organized into massive data stores for subsequent spatial/temporal analytics.
Modern data analytics applications typically process massive amounts of data on clusters of tens, hundreds, or thousands of machines to support near-real-time decisions.
