Today, business data and information are some of the most valuable assets of any enterprise. Critical for business success, enterprises are now becoming more and more aware and making conscious efforts to extract maximum business intelligence from their data to better optimize their workflows. Big data analytics and machine learning allow enterprises to perform deep analytics on any information that has been collected during the workflow process. When used wisely, it provides insights on potential savings, threats, company integrity, etcetera, which can be used as a base to build statistical data to highlight any inconsistencies in the organization.
Analytics is an important aspect of leveraging cyber resilience. As cyber attacks get increasingly sophisticated and more persistent, it comes down to the point that all organizations must ramp up their cyber protection as well. While cyber attackers require only one successful attempt to take-down security put in place by organizations, the latter must often rethink the entire cyber security concept, the key point being—improved detection. At this point, big data analytics enters the fray—the detection software has to be able to identify usage patterns, complex analysis actions should be performed rapidly near real-time. In order for these processes to take place, the software must have the ability to run analysis on, both, historical and current data at the same time. This ever-increasing data will provide a firm base to improve the cybersecurity of the organizations.