People and electronic gadgets are generating voluminous amount of data every day. You play a song or a video, shop through online stores or you just simply surf some popular pages, these activities are spawning data with greater velocity and even with greater variety. This large cookie collection, data exchanges and analytics worked out on such huge data collection to find out patterns and correlations is – in short Big Data Analytics.
With the kind of such huge “Data Pool”, boon lies in ability to make decisions in a timely and accurate way. It also allows for improved operational effectiveness and opens up new opportunities for revenue generation. But the foul part of it lies in, how to store and process such large amounts of data? Even this, at times lead to security and fraud issues.
Security is incredibly important in organization because if you ever have an attack on data warehouse and have your property stolen, it’s a reputational risk. Customers will never be interested to transact with companies who lose their private information. Chief Information Security Officer’s (CISO) are looking at measures to combat persistent threats, mitigating frauds on business processes, preventing hacktivism on their network as well as identifying internal threats for the organization.
Solution for such malicious threats may be - Big Data Analytics. It improves the visibility that security team has of their security processes by monitoring the network, log information from the host, identifying information from the access management and many other security devices that organization have including Firewall & other security information.
“Security intelligence” is already a big data solution mainly because it can yield terabytes of information that needs to be processed in real time or atleast near to real time to deliver important insights for organization. Whether it’s about crime detection, financial misconduct or national security, big data analytics provides the capability to store and analyze vast amounts of electronic communication data to identify patterns or connections that may indicate suspicious behavior.
Big Data Analytics helps in mitigating the risks arising out of the full gamut of fraud and financial crimes like mortgage fraud, tax evasion or insider trading. Suppose using statistical parameters on a share trading platform to calculate outliers with unusual gains/losses helps to find out anomalies, which often turns out to be the indicators of fraud.
Similarly, banks have been using the tool of “Predictive Analytics” for their risk and fraud management system whereby they aggregate information from multiple sources about the credit worthiness or credit score of their customers. These statistics provide banks with real time risk intelligence which allows them to take decisions based on hundreds of variables.
Data privacy is still vague and tremendously intricate, making it capable of creating a major trust deficit. Therefore, organizations need to build customer friendly privacy models to enhance “Cyber-Security” and increase up business efficiency.