Big firms rev up analytics adoption

A quarter of large global companies will have adopted big data analytics for at least one security or fraud detection use case, up from 8% today, according to Gartner.

Gartner VP Avivah Litan said in a blog post that this will achieve a positive return on investment – which is “typically too big to ignore -- within the first six months of implementation.

Litan said such enterprises can achieve significant savings in time and money when using big data analytics to stop crime and security infractions, by stopping losses and increasing productivity.

Big data analytics is applicable in many security and fraud use cases such as detection of advanced threats, insider threats and account takeover.

Information needed to uncover security events loses value over time, and timely intelligent data analysis is critical as criminals and bad actors move much more quickly to commit their crimes. Nowadays, hackers — aware of more-effective security and fraud prevention measures erected by their target victim enterprises — simply go directly to the theft without a drawn-out reconnaissance phase.

With big data analytics, enterprises can cut down on the noise and false alerts in existing monitoring systems by enriching them with contextual data and applying smarter analytics. This is especially important as the number of security events increase substantially year over year.

Also, organizations can correlate the resulting high-priority alerts across monitoring systems to detect patterns of abuse and fraud, and to get the big picture on the security state of the enterprise.

Companies can also pool their internal data and relevant external data into one logical place, and look for known patterns of security violations or fraud.

Further, firms can profile accounts, users or other entities, and look for anomalous transactions against those profiles.

Moreover, they can remain agile, and stay ahead of malicious actors and activities, via faster tuning of rules and models tested against data streaming in near real time.

Currently, big data analytics is ahead of most organizations' abilities to successfully adopt them. Enterprises are recommended to start small, but think big, and develop a road map that encompasses multiple use cases and applications across the organization.

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