Page 16 - GCN, Oct/Nov 2016

ADVANCED ANALYTICS
SPONSORED CONTENT
TURNING MACHINE DATA INTO
OPERATIONAL INTELLIGENCE
Machine data is the most valuable segment of big data analytics.
KEVIN DAVIS
VICE PRESIDENT,
PUBLIC SECTOR, SPLUNK
ADIGITAL REVOLUTION is underway that is changing every aspect of every mission and agency. It is shifting business models to online and mobile platforms, opening new opportunities
while introducing uncertain risks, and making agile and on-demand interactions the new normal. Gaining deep insights into patterns and behaviors— both machine and human—are imperative to navigating this transformation. Technology
leaders are looking at the promise of big data
and analytics for this purpose with the hope that endeavors in this realm would help them manage risks better, gain enhanced situational awareness, better understand constituent needs, ensure faster resolutions, and even foresee issues and prescribe remediations to ensure mission success.
At the core is machine data—the digital exhaust continuously created by all systems, technologies, users, and infrastructure at the center of this digital transformation. Machine data is generated when a security device senses an event, when a citizen accesses a website, when a warfighter uses his mobile device— literally from any digital activity. It has proven to be the most complex, yet most valuable, segment of big data.
Turning machine data into Operational Intelligence—real-time insights that provide an understanding of what’s transpiring across an agency, its activities, and the supporting infrastruc- ture—enables informed and confident decisions
for traversing this transformation successfully. The unprecedented visibility gained when machine data is collected, correlated, enriched, and analyzed has proven to resolve complex and tedious issues across an organization, from security to IT operations to mission challenges.
Big data attributes—volume, variety and velocity—require technologies that can match
them in terms of scale, ingestion, and agility. Given adaptability as one of its core attributes, machine learning has come to the forefront
in facing these challenges and winning favor as a preferred method. Machine learning
can analyze vast amounts of data much faster, helping separate the signal from the noise, leaving the cognitive requirements of inquisition and decision making to humans.
The more data it is fed, the more accurate the results. It allows organizations to quickly build models that mimic real-world scenarios and environments, create baselines of normal activities, set dynamic thresholds to account for acceptable variances, and quickly identify anomalies based on situational context. When machine data is enriched with other structured and unstructured information and overlaid with machine learning, organizations can optimize IT operations, enhance security, manage risk, detect and even anticipate incidents, and take action before any adverse impact.
The rising proclivity for big data and analytics is driving a panacea on the supply side. While point products may solve niche problems, most of them are not built to account for all the complex attributes of big data. Organizations would be well served
in considering a platform approach that is inherently built with these attributes in mind.
To derive value across the agency, leaders should ensure that the platform can manage data collection and associated flows, make ingestion easy without requiring explicit normalization, and provide visualizations that will help them make sense of the information quickly in the context of their objectives.
Kevin Davis is Vice President of Public Sector for Splunk.
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