Improving Intelligence Analysis Through Cloud Analytics

Improving Intelligence Analysis Through Cloud Analytics A Hybrid Cloud Approach to Enabling the Mission Ray Hensberger Lead Associate Systems DevelopmentThis document is confidential and is intended solely for the use and information of the client to whom it is addressed.

Table of contents• The Challenge• Our Solution• Cloud Analytic Techniques• Results & Applications• Helping Booz Allen’s Clients be Ready for What’s Next 2

Data Volumes Outstrip Analysis Capabilities Client Applications, Dashboards, Web Applications, Rich Clients • Complex data inputs • Variety of formats • Large volumes Service Oriented Architecture • Distressed with noise Business Logic • Client Mission Needs • Data correlation • Quick access to analytic results • Ad-hoc query • Advanced, scalable analyticsDisparate Data Disparate Data Disparate Data • Real-time alerting Sources Sources Sources 3

Our Solution: A Hybrid Cloud Approach• A U.S. Government client called on Booz Allen Hamilton to help improve mission performance while leveraging existing infrastructure• The client needed a secure, scalable, and automated solution that would more quickly and precisely sift through growing mountains of data, ensuring that an analysts’ pipeline of prioritized, actionable information would meet current and future needs• Booz Allen worked closely with the client to adopt a data cloud implementation by augmenting the legacy relational databases with cloud computing and analytics• With many existing systems and applications dependent on the legacy relational database for transactional queries of data, Booz Allen pulled together excess servers from the client’s infrastructure to build a hybrid cloud solution• The design focused on keeping transactional based queries in the current relational databases, but do the “heavy lifting” in the cloud, outputting the interesting, processed, or desired analytic results into relational data stores for quick transactional access 5

Hybrid Cloud Architecture Client Applications, Dashboards, Web Applications, Rich Clients Service Oriented Architecture Business LogicTransactional Queries Cloud Advanced Analytics Accumulo (NoSQL database) HDFS MapReduce Disparate Data Disparate Data Disparate Data Sources Sources Sources 6

Cloud Analytic Techniques• Rather than focus on gaining IT efficiencies using cloud technology for infrastructure, Booz Allen focused on applying cloud analytics and in- depth understanding of client operational and mission needs to extract more value faster from massive data sets• The solution called for advanced analytics, specifically predictive analytics to forecast potential events from existing data and anomaly detection to extract potentially significant information and patterns Service Oriented Architecture Business Logic Cloud Content Normalization Accumulo (NoSQL database) and Indexing HDFS MapReduce Pre-computation Engine 8 Scalable Ingest and Storage

Cloud Analytic Techniques• Our approach leverages the core principles of Cloud Analytics that enable: • Automated analysis techniques • Pre-computation and aggressive indexing • Answer previous unanswerable questions • Create deep insight through fusion of different data types at scale • Anomaly detection Service Oriented Architecture Business Logic  Data Correlation  Quick access to Cloud analytic results  Ad-hoc query Accumulo (NoSQL database)  Advanced Scalable Analytics  Real-time alerting HDFS MapReduce 9

A Scalable Solution with Significant Results • The new cloud solution provided immediate and striking improvements across the client’s increasing volume of structured and unstructured data using aggressive indexing techniques, on-demand analytics, and pre-computed results for common analytics • The final product combined sophistication with scalability to move from humans stitching together sparse bits of data to distilling real-time, actionable information from the aggregation of data • Storing large volumes of data in a data cloud provides the ability to follow the lineage or pedigree of the data (how good the data really is), allowing you to map cost versus how valuable is the data or how well is it being usedSystem Data Ingest Index Time Query (large) Query (small)Legacy 50 GB / day Minutes ~ 45 min SecondsHybrid 300+ GB / day Seconds < 4 min Milliseconds 11

Helping the U.S. Government Be Ready for What’s Next• The cloud analytics solution is now accessible throughout the client’s organization and is part of a larger set of advanced analytic solutions that Booz Allen is providing• As the client’s needs change to adapt to the mission, the solution is scalable and flexible to support future innovation and evolution without reengineering• The success of this project has led to additional federal organizations to express interest in adapting similar solutions for their environments• The demand for cloud analytic solutions will only grow in the future with the White House Office of Management and Budget seeking to improve policy and operational decisions, lower costs, and improve mission effectiveness across the federal government 13

Learn More about our Cloud Analytic Capabilities www.boozallen.com/analytics Ray Hensberger Lead Associate / Systems Development hensberger_raymond@bah.comThis document is confidential and is intended solely for the use and information of the client to whom it is addressed.

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Improving Intelligence Analysis Through Cloud Analytics

by Booz Allen Hamilton on Feb 09, 2012

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