Cloud Analytics Playbook

3
An enormous amount of valuable information is out there,
waiting to be transformed into differentiating services. Booz Allen
Hamilton uses its Cloud Analytics Reference Architecture to build
technology infrastructures that can withstand the weight of massive
datasets—and deliver the deep insights organizations need to
drive innovation.
1.0 | Summary
The problems of explosive data growth and how
cloud analytics provide the solution
page 4
2.0 | Differentiation
Introduces the Architecture and explains how
Booz Allen Hamilton’s unique approach to people,
processes, and technology gets the job done
page 10
3.0 | Depth
Takes the Architecture apart
layer by layer with detailed visuals that
show you how we frame a solution
page 16
4.0 | Successes
Presents real-world examples from the hundreds of organizations
who have successfully worked with Booz Allen Hamilton to
implement an analytics solution using the Architecture
page 31
1.0|Summary2.0|Differentiation3.0|Depth4.0|Successes
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Summary
1.0
A majority of executives believe their
companies are unprepared to leverage
their data. We look at why that is and
how to change it.
in this section

5
The Growing Data Analysis Gap
We are living in the greatest age of information discovery the world has ever known.
According to recent industry research, we now generate more data every 2 days than we did from the dawn of early civilization
through the year 2003 combined. And data rates are still growing—approximately 40% each year.
Fueled in large part by the more than five billion mobile phones in use around the globe, our world is increasingly measured,
instrumented, monitored, and automated in ways that generate incredible amounts of rich and complex data. Unfortunately,
the number of big data analysts and the capabilities of traditional tools aren’t keeping pace with this unprecedented data growth.
At Booz Allen Hamilton, we’ve watched this trend for some time now—we call it the “data analysis gap.” It’s clear that data has
outstripped common analytics tools and staffing levels. In order to move forward, organizations must be able to analyze data on a
massive scale and quickly use it to provide deeper insights, create new products, and differentiate their services.
2009 2020
quantity
sustainable challenging missed
opportunities
time
Data
Analysis
Gap
By 2020, the amount of information in our economy will grow 44 times.
Very few organizations are prepared for this wave of data.
44times
Extracting True Insights
(Source: IDC)
2.0|Differentiation3.0|Depth4.0|Successes
1.0|Summary

The ability to compete and win in the information economy will come from powerful analytics that draw insights and value from
data, and from high-fidelity visualizations that present those insights in impactful, intuitive ways. Both will become key
influencers of corporate decision making and consumer purchasing.
Many of the world’s IT systems are not ready for the technology revolution happening as organizations seek to transform how
they use data. Their infrastructures face three major challenges:
Preparing for
What’s Ahead
A Framework for the Future
Volume
Not enough storage capacity and
analytical capabilities to handle
massive volumes of data
Variety
Data comes in many different formats,
which can be difficult and expensive
to integrate
Velocity
Inability to process data in real time in
order to extract the most value from it
Booz Allen Hamilton has a framework for intelligently integrating cloud computing technology and advanced analytic
capabilities, called the Cloud Analytics Reference Architecture. The Architecture is designed to solve compute-intensive
problems that were previously out of reach for most organizations, including large-scale image processing, sensor data
correlation, social network analysis, encryption/decryption, data mining, simulations, and pattern recognition.
At the core of the Architecture are systems that accommodate petabytes of data at reasonable cost and allow analytics
to run at previously unattainable scales in reasonable amounts of time. However, human insights and action are still the
fundamental drivers.
The purpose of the Architecture is to allow machines to do 80% of the work—the mundane tasks they are best suited for—
and enable people to do the 20% of the work they do best, tasks that involve analysis and creativity.
To help organizations overcome these hurdles and prepare for what’s next, Booz Allen Hamilton has pioneered strategies for the
implementation of the Digital Enterprise—a way of using technology, machine-based analytics, and human-powered analysis to
create competitive and mission advantage.
▶▶ Improve overall performance
and efficiency
▶▶ Better understand customer and
employee needs
▶▶ Translate data into actionable
intelligence and faster decision
making
▶▶ Reduce IT costs
▶▶ Improve scalability to handle
future growth
However, before you invest in a
cloud analytics solution, you should
fully understand the scope of what’s
involved and engage in the proper
planning to ensure that all the right
elements will be in place.
Done right, analytics hosted in
the cloud will help:

The Transformative
Power of Cloud Analytics
Booz Allen Hamilton is the
leader in the emerging field
of cloud analytics. Our unique
approach combines cloud
and other technologies with
superior analytic tradecraft to
create breakthroughs in how
organizations capture, store,
correlate, pre-compute, and extract
value from large sets of data.
To understand the power of
cloud analytics, it helps to see
the progression from basic
data analytics performed in
most organizations today. As an
infrastructure is built out along
the continuum to cloud analytics,
the size and scale of data it can
process increases along with the
ability to drive performance and
improve decision making.
Basic data analysis usually
happens in core business
functions with smaller datasets.
Reports are usually created
on a “one-off” basis, limited
to distribution within a specific
department to support routine
decision making.
Analysis using standard cloud
computing solutions extends
basic analytic techniques to
large or very large datasets.
This is a logical entry point for
cloud solutions because cloud
technology is the most efficient,
cost-effective way to run analytics
on large amounts of data.
Advanced analytics is where
predictive capabilities are brought
into the mix. It’s generally used
to evaluate the future impact of
strategic decisions. However, it
represents a step back in terms
of the size of datasets that can
be manipulated.
Cloud analytics transcends
the limits of the other forms of
analysis. It delivers insights to
answer previously unanswerable
questions such as:
▶▶ How can we gain competitive
advantage in our market
space?
▶▶ Where can we save money
within our organization?
▶▶ How should we turn our data
into a product?
7
1.0|Summary

From Data
to Digital Enterprise
Booz Allen Hamilton clients
have a wide variety of data
analysis challenges and IT
infrastructures. Our flexible,
scalable Cloud Analytics
Reference Architecture has
three stages or entry points
to accommodate these
differences.
In each stage, we enable
shifts in technology
investments while helping
manage risk and maximize
the reward. That means
leveraging the assets you
already own and taking logical
steps to add what’s needed.
This is the only way to build a
structure to instrument data
so you can truly experience
breakthrough analytics.
1IT EFFICIENCIES
2SMART DATA
3CLOUD ANALYTICS
STAGE 1
The focus is on saving
money and reducing risk.
You may have already
begun some of these
initiatives; we leverage
what’s working now as we
discover new ways to
increase efficiency.
STAGE 2
We begin to modernize
applications to handle
the demands of advanced
analytics. Faster, reusable,
and more intuitive applications
will enable everyone in your
organization to work smarter.
STAGE 3
Significant improvements
in performance are realized
when you achieve success
in managing the flow of
information at scale and
derive the fullest value from
your data.
IT Maturity
▶ Data center consolidation
▶ Server and data consolidation
▶ Increased automation
▶ Modernized security
posture and metrics
▶ Reduced licensing costs
▶ Enhanced Enterprise
Data Architecture
▶ Clarify pedigree
(data tagging)
▶ Multidimensional indexing
▶ Adopt distributed database
▶ Reusable applications
▶ Create deep insight into
relevant mission data
at scale
▶ Ask and answer previously
unanswerable questions

A Better Approach
As the leaders in cloud analytics, Booz Allen Hamilton has a proven approach
delivered by some of the industry’s best talent. Here’s why we’re different:
Section 2.0
Differentiation: Introduces and diagrams
the Architecture, and explains how it
reflects Booz Allen Hamilton’s unique
approach. You’ll also read about our
core design principles, extensive service
offerings, and technology choices.
Pages 10–15
Section 3.0
Depth: Takes the Architecture apart layer
by layer with detailed visuals, design
concepts, and recommended solutions
from the cloud vendor landscape. The
section ends with a look at how security is
built into all levels. Pages 16–30
Section 4.0
Successes: Presents real-world
examples from our extensive file of case
studies. We present the solutions and
challenges, describe and diagram the
implementations, and explain the results.
Pages 31–35
A LOOK AHEADTechnical framework
Our Architecture combines the collective experience of thousands of people who have road tested
technologies from across the cloud solution landscape in hundreds of client organizations, ranging
from the U.S. Federal Government to commercial and international clients.
Best practices
We have an exclusive set of lessons learned and breadth of technical knowledge that saves time and
money while reducing risk.
Core principles
These are “rules of the road” we’ve developed to build the most effective solution with the highest
return on investment. They encompass everything from how data should be stored to how to improve
relationships with the end users of your data.
Critical skill sets
We bring technologists as architecture and solutions specialists, domain experts who know your
industry and your data, and data scientists who explore and examine data from disparate sources
and recommend how best to use it. No one else in the industry offers a better combination of talent.
Vendor neutrality
Our approach utilizes a broad ecosystem of products and custom systems culled from an exhaustive
survey of available options. In the crowded, fragmented, and continually evolving landscape of cloud
solutions, we recommend only the best fit and value for your organization.
1.0|Summary
9

Differentiation
Booz Allen Hamilton’s approach to
cloud analytics is unmatched in the
industry. Read about our unique
principles and best practices.
in this section
2.0

11
Streaming
Indexes
The Booz Allen Hamilton
Cloud Analytics Reference
Architecture incorporates a
wide range of services to move
from a technology infrastructure
with chaotic, distributed data
burdened by noise to large-
scale data processing and
analytics characterized by
speed, precision, security,
scalability, and cost efficiency.
However, Booz Allen Hamilton’s
approach is about much
more than infrastructure. We
start with your need to make
better sense and better use of
your mission data, and build
from there.
Human Insights and Actions
Enabled by customizable interfaces
and visualizations of the data
Services (SOA)
Analytics and
Discovery
Views and Indexes
Data Lake
Metadata Tagging
Data Sources
Infrastructure/
Management
Analytics and Services
Your tools for analysis, modeling,
testing, and simulations
Data Management
The single, secure repository
for all of your valuable data
Infrastructure
The technology platform for storing
and managing your data
Visualization,
Reporting,
Dashboards, and
Query Interface
A FRAMEWORK FOR SECURITY
Page 30 details our security processes
1.0|Summary3.0|Depth4.0|Successes
A Layered
Framework
2.0|Differentiation

cloud strategy and
economics
Delivery of strategy, technology, and economic
analysis for evaluating and planning all of the
business, technical, operational, and financial
aspects of a cloud transition
vdi deployment and
integration
Delivery of flexible and dynamic virtual desktop
infrastructure to simplify management, reduce
licensing costs, and increase desktop security
and data protection requirements
Cloud application migration
Expertise in the assessment, prioritization,
architectural mapping, re-engineering, and
optimization of workloads that have high value
and are ready for migration to the cloud
cloud security
Unified risk management approach to define cloud
security requirements, controls, and a continuous
monitoring framework to address data protection,
identity, privacy, regulatory, and compliance risks
data center migration
and optimization
Identify critical factors, design, and execute
the transformation of legacy IT systems to
virtualized and cloud computing environments
advanced cloud analytics
Delivery of scalable analytics platforms allowing
the processing of information at extreme scale;
and eDiscovery: high-volume, full text indexing,
and context-based search of information
software and platform
Expertise in the secure implementation of SaaS and
PaaS service delivery models, data migration, and
integration with existing enterprise infrastructure
and applications
infrastructure
Design and implementation of IaaS offerings
to provide global access to data storage,
computing, and networking services on
demand through self-service portals
Booz Allen Hamilton
Cloud Analytics Service Offerings

13
Data Management
Infrastructure
Data Integration
Complex Ecosystem
We’ll help you navigate the crowded, fragmented, and continually evolving
vendor ecosystem to design a best-of-breed solution for your organization.
2.0|Differentiation

Booz Allen Hamilton
Cloud Analytics Core Principles
In-situ processing
The Architecture demands that “you send the
question to the data,” because most big data
processes are disk I/O-bound. In-situ processing
means that most of the computation is done
locally to the data, so that analytics run faster.
This can enhance existing analytic capabilities
and/or allow you to ask entirely new types
of questions.
Data tagging
You can now afford to tag all of your data for
sensitivity or other controls (such as geographic).
This is the fastest, most reliable way to instrument
change across your entire Data Lake.
Use commodity hardware
Hardware should be expected to fail as the
normal condition. The Architecture supports
both scalability and fault tolerance to achieve
optimal application load balancing.
Economies of scale
What used to be called service-oriented
architecture (SOA) means that you can
define the value and cost of services in
your enterprise, and plan your development
actions, either to reduce the cost of low-value
components or increase the scale of high-
value components.
Schema on read
If you have all the source data indexed and query-
able, plus the ability to create aggregations, then
you can manage complex ontologies and demands
in a very efficient manner.
Change development process
In order to develop a tight, iterative relationship
with your end users, you can develop/research
a new capability in hours (not months), and the
process of discovery and integration with the rest
of the enterprise begins much sooner, too.
Throw away nothing
Near-linear scalable hardware and software
systems allow much more data to be stored,
which enable reprocessing of historical data
with new algorithms and correlations that
bring new insights.

15
Deeper
Insights
Analysts and Data
Scientists
▶▶ Create and use many views into the
same data
▶▶ Automatically find trends and outliers
▶▶ Evaluate analysis methods to determine
and enhance best-of-breed tradecraft
Developers and Data
Scientists
▶▶ No longer constrained by years-old
schemas
▶▶ Catalog and index the data that is
relevant today
▶▶ Free to create new views and
reporting metrics
▶▶ Reference undiscovered trends in
original data
▶▶ Apply advanced machine learning and
statistical methods
▶▶ In-situ hypothesis testing
System Administrators
and IT Staff
▶▶ Reduce IT costs through commoditization
and economies of scale
▶▶ Meet long-term scalability requirements
DecisionMakers,
Investigators, Interdictors,
AND Analysts
▶▶ Real-time alerting, situational awareness, and
dissemination specific to their clearance level
▶▶ Investigate and provide feedback
on reporting
▶▶ Interact and search using tailored tools
The Cloud Analytics Reference Architecture enables staff at all levels to quickly gather and act on granular insights
from all of your available data, regardless of its format or location. Below are some of the ways human insights and
actions are enhanced by this new framework, which fosters greater collaboration and teamwork, and, ultimately,
delivers the highest business value from your information and your computing infrastructure.
Analytics and Services Data Management InfrastructureHuman Insights and Actions
2.0|Differentiation

Depth
3.0
We diagram and describe each layer
of the Cloud Analytics Reference
Architecture, including our design
principles and technology choices.
in this section

17
Infrastructure
Data Management
Reference
Architecture
Booz Allen Hamilton’s Cloud
Analytics Reference Architecture
provides a holistic approach to
people, processes, and technology
in four tightly integrated layers.
Key Attributes
By design, the Booz Allen Hamilton
Cloud Analytics Reference
Architecture:
▶▶ Is reliable, allowing distributed
storage and replication of bytes
across networks and hardware
that is assumed to fail at any time
▶▶ Allows for massive, world-scale
storage that separates metadata
from data
▶▶ Supports a write-once,
sporadic append, read-many
usage structure
▶▶ Stores records of various sizes,
from a few bytes up to a few
terabytes in size
▶▶ Allows compute cycles to be
easily moved to the data store,
instead of moving the data to
a processer farm
Building on results and outputs from various analytical
methods, multiple data visualizations can be created
in your new cloud analytics solution. These are used
to compose the interactive, real-time dashboard
interfaces your decision-makers and analysts need to
make sense of your data.
Both traditional and “Big Data” tools and software
can operate on the information stored in your
Data Lake, producing advanced specific analysis,
modeling, testing, and simulations you need for
decision making.
Data Management
Your Data Lake is a secure, distributed repository of
a wide variety of data sources. Security, metadata,
and indexing of Big Data are enabled by distributed
key value systems (NoSQL), but the Architecture
allows for traditional relational databases as well.
Infrastructure
This foundational layer allows for quick, streamlined,
low-risk deployment of the cloud implementation.
The plug-and-play, vendor-neutral framework is
unique to Booz Allen Hamilton.
layer 1
layer 2
layer 3
layer 4
1.0|Summary2.0|Differentiation4.0|Successes
A FRAMEWORK FOR SECURITY
Page 30 details our security processes
3.0|Depth

Monitoring Exploratory
Geospatial
Line Chart
layer 1
architecture model

Human Insights and Actions(continued)
TECHNOLOGY EXAMPLES
HTML5, JavaScript,
OWF, Synapse
Commercial products
(Splunk, Pentaho, Datameer
Business Infographics, etc.)
Adobe Flex and Adobe Flash
Lightweight, custom web-based
applications and dashboards
tailored to specific user
communities or stakeholders for
data exploration, event alerting,
and monitoring, as well as
continuous quality improvement
Out-of-the-box, easy-to-build
dashboards for historical trending
and real-time monitoring to analyze
user transactions, customer
behavior, network patterns, security
threats, and fraudulent activity
Despite the rise of HTML5, Adobe
Flex and Flash applications still
remain strong candidates for
quickly building and deploying rich
user interfaces
In analytics solutions built on the Architecture, the data that’s available and the desired results drive the interfaces—
not the other way around. When user communities and stakeholders aren’t restricted by their tools, they can perform
complex visualizations to identify patterns they previously couldn’t see.
That freedom defines the guiding principles behind this first layer of the Architecture:
▶▶ Design and build the framework so that the desired data and analytic results define the visualization
▶▶ Reuse results and outputs of analytics across different visualizations
▶▶ Decouple the underlying analytics and data access from the visualizations and interfaces so that it’s possible
to build customized, interactive dashboard interfaces composed of dynamically linked visualizations
PRINCIPLES AND TECHNOLOGIES
3.0|Depth
19

layer 2
architecture model
Time Series Social Network
Analysis
R, SAS, Matlab,
Mathematica
MapReduce, Hive,
Pig, Hama

Frequently where data is concerned, the whole is greater than the sum of its parts. In the most strategic business
decisions, the ability to combine multiple types of analyses creates a holistic picture that can lead to much more
valuable insight. With the Cloud Analytics Reference Architecture, you can implement different
types of analytical methods.
This integrated approach is an anchor for the guiding principles behind our Analytics and Services layer:
▶▶ Allow both traditional and Big Data analysis tools and software to operate on a centralized repository of data
(the DataLake)
▶▶ Integrate results and outputs of analyses and visualize them on dashboards for decision making
▶▶ Decouple tools from the various types of analyses to make the system more extensible and adaptable
▶▶ Include a service-oriented architecture layer to reuse results and outputs in many different ways relevant to
different stakeholders and decisionmakers
▶▶ Incorporate Certified Catastrophe Risk Analysis (CCRA) to allow a variety of data analysis tools and software
to be integrated and used; it also enables results and outputs of analyses to be visualized and used across
multiple interfaces
TECHNOLOGY EXAMPLES
Data Mining
Machine Learning
Natural Language
Processing (NLP)
Network Analysis
Statistical Analysis
Data mining is used to discover
patterns in large datasets and draws
from multiple fields including artificial
intelligence, machine learning,
statistics, and database systems.
Machine learning is used to learn
classifiers and prediction models
in the absence of an expert and
employs many algorithms in the
areas of decision trees, association
learning, artificial neural networks,
inductive logic programming, support
vector machines, clustering, Bayesian
networks, genetic algorithms,
reinforcement learning, and
representation learning.
NLP is used to process unstructured
and semi-structured documents
for the purposes of information
retrieval, sentiment analysis,
statistical machine translation,
and classification.
Network analysis using graph
theory and social network
analysis are used to understand
association and relationships
between entities of interests.
Traditional statistical methods using
univariate and multivariate analysis
on relatively small datasets are
employed to make inferences, test
hypotheses, and summarize data.
Analytics and Services (continued)
3.0|Depth
21

discovering your data
layer 2
Technical framework
Discovery is intimately related to search and analysis. All three feed into insight in a nonlinear fashion.
A search-discovery-analytics process that solves business problems without consuming disproportionate
resources meets these user needs:
▶▶ Real-time, ad hoc access to content
▶▶ Aggressive prioritization based on importance to the user and the business
▶▶ Data-driven decision making, which relies on the ability to try different approaches and ideas in order
to discover previously unimagined insights
▶▶ Feedback/learning from the past intelligently applied to today’s data
Before working with Booz Allen Hamilton, most clients faced a
fundamental challenge with data discovery.They didn’t know what data
was actually available or how to sort through all of it to identify the most
important business problems or trends it could reveal.
Search
Analytics Discovery
How Booz Allen Hamilton simplifies discovery
Other solutions require analysts to break down data into numerous subsets and samples before it can
be digested. This expensive, time-consuming process is one of the major roadblocks to turning data into
true business intelligence.
Even though the Booz Allen Hamilton Cloud Analytics Reference Architecture supports the most
advanced analysis, it can also allow your staff to sift through all of your data on a basic level. Without
tedious or sophisticated sampling and complex tools, they can discover what’s useful and what’s not
useful for a specific business problem.
How does the Architecture support fast, efficient, and scalable search on entire datasets, not just
samples?
▶▶ Bulk and soft real-time indexing enable the solution to handle billions of records with subsecond
search and faceting
▶▶ Large-scale, cost-effective storage and processing capabilities accommodate “whole data”
consumption and analysis; in-memory caching of critical data ensures applications meet performance
requirements
▶▶ NLP and machine learning tools can scale to enhance discovery and analysis on very large datasets

HOW THE DATA SCIENCE LIFECYCLE DISTILLS INSIGHTS
Step 1
First, data is sampled using a cloud analytics platform. This step may involve a sophisticated analytic
that runs in the cloud, such as one that crawls a social network to find people with certain types of
relationships with an individual or organization. This sampling can be done using either high-level query
languages that are specially made for scalable cloud analytics or low-level developer interfaces.
Step 2
Next, a data scientist models the data sample in order to understand it better. This is usually done using
a statistical modeling environment on the data scientist’s workstation.
Step 3
Finally, once a trend is established using the model, the data scientist works with analysts and domain
experts to explain the trend and yield insights.
This cycle is repeated until the data science team reaches actionable insights and intelligence that
can be presented to senior leadership for decision making purposes. Information may be delivered in
a visualization, dashboard, or written report.
The data science lifecycle consists of three basic steps:
3.0|Depth
23

Data Management
StructuredBatch
Unstructured
layer 3
architecture model

A central feature of the Architecture, the Data Lake delivers on the promise of cloud analytics to offer previously hidden insights and
drive better decisions. It’s a secure repository for data of all types and origins. Instead of precategorizing data, which restricts its
usability from the moment it enters your organization, the Architecture combines unstructured, structured, and streaming data types
and makes them available for many different forms of analysis.
The following principles demonstrate how the Architecture enables your organization to use this repository of enterprise
data to the best advantage:
▶▶ Provide inherent replication of the data through a distributed file system
▶▶ Use distributed key value (NoSQL) data storage to enable security and metadata tagging at the data level
as well as indexing for specialized retrieval
▶▶ Relax schema constraints and provide the flexibility to adapt to changing data sources and types with the
schema-on-read approach of distributed key value data storage
▶▶ Store the Data Lake on commodity hardware and scale linearly in performance and storage
▶▶ Don’t presummarize or precategorize data
▶▶ Enable rapid ingest of data, aggressive indexing, and dynamic question-focused datasets through scale
TECHNOLOGY EXAMPLES
Hadoop Distributed File
System (HDFS)
Accumulo
Hbase, Cassandra, MongoDB
Neo4j
The primary open-source,
distributed storage system creates
multiple replicas of data blocks and
distributes them on compute nodes
throughout a cluster to enable
reliable, rapid computations.
NoSQL store based on Google’s
BigTable design features cell-
level security access labels
and a server-side programming
mechanism that can modify key/
value pairs at various points in
the data management process.
Open-source NoSQL databases
focused on a combination of
consistency, availability, and
partition tolerance.
NoSQL scalable graph database
storing data in nodes and the
relationships of a graph.
3.0|Depth
Data Management (continued)
3.0|Depth
25

DATA LAKE
Booz Allen Hamilton works with organizations in corporate and government sectors that have an
urgent need to make sense of volumes of data from diverse sources, including those that had been
inaccessible or extremely difficult to utilize, such as streams from social networks. Now analysts
and decisionmakers can form new connections between all of this data to uncover previously
hidden trends and relationships.
Intrusion and
malware detection
Enterprise
Data
Machine-to-
machine
communication
Transaction
logs
Sensor Data
Fraud Detection
healthcare
Email
Reports
Financials
Press Articles
Finance
Enhanced
Forecasting
Defense
Enhanced Situational
Awareness
Government
Regulatory Compliance
Cyber
Security Logs
Quarterly Filings
System Logs
Data Management (continued)
layer 3
Individual organizations require different types of data. Not all types of data listed above may apply to every organization.

27
Booz Allen Hamilton’s strategy and technology consultants are highly regarded subject matter experts. Through
groundbreaking conference keynotes, whiteboard talks, and papers, they help educate and shape the analytics industry.
We invite you and your team to take advantage of the educational resources listed below to gain strategic insights
about the use of analytics, explore technical topics in depth, and stay on top of the latest trends.
Presentations
Yahoo! Hadoop Summit: Biometric Databases and Hadoop
Invented and demonstrated methods for dense data correlation (e.g., imagery and
biometrics) within a Hadoop distributed computing platform using new machine learning
parallel methods.
Yahoo! Hadoop Summit: Culvert—A Robust Framework for Secondary Indexing of
Structured and Unstructured Data
Demonstration of Booz Allen Hamilton’s secondary indexing solutions and design patterns,
which support online index updates as well as a variation of the HIVE query language
over Accumulo and other BigTable-like databases to allow indexing one or more columns
in a table.
Slidecast: Hadoop World—Protein Alignment
Demonstration of advanced analytics in using protein alignment sequences to identify
disease markers using Hadoop, HBase, Accumulo, and novel machine learning concepts.
Slidecast: Innovative Cyber Defense with Cloud Analytics
Presentation on improving intelligence analysis through a hybrid cloud approach
to analytics, with descriptions and diagrams from Booz Allen Hamilton client solutions.
Slidecast: Integrating Tahoe with Hadoop’s MapReduce
Invented and demonstrated method to use least-authority encrypted file system as plugin
to HDFS within Hadoop cluster.
Papers
Massive Data Analytics in the Cloud
Overview of the business impact of cloud computing, and how data clouds are
shaping new advances in intelligence analysis.
Videos
Cloud Whiteboard Playlist
Short instructional videos on a range of topics from introductory talks for executives
to tutorials for data analysts. Check back frequently for new material.
Cloud Analytics for Executive Leadership
Booz Allen Hamilton Principal Josh Sullivan discusses how analysis of data can be used
as a tool to provide insight to executives.
Informed Decision Making: Sampling Techniques for Cloud Data
Booz Allen Hamilton Data Scientist Ed Kohlwey explains how sampling large amounts
of data can be useful for program managers to make informed decisions.
Developer Perspectives: The FuzzyTable Database
Booz Allen Hamilton Data Scientist Drew Farris explains how to use the FuzzyTable
biometrics database.
Workshop
O’Reilly Strata Conference: Beyond MapReduce—Getting Creative with Parallel Processing
Technical discussion of MapReduce as an excellent environment for some parallel
computing tasks and the many ways to use a cluster beyond MapReduce.
Enhanced Media Content
Learn More
Scan the QR code, or go directly to:
boozallen.com/cloud
3.0|Depth

Infrastructure
Virtual Desktop
Integration
layer 4
architecture model

Infrastructure is the foundation for any cloud implementation.
What makes the Booz Allen Hamilton Cloud Analytics Reference
Architecture unique is its plug-and-play, vendor-neutral framework.
This framework not only allows a greater range of choices in
selecting resources and building services, it also allows for a faster,
more streamlined, more secure, and lower risk deployment.
The following principles guide the infrastructure layer of
the Architecture:
▶▶ Make it easy to transform physical resources from legacy IT
systems to secure, virtualized data centers and trusted cloud
computing environments
▶▶ Implement core services to provide the mechanisms to realize
on-demand self-service, broad network access, resource pooling,
rapid elasticity, and measured service
▶▶ Employ virtualization to increase utilization of existing assets and
resources, and improve operational effectiveness
▶▶ Engineer in-depth security to provide controls and continuous
monitoring in order to fully address data protection, identity,
privacy, regulatory, and compliance risks
Infrastructure (continued)
TECHNOLOGY EXAMPLES
Amazon Web Services,
Microsoft Azure, Puppet,
VMware, vSphere
Security through VMware,
McAfee, Symantec, Cisco,
TripWire, EnCase
Cloud tool chain for provisioning,
configuration, orchestration, and
monitoring of virtual environment.
These tools provide the building
blocks for IaaS, PaaS, and foundation
for SaaS. Run multiple operating
systems and virtual network
platforms on the same hardware—
sharing computing, storage, and
networking resources.
Protect assets—physical, logical,
and virtual—while automating
governance and compliance.
3.0|Depth
29

Assets to Be Protected
Threats and Processes that Require Security
Organizational Security
Governance | Supply Chain | Strategic Partnerships
Geography
Distributed Sites | Remote Workers | Jurisdictions
Time Dependencies
Transaction Throughput | Lifetimes and Deadlines
Business
Business Layer
Business Attributes
Security Requirements to Support the Business
Control and Enablement Objectives
Resulting from Risk Assessment
Technical and Management Security Strategies
Trust Relationships
Security Domains, Boundaries, and Associations
Roles and Responsibilities
Time Dependencies
When Is Protection Relevant?
Conceptual
Business Layer
Business Information to Be Secured
Security and Risk
Security Entities
Security Services
Authentication Confidentiality and Integrity
Protection | Strategic Partnerships
Interrelationships
Attributes
Management Policy
Logical
Business Layer
Security-Related Data Structures
Tables | Messages | Pointers | Certificates | Signatures
Security Rules
Conditions | Practices | Procedures
Human Interface
Screen Formats | User Interaction
Access Control | Systems
Time Dependencies
Sequence of Processes and Sessions
Security Mechanisms
Encryption | Access Control | Digital Signatures
Security Infrastructure
Physical Layout of Hardware, Software, and
Communication Lines
Physical
Business Layer
Security IT Products
Risk Management
Tools for Monitoring and Reporting
Security Process
Tools, Standards, and Protocols
Time Dependencies
Time Schedules | Clocks | Timers and Interrupts
Personnel Management Tools
Identities | Roles | Functions | Access Controls | Lists
Locator Tools
Dynamic Inventory of Nodes | Addresses and Locations
Component
Business Layer
Service Delivery Management
Assurance of Operational Continuity
Operational Risk Management
Risk Assessment | Monitoring and Reporting
Management of Environment
Buildings | Sites | Platforms and Networks
Management Schedule
Security-Related | Calendar and Timetable
Management of Security Operations
Admin | Backups | Monitoring | Emergency Response
Personnel Management
Account Provisioning | User Support | Management
Service Management
a framework
for security
The Architecture is designed to
protect your data at rest and
in flight, with security controls
embedded in each layer.This
is obviously more than just
a technology challenge.We
understand the need to embed
new processes and training
regimens so your staff handles
sensitive data correctly.We also
advise you on how to secure your
facilities and ensure that all off-
premise facilities have the right
controls in place as well.
Reference Architecture
Security Framework

31
Successes
4.0
These case studies show how
Booz Allen Hamilton uses superior
technology and analytics expertise to
solve complex problems for clients
in a wide range of corporate and
government sectors.
in this section
Case Studies
4.0|Successes
1.0|Summary2.0|Differentiation3.0|Depth

Improving Intelligence Analysis
To fulfill their mission, this
organization requires data
correlation, quick access to
analytic results, ad-hoc queries,
advanced scalable analytics,
and real-time alerting.
To provide their analysts
with a continuous pipeline
of prioritized, actionable
information, they needed a
secure, scalable, automated
solution that would more
quickly and precisely sift
through large (and growing)
volumes of complex data
characterized by a variety of
formats and noise. In addition,
they needed to leverage their
existing analytics infrastructure
in the new platform.
Booz Allen Hamilton worked
closely with the client to adopt
a data cloud implementation
by augmenting the legacy
relational databases with cloud
computing and analytics. The
design focused on keeping
transactional-based queries in
the current relational databases,
while doing the “heavy lifting”
in the cloud and outputting
the interesting, processed, or
desired analytic results into
relational data stores for quick
transactional access.
With many existing systems
and applications dependent on
the legacy relational database
for transactional queries of
data, Booz Allen Hamilton
pulled together excess servers
from the client’s infrastructure
to build a hybrid cloud solution.
Also, 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.
Interfaces and Visualizations
Dashboards, web applications,
client applications, and rich
clients interfaced and integrated
with advanced analytics
infrastructure and legacy
relational databases through a
SOA business logic layer.
The solution called for
predictive analytics to forecast
potential events from existing
data and anomaly detection to
extract potentially significant
information and patterns. The
solution leveraged the core
principle of cloud analytics that
enables automated analysis
techniques, precomputation,
and aggressive indexing.
Data Management
The data sources had multiple
formats, were large in size,
and distressed with noise.
The solution created deep
insight through fusion of
different data types at scale.
The solution enabled the
ability to follow the lineage or
pedigree of the data, allowing
the client to map cost in
relation to the value of the data
or how well it is being used.
Infrastructure
The solution used Accumulo
(distributed key value
systems/NoSQL database)
for content normalization
and indexing, MapReduce as
the precomputation engine,
and HDFS for scalable ingest
and storage.
Rather than simply focus on gaining IT efficiencies by using cloud technology for infrastructure, Booz Allen Hamilton focused on applying cloud
analytics and in-depth understanding of the organization’s operational and mission needs to extract more value faster from massive datasets.
The new cloud solution provided immediate and striking improvements across the increasing volume of structured and unstructured data
using aggressive indexing techniques, on-demand analytics, and precomputed results for common analytics.
The final solution combined sophistication with scalability, moving the organization from a situation in which analysts stitched together sparse
bits of data to a platform for distilling real-time, actionable information from the full aggregation of data.
Impact
Case Studies Example
One
Mission Solutions

33
Planning and Responding to Disaster
This organization, which
is responsible for disaster
planning and response, found
that social media could provide
timely situational awareness for
biological (and other disaster)
events. They wanted a solution
to better characterize and
forecast emerging disaster
events using social media data
as it streams in real time. With
such a solution in place, the
organization could increase
overall preparedness by
leveraging event characterization
to accurately predict the impact
and improve the response.
In order to reach their goal,
the organization needed higher
levels of confidence in the
social media data on which they
would base their decisions.
The specific challenges the
new solution had to overcome
included data ingestion and
normalization, social media
vocabulary, social media
characterization, information
extract, and geographical
isolation of events.
Booz Allen Hamilton developed
a framework to capture,
normalize, and transform
open-source media used to
characterize and forecast
disaster events, in real time.
The framework incorporated
computational and analytical
approaches to turn the noise
from social media into valuable
information using algorithms
such as term frequency-inverse
document frequency (TF-IDF),
natural language processing
(NLP), and predictive modeling
to characterize and forecast
the numbers of sick, dead,
and hospitalized, as well as to
extract symptoms, geography,
and demographics for specific
illness events.
The solution framework
was implemented in the
cloud, taking advantage of
the flexible computational
power and storage. The new
cloud infrastructure allowed
Booz Allen Hamilton’s data
capturing and visualization
tool, Splunk, to mine through
and analyze vast amounts
of data in real time, while
outputting characterization
and forecasting metrics of
captured events.
The solution included
dashboards that characterized
events captured in social
media. The visual analyses
include event extraction counts,
time series counts, forecasting
counts, a symptom tag cloud,
and geographical isolation.
TF-IDF and NLP algorithms
were used to classify and
extract relevant information
from the data. Booz Allen
Hamilton developed predictive
models for forecasting event
frequency and counts. The
algorithms were written in
Python and incorporated into
Splunk located on Amazon Web
Services (AWS).
Data Management
The solution framework
captured live, streaming open-
source media such as Twitter
and RSS feeds. Data was
captured in Splunk and stored
on AWS.
The new Booz Allen Hamilton solution, which builds upon current best practices in cyber terrorism, enables near real-time situational awareness
through a standalone surveillance system that captures, transforms, and analyzes massive volumes of social media data. By leveraging social
media data and analytics for more timely and accurate disaster characterization, the organization is able to more effectively plan and respond.
Impact
Two
Mission Solutions
Geotagging Link Analysis
Predictive ModelingRisk Scoring
Provider
Profile
Financial Licensing Exclusion ClaimsGeolocation
Provider
Registration
Online
Activities
Cases/
Rulings
Clean,Validate,
Normalize, Integrate
4.0|Successes

Detecting Fraud and Abuse
U.S. Medicare and Medicaid
pay out approximately $750
billion each year to more than
1.5 million doctors, hospitals,
and medical suppliers. By
many estimates, about $65
billion a year is lost to fraud.
This organization needed to
be able to detect fraud in
claim data streams and stop
processing immediately; they
also wanted to assign a fraud
risk score to providers and
patient data in order to prioritize
their investigations. They were
challenged by multiple disparate
sources of data, including
valuable historic data archived in
currently inaccessible formats.
In addition, fraud and abuse
techniques are evolving
rapidly, as are policies and
technologies, so the final
solution could not lock them
into specific tools, data
sources, or approaches to
detection. Lastly, the solution
had to allow them to operate
in compliance with regulatory
requirements and laws
governing the use of personally
identifiable information.
Solution
Booz Allen Hamilton used a
variety of analytical techniques
and detection methods to
support the creation and
maintenance of tools that allow
organizations to stay ahead of
criminals. The solution for this
client integrates and combines
the best technologies and
analytics available to enable
the analysis of multiple data
sources. Booz Allen Hamilton
built systems for routine
detection that are designed to
accept new data sources and
techniques for detection. For
example, Booz Allen Hamilton
helped build a risk-scoring
algorithm that drew information
from multiple federal and civil
data sources. The risk-scoring
system is flexible enough to
allow analysts to build new
rules quickly, and the cloud
architecture can then accurately
rescore the entire population.
Users are given an interface to
monitor overall provider risk.
They can drill down into data
on each provider to get more
statistical information and
visualizations to gain insight
into specific risk factors and to
compose forecasts.
Geotagging, risk scoring, and
predictive modeling analysis
are applied to the data. Specific
predictive analyses include
neural nets, clustering, and
regression. A rule-based system
is also used to detect many of
the known kinds of fraud.
Data Management
The solution used a Data
Lake to store multiple sources
of data, including financial
data, provider registration,
geolocation, licensing,
exclusion, Medicare and
Medicaid claims, online
activities, and cases and
rulings. Identity matching could
be performed against third-
party background checks and
criminal, credit, and business
information. These different
data sources were
then cleaned, validated,
normalized, and integrated to
build provider profiles.
For the first time, doctors and others who want to bill Medicare are being assessed based on their risk to commit fraud. Those who
are deemed likely to commit fraud or have a record of investigations are rooted out. In addition, payers can better prioritize and target
investigations to prevent improper payment or to recover funds.
Mission
Impact
Logistic Regression Natural Language
Predictive ModelingCorrelation Analysis
Patient
Profile
Medication Diagnostic
Data
Medical
Notes
Electronic
Health
Records
Time
Series
Data
Hospital
Records
Package
of Care
Integration
Three

35
Predicting and Detecting Disease
Four
This organization is charged with
evaluating and measuring the
efficacy of hospital compliance
with SSC guidelines for
addressing Severe Sepsis and
Septic Shock (S4). They needed
to develop a new solution for
compliance analysis and early
detection analysis in order to
lower mortality rates and overall
health costs related to S4.
The final solution needed to
allow them to mine Electronic
Health Records (EHR) for clinical
indicators that could lead to
early detection of S4 and predict
the development of S4 from
sepsis. They also wanted to
enable hospitals to harness the
value of patient information to
diagnose more quickly, and use
this data to decrease the time
between official diagnosis and
implementation of the standard
of care.
Booz Allen Hamilton’s team
led a cross-company project,
Sepsis Intervention Outcomes
Research (SIOR), that tapped
analytical, clinical, economic,
and informatics expertise. SIOR
analyzed medical workers’
compliance with international
standards of care for S4, and
compared that compliance with
patient outcomes. Booz Allen
Hamilton’s advanced analytics
experts helped develop an
Event-Centric Ontology (ECO)
that incorporated NLP of
medical personnel notes.
ECO provided a formalized
vocabulary and framework for
evaluating EHRs that expedited
real-time discovery and
harnessing of structured and
unstructured data. Booz Allen
Hamilton also developed a
predictive model based on vital
measurements at critical times
to produce a risk score
for developing S4 from sepsis.
In addition, Mahalanobis
distance plots from baseline
showed that signals are
present before POD, which
allows for earlier detection of
at-risk patients.
The solution used logistic
regression, NLP, correlation,
and time-series analyses.
Data Management
Booz Allen Hamilton obtained
over 27,000 individual
patient EHRs for analysis
containing both structured and
unstructured data spanning
four hospitals and a period of
2 years.
Compliance analysis suggests a strong correlation between compliance to SSC guidelines and decreased mortality. Early detection analysis
indicates there may be a set of clinical indicators that could be used to identify patients at risk for developing S4, allowing their care to be
prioritized. Booz Allen Hamilton developed an analytically expedient framework that allows for more efficient computation and discovery of
underlying relationships, which can allow hospitals to expedite diagnosis and treatment and save more lives.
Mission Solution
Impact
Posts in the Past 12 Hours
2:00 AM
Wednesday
July 25 2012
Results
Results Results Results
Number of Mentions
Number of MentionsSparkline
4.0|Successes

MENA Operating Offices
Abu Dhabi • Cairo • Doha • Jeddah
Jubail • Kuwait City • Riyadh
+971-2-691-3600
www.boozallen.com/international
About Booz Allen Hamilton
Booz Allen Hamilton has been at the forefront of strategy
and technology consulting for nearly a century. Today, the firm
provides services to US and international governments in
defense, intelligence, and civil sectors, and to major corporations,
institutions, and not-for-profit organizations. Booz Allen Hamilton
offers clients deep functional knowledge spanning strategy
and organization, engineering and operations, technology, and
analytics—which it combines with specialized expertise in clients’
mission and domain areas to help solve their toughest problems.
Booz Allen Hamilton is headquartered in McLean, Virginia, employs
approximately 25,000 people, and had revenue of $5.86 billion
for the 12 months ended March 31, 2012. To learn more, visit
www.boozallen.com. (NYSE: BAH)
Version
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