The Cloud Analytics Reference Architecture VP

8/13/2019 The Cloud Analytics Reference Architecture VP

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Harnessing Big Data to Solve Complex Problems:

The Cloud Analytics Reference Architecture


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Table of Contents

Introduction ....................................................................................................................... 1

Cloud Analytics Reference Architecture ............................................................................... 1

Using All the Data .............................................................................................................. 3

Better Questions and Answers ............................................................................................ 3

A Deliberate Approach to Unlocking the Promise of Big Data .................................................. 5

A Strong Foundation ........................................................................................................... 5

The Data Lake ................................................................................................................... 6

The Analytics .................................................................................................................... 7

Visualization and Interaction ............................................................................................. 10

Conclusion....................................................................................................................... 13


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Introduction

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.

Government and business organizations are

increasingly looking to big data to create new

opportunities and solve their most complex real-world

problems. They hope to tap into the many rich new

sources of information that are emergingfrom online

consumer behavior to social networking to the growinguse of electronic health records. At the same time,

organizations are building up immense databases of

their own, using rapid advances in cloud storage.

Despite this new wealth of information, the key to

unlocking its value seems to be missing. Organizations

are discovering that the size and diversity of big data

make it difficult to use in a meaningful way. They are

never able to explore all of the information at once, and

so are unable to track overall trends, or find the kinds

of larger, unexpected patterns that can lead to valuable

knowledge and insight.

The problem is that organizations are limited by

computing techniques developed long before big

data arrived on the scene. With these conventional

techniques, only narrow slices of data can be

accessed at any time. Datasets and analytics are

highly structured, and must be torn down and rebuilt

with each new line of inquiry. Information that does

not neatly fit into such rigid structuressuch as

Twitter and video feedsoften cannot be used. While

organizations are collecting more information thanever, the data tends to reside in silos that are difficult

to integrate. Cloud storage, despite its benefits, has

not eliminated the data silosit has simply made

them fatter.

In addition, few of the worlds IT systems are ready for

the technology revolution happening as organizationsseek to transform how they use data. As illustrated

in Exhibit 1, their infrastructures. face three major

challenges:

To help organizations overcome these hurdles and

prepare for whats next, Booz Allen has pioneered an

entirely new approach for the implementation of big

datain the digital enterprisea way of using technology,

machine-based analytics, and human-powered analysis

to create competitive and mission advantage.

This innovative approach, known as the Cloud Analytics

Reference Architecture, removes the conventional

constraints, enabling organizations to integrate all

of their available data, along with information from

multiple outside data sources. This powerful capability

makes it possible for organizations to find value, guide

strategy, and solve mission and business problems

long considered too complex.

Cloud Analytics Reference ArchitectureThe Cloud Analytics Reference Architecture has been

proven in high-stakes environments. It was developed

through/during an ongoing collaboration between

Booz Allen and the US government to leverage big

data in the search for terrorists and other threats.

Intelligence analysts are currently using the Cloud

Analytics Reference Architecture to integrate the

wide entire spectrum of intelligence sources, and

apply sophisticated analytical tools to find hidden

The Cloud Analytics Reference Architecture

Harnessing Big Data to Solve Complex Problems

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

Exhibit 1| Data Challenges in the Era of Big Data

Source: Booz Allen Hamilton


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connections and patterns. Similarly, the US military is

using the Cloud Analytics Reference Architecture to

provide information on insurgents and others who are

planting improvised explosive devices (IEDs) and other

bombs. The capability of the Cloud Analytics ReferenceArchitecture to analyze a vast array of disparate

data sources is providing military commanders with

unprecedented situational awareness. Commanders

have reported that the approach is saving lives.

In another example, Booz Allen and a large hospital

chain in the Midwest have demonstrated how the

Cloud Analytics Reference Architecture can also save

lives in medicine. By analyzing a large volume of

electronic health records, researchers have discovered

unexpected patterns over time in the vital signs offormer patients whose serious, often hospital-acquired

infections suddenly became life-threatening. Using

those insights, the hospital system has begun a

program to monitor current patients with infections,

watching whether their vital signs are following the

same patterns. This procedure is providing doctors with

an early warning that their patients conditions may be

deteriorating.

Booz Allen is now adapting the Cloud Analytics

Reference Architecture for the larger government and

business communities. This groundbreaking approach

can be applied to a broad range of critical problems,

such as:

Looking across large populations of internal and

external network users to identify those most likely

to steal information and commit fraud. The Cloud

Analytics Reference Architecture can achieve this

by integrating data from sources as varied as social

media sites, public records and even users patterns

of computer behavior.

Uncovering threats to the stability of the US financial

system, by discovering hidden patterns in the

combined data of an array of government regulators

and private financial institutions.

Exhibit 2| Booz Allens Cloud Analytics Reference Architecture


Streaming

Indexes

Human Insights and ActionsEnabled by customizable interfaces

and visualizations of the data

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

Services (SOA)

Analytics and

Discovery

Views and Indexes

Data Lake

Metadata Tagging

Data Sources

Infrastructure/Management

Visualization,

Reporting, Dashboards,and Query Interface


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Enabling two or more government investigative

agencies with a shared mission to integrate their

intelligence and create a common operating

picturewhile precisely adhering to the restrictions,

authorities and security issues pertaining to eachorganizations data.

The Cloud Analytics Reference Architecture represents

not an incremental step forward, but rather an entirely

new approachone specifically designed to solve

organizations real-world problems, and provide them

new opportunities, by harnessing the power of big data.

Using All the DataThe Cloud Analytics Reference Architecture takes

advantage of the immense storage ability of the cloud,but in a completely new way. An organizations repository

of information is no longer stored in rigid, regimented

data structures, but rather is consolidated in a vast

pool, or data lake. Every inquiry can make use of this

entire pool, along with information from multiple outside

data sourcesand it is all available at once. Users no

longer need to move from database to database, pulling

out specific information. And because there are no data

silos, there is no need to integrate them.

What results is not chaotic or overwhelming. Rather,

the rich diversity of information in the data lake

becomes a powerful force. The data lake is more than

a means of storageit is a medium expressly designed

to foster connections in data. And the Cloud Analytics

Reference Architecture explores those connections

to search for valuable correlations and patterns. This

actually reduces the complexity of big data, making it

manageable and useful, and creating efficiencies.

The crucial role of the data lake can be seen when

the Cloud Analytics Reference Architecture is viewed

in layers (see Exhibit 2). The data lake is supportedfrom below by the cloud storage infrastructure, and

in turn supports the computer analytics. All of these

elements support the final phase, the visualization and

interaction, where human insight and action take place.

Better Questions and AnswersWith the conventional approach, we do not really ask

questions of the datawe create hypotheses, and then

test the data to see whether we are right. In order to

pose these hypotheses, we have to guess in advance

what the answers might be, often a difficult proposition.

We also need to be familiar with the data we are

considering, including where it is (in what specific

datasets or databases), what format it is in, and even

to a large extent what the data itself contains.

That level of knowledge might be achievable when

we are working with a limited number of datasets

or databases, but not with the vast amounts of

information now becoming available to us. We often

have to put aside, or assume away, factors that wemight actually believe are critical. And so we end up

settling for marginal questions, and marginal answers.

Because the data lake removes the need for rigid data

structures, all of these constraints are removed. We no

longer need to pose hypotheses of defined data, and

so can ask more big-picture, intuitive questions.

The Cloud Analytics Reference Architecture also allows

us to more readily look for unexpected patternsit

lets the data talk to us, so to speak. While we can

look for patterns with the conventional approach, wecan only do so within our narrowly defined datasets

and databases, and we have to know in advance

what patterns we might be looking for. With the Cloud

Analytics Reference Architecture, we can discover

unexpected patterns that naturally emerge in the data.

This capability creates opportunities to predict the

future by looking at the past. Because the Cloud

Analytics Reference Architecture can store and analyze

vast amounts of information, organizations can look

for patterns in historical data, and see whether such

patterns are repeating today. This can, for example,

help regulators determine whether financial institutions

are repeating the mistakes of the past. And it can help

medical researchers look for patterns in the historical

heath records of thousands of previous patients, to

help treat patients today.


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A Deliberate Approach to Unlocking thePromise of Big DataBooz Allens Cloud Analytics Reference Architecture

provides a holistic approach to people, processes, and

technology in four tightly integrated layers, as depicted

in Exhibit 3. By design, these layers work seamlessly

together to:

Allow distributed storage and replication of bytes

across networks and hardware that are assumed

to fail at any time

Allow for massive, world-scale storage that separates

metadata from data

Support a write-once, sporadic append, read-many

usage structure

Store records of various sizes, from a few bytes up

to a few terabytes in size

Allow compute cycles to be easily moved to the data

store, instead of moving the data to a processer farm.

The Cloud Analytics Reference Architecture has an

inherent flexibility that enables organizations to pursue

new analytical approaches with few if any changes to

the underlying infrastructure. For example, the data

lake is easily expandable. Because it stores information

so efficiently, it can accommodate both the natural

growth of an organizations data, as well as the

addition of data from multiple outside sources. At the

same time, the Cloud Analytics Reference Architecture

replaces the current, custom-built analytic and

visualization tools with ones that can easily be adapted

for almost any number of inquiries.

A Strong FoundationWith the conventional approach, organizations must

continually reinvest in infrastructure as analytic needs

change. Building bridges between silos, for example,

typically requires reconfiguring and even expanding

Infrastructure

Data Management

Human Insights and Actions

Analytics and Services

Human Insights and Actions

Building on results and outputs from various analytical methods, multipledata visualizations can be created in your new cloud analytics solution.These are used to compose the interactive, real-time dashboard interfacesyour decision makers and analysts need to make sense of your data.

Analytics and ServicesBoth traditional and Big Data tools and software can operate on theinformation stored in your Data Lake, producing advanced specific analysis,modeling, testing, and simulations you need for decision making.

Data ManagementYour Data Lake is a secure, distributed repository of a wide variety ofdata sources. Security, metadata, and indexing of Big Data are enabledby distributed key value systems (NoSQL), but the Architecture allows for

traditional relational databases as well.

InfrastructureThis foundational layer allows for quick, streamlined, low-risk deploymentof the cloud implementation. The plug-and-play, vendor-neutral framework isunique to Booz Allen.

Exhibit 3| Layers within the Cloud Analytics Reference Architecture


LAYER 1

LAYER 2

LAYER 3

LAYER 4


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the infrastructure. With the Cloud Analytics Reference

Architecture, the infrastructure becomes a stable

platform to support all aspects of cloud computing.

With the top-to-bottom flexibility of the Cloud Analytics

Reference Architecture, organizations do not need tocontinually rebuild and reconfigure their infrastructure.

Their initial investment in infrastructure is both enduring

and cost-effective.

The Data LakeWith the conventional approach, the computer finds

information by looking in a particular database. With

the data lake, information is located in an entirely

different wayby tags, or details that have been

embedded in them for sorting and identification.

For example, an investors portfolio balance (the data)

is generally stored with identifying information such

as the name of the investor, the account number, one

or more dates, the location of the account, the types

of investments, the country the investor lives in, and

so on. This metadata is what gets tagged, and is

located by the computer during inquiries.

The tags themselves are also a way of gaining

knowledge from the data. In the example above,

the tags might allow us to look for, say, connections

between investors countries and their types of

investments. The basic datathe portfolio balancemight not even be part of the inquiry. Such connections

can be made with the conventional approach, but

only if the custom-built databases and computer

analytics have already been designed to take them into

consideration. As illustrated in Exhibit 4, with the data

lake, all of the data, metadata and identifying tags are

available for any inquiry or search for patterns. And,

such inquiries or searches can pivot off of any one of

those pieces of information. This greatly expands the

usability of the data available to an organization. It

actually makes big data even bigger.

In addition, the data lake smoothly accepts every type

of data, including unstructured datainformation that

has not been organized for inclusion in a data base. An

example might be the doctors and nurses notes that

accompany a patients electronic health records, or

information from social networking sites.

Exhibit 4| Data Management Architectural Model



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Two other critical emerging data types are batch

and streaming. Batch data is typically collected on

an automated basis and then delivered for analysis

en massefor example, the utility meter readings

from homes. Streaming data is information from acontinuous feed, such as video surveillance.

Much of the flood of big data is unstructured, batch

and streaming, and so it is essential that organizations

have the ability to make full use of all types. With the

data lake, there is no second-class or third-class data.

All of it, including structured, unstructured, batch and

streaming, is equally ingested into the data lake, and

available for every inquiry.

This mass of information is not random and chaotic,

but rather is purposeful. The data lake is like a viscousmedium that holds the data in place, and at the same

time fosters connections. Because the data is all in

one place, it is, in a sense, all connected.

As an example, cybersecurity experts trying to identify

internal and external network users most likely to steal

information and commit fraud might consolidate a

broad range of disparate information into a data lake.

In addition to unstructured information about individuals

from social media sites, the data lake could include

thousands of public records sources, from bankruptcyand criminal histories to trajectories of zip codes.

These might show out-of-the ordinary improvements or

declines in personal finances (possibly indicating that

an individual has committed fraud or is in dire straits

and may have motivation to do so). The data lake

might also include users computer behavior, enabling

the analytics to look for anomalies. Is the customer or

employee staying on the network far longer than usual,

or visiting new and different parts of the network, or

engaging in activities uncharacteristic of prior use?

With conventional methods, each potential datasource would have to be examined separatelyand

the results would be difficult to integrate. A data lake

would remove these constraints, making it possible

for the analytics to look for patterns and connections

in all of the available data at once, and to compile

sophisticated risk scores on every internal and external

user of the system.

The AnalyticsThe data lake supports a two-step process to analyze

the data. In the first step, the pre-analytical tools filter

and organize information from the data lake. That sets

the stage for computer analyticsin the next layer

upto search for valuable knowledge.

Extracting the Data

Pre-analytics use the metadata tags to locate

the relevant data from the data lake and give it

an underlying organization. For example, in the

collaboration between Booz Allen and the Midwest

hospital system, the electronic health records of

more than a thousand previous patients with serious

infections were ingested into a version of a data

lake. Special pre-analytics pulled out the patients

vital signs, and thenusing the time-and-date

stamps embedded in the recordsorganized them in

chronological order. That enabled analytics, in the next

step, to search for patterns in the way the patients

vital signs changed over time.

Although pre-analytical tools are commonly used inthe conventional approach, they are typically part of

the rigid structure that must be torn down and rebuilt

as inquiries change. Because such work is resource-

intensive, only a limited number of such tools can be

built, severely hampering an organizations ability to

make full use of its data. By contrast, the pre-analytics

in the Cloud Analytics Reference Architecture are

designed for use with the data lake, and so are not

part of a custom-built structure. They are both flexible

and reusable, giving organizations almost endless

windows into their data. Moreover, they are designed tobe interoperable from the moment they come online,

creating a set of easily shared services for all users of

the data.


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Finding Connections and Patterns

Once the data has been prepared by the pre-analytics,

the search for knowledge and insight can begin. As with

the other elements of the Cloud Analytics Reference

Architecture, computer analytics are used in an entirely

new way (see Exhibit 5). Two key types of analytics are:

Ad hoc queries. These are the analytics that ask

questions of the data. While in the conventional

approach the analytics are part of the narrow,

custom-built structure, here they are free to pursue

any line of inquiry.

Machine learning. This is the search for patterns.

Because all of the data is available at once, and

because there is no need to hypothesize in advance

what patterns might exist, these analytics can look

for patterns that emerge anywhere across the data.

Giving Computers More Work

A key feature of the Cloud Analytics Reference

Architecture is that it allows computers to take over

much of the work humans are doing now. Conventional

methods require that people play a large role in

processing the dataincluding selecting samples to be

analyzed, creating data structures, posing hypotheses,

and sifting through and refining results. That intense

level of effort may be workable for small amounts

of data, but no organization has the personnel or

resources to use such methods to process big data.

The Cloud Analytics Reference Architecture solves

this problem by giving a great deal of that work to

the computers, particularly tasks that are repetitiveand computationally intensive. This reduces human

error, and substantially speeds up the work. When we

use the Reference Architecture to pose more intuitive

questions, or to find patterns, we are essentially asking

Exhibit 5| Analytics and Services Architectural Model



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the computer to take us as close as it can to finding

the answers we want. It is then up to us, using our

cognitive skills, to find meaning in those answers.

By separating out what the computer can dothe

analyticsand what only people can dothe actual

analysisthe Cloud Analytics Reference Architecture

greatly eases the human workload. It is a division

of labor that frees subject matter experts to look at

the larger picture. At the same time, the Reference

Architecture rapidly highlights areas that analysts

should not waste their time exploringenabling them

to focus their resources in the right direction.

For example, agencies that investigate consumer

complaints against financial institutions often do not

know which individual complaints are indicative of abroader pattern of consumer abuse, and so deserve

the most attention. Investigators rarely have the time

to sort through the vast array of sources that might

provide valuable clues, such as blogs and social media

sites where consumers commonly air their grievances.

With a data lake that included all such available

information, the Reference Architectures analytics

could quickly identify patterns, such as consumer abuse

affecting large numbers of people. Investigators could

then focus their resources on the most serious cases.

Security of the Data

In its ability to integrate disparate data sources,

the Cloud Analytics Reference Architecture makes it

possible for organizations to easily share information,

confident that security, privacy and other rules

governing the data will be strictly maintained. With

the conventional approach, the primary obstacle to

information-sharing is not technology, but rather the

concern that secure information will be compromised.

Investigative agencies, for example, worry that

confidential sources will be inadvertently revealed.

Hospitals and doctors are concerned that patient

privacy will be violated.

Those concerns go away with the Cloud Analytics

Reference Architecture. As information is put into

the data lake, the relevant restrictions, authorities,

and security issues are tagged. All or portions of

documents are tagged as well, indicating the security

and privacy levels of specific information. Using these

tags, organizations can establish rules regarding which

information can be shared, with whom, and underwhat circumstances. If new information agreements

are instituted, organizations do not need to re-tag the

datathey simply change the rules regarding the tags

already in place.

The security of data in the Cloud Analytics Reference

Architecture has been proven to work in very secure

environments within the US government, where the

highest levels of precision in security and privacy

are required.

Visualization and InteractionDecision makers may be understandably concerned that

big data will be overwhelming, and lead to information

overload. Quite the opposite is true. The Cloud Analytics

Reference Architecture addresses the issue head-on

by incorporating the visualizationhow the knowledge

is presented to usinto the analytics from the outset.

That is, the analytics not only conduct the inquiries,

they help contextualize and focus the results.

This enables analysts to more easily make sense of the

information, to frame better, more intuitive inquiries,

and to gain deeper insights. Building the visualization

into the analytics has another advantageit provides

the ability for quick and effective feedback between the

two layers, so that the presentation of the findings can

be continually refined for the decision maker.

The visualization tools also make it possible for

different organizations to tailor how they see the same

data. For example, two agencies of the Department

of Homeland SecurityImmigration and Customs

Enforcement (ICE) and Customs and Border Protection(CBP)may want to visualize certain data in their

own way. ICE, which has an investigative focus, might

prefer that the visualization show how individuals are

connected to one another. CBP, which is interdiction

focused, may want the same data displayed

geographically and temporally, to understand where


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and when most activity occurs. The Cloud Analytics

Reference Architecture easily accommodates both

views of the dataand any number of others, as

illustrated in Exhibit 6.

Another important breakthrough is that analysts,

or subject matter experts, can explore the data

without the need for computer experts to serve as

intermediaries. Because of the high level of computer

expertise needed to design custom data storage

structures and analytics, much of the analysis in the

conventional approach is conducted by computer

scientists, computer engineers, and mathematicians

acting as agents for the subject matter experts.

They are typically the ones who translate the overall

goals of the business and government analysts into

the language of the machine. Whenever there is a

middleman in any field, things tend to get lost in the

translation, and data analysis is no exception. Here, it

leads to a disconnect between the people who need

knowledge and insight (the subject matter experts) and

the data itself. It also substantially slows the process.

In the top layers of the Cloud Analytics Reference

Architecture, the middleman syndrome disappears.

The ability to ask intuitive questions, and to look for

patterns, provides the analysts with direct access to

the data. That gives them the flexibility they need to

experiment and explore, and allows the system to reachmaximum velocity. The computer scientists, computer

engineers and mathematicians still play a key role, but

now are no longer the ones who drive the inquiries into

the data.

For example, investigators who suspect that credit

card fraud may be occurring are often hampered by

the need to go through computer experts to query the

data. Their request may be one of many, and by the

time they get back the information they need to act,

the criminals have often made large purchases on

the credit cards. With the Cloud Analytics Reference

Architecture, however, investigators could query the

data themselves, quickly pinpoint the fraud, and take

action in time to stop the activity. Subject matter

experts in other fields, such as financial analysts,

medical researchers and policy experts, can have

similar direct access to the data.

Exhibit 6| Human Insights and Actions Architectural Model



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With the Cloud Analytics Reference Architecture,

the flood of information is not overwhelmingit is

readied for action as never before. This breakthrough

in visualization could have as profound an effect on

decision making as bar graphs and pie charts did in the1950s and 1960s, when statistics became widely used

in business. Those visuals presented all the essential

information at a glance, changing the nature of decision

making. The Cloud Analytics Reference Architecture will

do the samebut this time with big data.

ConclusionThe opportunities offered by the Cloud Analytics

Reference Architecture will not emerge on their own

conscious effort and deliberate planning are needed.

Unless organizations make the right infrastructure

decisions, they cannot hope to build a data lake.

Unless they make the right data management

decisions, they will never break free from the rigid

data and analytic structures that are so limiting. The

Cloud Analytics Reference Architecture can be seen as

a road map for that decision making, one that shows

the importance of a holistic, rather than piecemeal,

haphazard approach. Each element is closely tied

to each of the other elements, and so all must be

considered together.

The Cloud Analytics Reference Architecture is no more

expensive to build than one based on the traditional

approach, and is considerably more cost-effective in the

long run. Because the elements of the Cloud Analytics

Reference Architecture are largely reusable, they canscale an organizations big data in an affordable way.

In this time of doing more with less, the Cloud

Analytics Reference Architecture enables organizations

to leverage the substantial investment the US

government has already made in this area. Many of

the same data challenges business and government

organizations currently face are being successfully

addressed by military and non-military agencies.

Organizations now have an opportunity to take

advantage of the advanced technologies and bestpractices that have led to that success.

It is impossible to harness big data with approaches and

techniques designed for small data. But by reimagining

how data can be stored, analyzed and visualized,

the Cloud Analytics Reference Architecture gives

organizations a powerful tool to solve their most complex

problems, and drive mission and business success.

See our ideas in action at boozallen.com/cloud.


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Booz Allen Hamilton has been at the forefront ofstrategy and technology consulting for nearly a century.

Today, Booz Allen Hamilton is a leading provider of

management and technology consulting services to

the US and international governments in defense,

intelligence, and civil sectors, and to major corporations,

institutions, and not-for-profit organizations. In the

commercial sector, the firm focuses on leveraging its

existing expertise for clients in the financial services,

healthcare, and energy markets, and to international

clients in the Middle East. Booz Allen Hamilton offers

clients deep functional knowledge spanning strategy andorganization, engineering and operations, technology,

and analyticswhich it combines with specialized

expertise in clients mission and domain areas to help

solve their toughest problems.

The firms management consulting heritage is the

basis for its unique collaborative culture and operating

model, enabling Booz Allen Hamilton to anticipate

needs and opportunities, rapidly deploy talent and

resources, and deliver enduring results. By combining

a consultants problem-solving orientation with deep

technical knowledge and strong execution, Booz

Allen Hamilton helps clients achieve success in their

most critical missionsas evidenced by the firms

many client relationships that span decades. Booz

Allen Hamilton helps shape thinking and prepare for

future developments in areas of national importance,

including cybersecurity, homeland security, healthcare,

and information technology.

Booz Allen Hamilton is headquartered in McLean,

Virginia, employs approximately 25,000 people, and had

revenue of $5.86 billion for 12 months ended March31, 2012. Fortunehas named Booz Allen Hamilton

one of its 100 Best Companies to Work For for eight

consecutive years. Working Motherhas ranked the firm

among its 100 Best Companies for Working Mothers

annually since 1999. More information is available at

www.boozallen.com. (NYSE: BAH)

About Booz Allen Hamilton

Contacts

Josh Sullivan, Ph.D.

Vice President

[email protected]

301-543-4611

Jason Escaravage

Principal

[email protected]

703-902-5635

Peter Guerra

Senior Associate

[email protected]

301-497-6754


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The most complete, recent list of offices and their addresses and telephone numbers can be found on

www.boozallen.com

Principal Offices

Huntsville, Alabama

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Los Angeles, California

San Diego, California

San Francisco, California

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Denver, Colorado

District of Columbia

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Pensacola, Florida

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Tampa, Florida

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The Cloud Analytics Reference Architecture VP

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