This article explains a 10-year vision for the US Department of Homeland Security in regards to marshaling data.

1. Marshaling Data for Enterprise Insights
A 10-Year Vision for the US Department of Homeland Security

2. 1
As owners of one of the most comprehensive data
environments in the US federal system, the US
Department of Homeland Security (DHS) has an
asset like no other for conducting its basic mission
of safeguarding America’s citizens, infrastructure,
and borders. The information it collects is as diverse
as the missions of the many agencies and offices
under its purview, ranging from the Transportation
Security Administration (TSA) to the Federal Emergency
Management Agency (FEMA), US Customs and
Border Protection (CPB), Immigration and Customs
Enforcement (ICE), the US Secret Service, and the
Coast Guard.
Because the mission of DHS is so broad and all
consuming, it is challenged with ushering in the kinds
of changes that its data regime needs to render new
insights and drive down costs. Each of the extensive
data sets that DHS and its components maintain
supports specific mission needs and exists under
established legal and regulatory authorities. Their
owners naturally strive to improve the efficiency and
effectiveness of data analysis for their intended
purposes. But even when users recognize the mission
benefits of sharing data and conducting analysis
across multiple data sets, they too often run into
technical, procedural, legal, and cultural barriers to
shared analysis.
These barriers are preventing DHS from realizing
the full potential of the data it has as its disposal.
Intelligence and law enforcement analysts searching for
terrorists, and other threats, need the ability to paint a
comprehensive picture that considers all kinds of data
at once—such as travel and entry and exit records,
evidence that ties suspected terrorists or criminals to
illicit shipments of cash or contraband, or involvement
of specific individuals or groups in diverse criminal
activities, from weapons trafficking to cyber crime.
The need to learn from and capitalize on multiple
sources of data is no less urgent for DHS teams
responsible for responding to emergencies, enforcing
trade agreements, combating transnational criminal
organizations, managing the flow of people through
US borders, protecting intellectual property rights, and
safeguarding America’s critical infrastructure.
Over the coming years, DHS will need to master
the ability to marshal its tremendous repository of
data while still living up to its manifold day-to-day
responsibilities. These tasks are mutually dependent—
the bridge must be rebuilt while remaining open to
traffic. We believe it will be easier for DHS to manage
its transformation into a data-enabled enterprise if
it attacks the problem from a mission perspective
and taps emerging cloud-based analytics to gain new
insights from the information it already has at its
disposal. Through this effort, DHS can significantly
elevate its role in the nation’s homeland security
ecosystem, position itself as a valuable and
insight-driven partner to other crime enforcement
agencies, and increase the efficiency of its
mission-support functions.
Opening the Aperture
When Michael Chertoff became the second secretary
of homeland security in 2005, one of his first priorities
was to change a departmental policy of only taking two
fingerprints from each person either wanting a visa to
come into the country or for those traveling without
visas. Chertoff reasoned that by only taking two
fingerprints—one from each index finger—homeland
security personnel were potentially missing scores of
Marshaling Data for Enterprise Insights
A 10-Year Vision for the US Department of Homeland Security

3. 2
latent fingerprints, or the fingerprint residue collected
all over the world at crime scenes, in safehouses
where terrorists plan, and even on battlefields. By
expanding the information collected from travelers to
10 fingerprints, Chertoff said that the DHS significantly
enhanced its capability to identify those who weren’t
included on watch lists but “left a little piece of
themselves somewhere and some place that suggests
we ought to take a closer look.”1
Chertoff’s policy change was predicated on the belief
that information is power. By opening the department’s
aperture to collect more information, he reasoned that
the department was strengthening its powers to detect
bad actors before they performed bad deeds.
This premise is at the heart of all homeland security
efforts, and yet, there are real and significant
constraints to expanding the amount of pertinent data
that each DHS entity has at its disposal. The most
significant constraint is rooted in the department’s
origins—bringing 22 different agencies under one
organization necessarily meant bringing their legacy
information technology systems with them. While
considerable progress has been made in improving
these systems over the last 10 years, too little has
been done to address the challenges of sharing data
and conducting analysis across these systems. As
a result, DHS has limited the usefulness of its data.
Users have to mine a number of databases to find
answers to problems identified at the policy level
or pursue particular cases, and these efforts have
historically been susceptible to redundancy and lack of
coordination. In addition, the people operating at the
collection end of those stovepipes have little incentive
to acquire information that isn’t directly relevant to
their own priorities, even though it might be very
important to others.
But the issue also goes to a lack of awareness that
data exist in the first place. In the homeland security
setting, the real power of data is not limited to
helping you solve problems identified elsewhere—it’s
identifying new problems so you can get ahead of
them. Information sharing relies on the owner of that
information to recognize its value to others, and this is
very seldom the case. As a result of this shortcoming,
information just stays where it is, where it has no value
at all. It’s as if 10 different DHS entities were each
collecting a print sample from a different finger, but
nobody could ever put them together because there
was no mechanism for matching them up.
These organizational and other barriers are particularly
problematic for homeland security leaders trying to
secure the nation’s borders, keep ahead of criminal
organizations, enforce and facilitate trade and travel,
and plan for emergency response. For instance,
Customs and Border Protection has developed
reasonably effective processes for physically and
virtually screening cargo shipments by analyzing trade
data supplied by shippers and other information on the
flow of goods into and out of ports of entry. But it is
difficult to link that data to information on individuals
who may be involved in those shipments, the past
1 Remarks by Michael Chertoff, www.tsa.gov/press/happenings/2007_chertoff_remarks.
shtm

4. 3
histories of the conveyances used in the shipments,
and the money and data flows that accompany all
movement of goods—whether legal or not.
Part of this difficulty stems from the fact that different
agencies relying on different databases are responsible
for different aspects of securing and facilitating the
movement of people, goods, money, and data. A data
point as plain as a phone number on a bill of lading
could be associated with a known terrorist, but it
won’t be flagged for follow-up if nobody inside CBP
understands its value and those outside of CBP remain
unaware that this information is even being tracked.
Only by revealing the presence of data in the first place
can DHS and its partners expect its analysts at the
edge to make these critical connections.
More Tooth, Less Tail
DHS faces the added challenge of evolving into a data-
centric organization at a time when every cost is being
scrutinized. While mission challenges remain high, the
department is facing budget constraints unprecedented
in its 10-year history.
As in the military, concern is growing that the “tail” of
back-office functions has grown too big and unwieldy
to support DHS’s mission in a timely and cost-effective
manner. One of the biggest complaints coming from
inside the organization is that staffers have to supply
the same data to five or six different entities. That
level of duplication is expensive and ties up valuable
resources. This spending seems particularly out
of synch when analysts working at the edge of the
enterprise have a limited view of the information the
back-office support groups are helping to maintain.
Our view is that DHS has the opportunity to become
a leaner, more productive organization in the future if
it harnesses the power of the data it already collects
today to support its many mission objectives. In
10 years' time, we see DHS components thinking
of themselves as collective sources and sharers of
information rather than separate parts of a larger
bureaucracy. DHS must tap the potential of its data
much in the way that retailers, banks, and healthcare
providers of today are tapping every available piece
of information about their customers to predict
their behavior and help set strategy. Investigators,
inspectors, and analysts in the field will know
what kinds of data are being collected across the
organization, so they know where to go to mine for
information and learn more about the cases they are
pursuing. Ultimately, this capability will be extended
from mission data to mission-support activities to help
DHS more efficiently manage its personnel, property,
fixed assets, and finances.
Mining Big Data for Insights
The first step in developing this capability and
transforming DHS into a data-enabled enterprise is
to embrace a new method of keeping track of and
accessing information—one specifically designed
for big data that is distributed in a classified or
law enforcement environment. The emergence of
cloud computing technologies has paved the way
for organizations to unlock new insights by coupling
technological infrastructure with analytical tools to
gain better access to all of the data they have at
their disposal.
To support this new capability, DHS must have a
full and working knowledge of all the types of data
its many entities collect on a regular basis. As it
stands now, a DHS analyst is only able to locate the
information he or she needs if they know precisely
where it is housed—in one database or another. But
DHS’s experience reveals that this is seldom the case;
users move from database to database, searching for
specific information that may or may not be there. If
users want to ask different kinds of questions, they
often have to reengineer both the databases and the
analytics involved—a process that can be prohibitively
long and expensive. This tends to limit both the
complexity of the questions that are asked and the
utility of the results.

5. 4
What DHS needs to derive actionable insights from
its data is to evolve toward a platform where the data
can be staged for advanced analytics. Cloud-based
technology solutions now avail organizations with an
effective and efficient way to load, store, and access
multiple data sources to enable multivariate analytics
in a mission-specific setting. The evolution will
systematically convert the DHS’s collection of individual
databases into a consolidated and connected pool of
information—or “data lake”—making all of it readily
accessible for all types of analysis.
With the data lake, users can easily tap all of the
available data in a variety of constantly changing ways.
A key feature of the data lake is that information is no
longer defined strictly by its location. Specific pieces
of supporting information—known as meta-data, or
“data about the data”—are identified by embedded
“tags” that allow for sorting and identification. In the
shipping container example illustrated above, the
user could find the phone number listed on the bill of
lading simply by searching for it, regardless of which
DHS agency captured the information. The process of
tagging information is not new—it is commonly done
within specific datasets or databases. What is new is
using the cloud in combination with the technique to
make it available to a wide array of users. This is an
important distinction, as an important advantage of the
data lake is there is no need to build, tear down, and
rebuild rigid data structures.
The most transformative aspect of a cloud analytics
reference architecture that incorporates a data lake is
that users do not need to have the possible answers
in mind when they ask questions. Instead, they can
let the data talk to them. The ability to make complex
inquiries, easily switching in and out any number of
variables, allows users to look for patterns, and then
follow them wherever they may lead. This is particularly
important in predictive analytics, when people may not
know exactly what they are seeking. This capability
strengthens the power of inquiry by empowering
data to help reveal new or broader questions and
correlations, expanding insight into scenarios and
challenges. Importantly, a data lake also helps draw
from unstructured and streaming data, which exist
in forms that cannot be directly placed in structured
databases and data sets.
Our prior work with another intelligence-driven
organization shows that a data lake can be employed
even when classified information is at issue by putting
appropriate levels of security and authorization in
place. The solution called for predictive analytics
to use existing data to forecast potential events
and detect anomalies in order to extract potentially
significant information and patterns. Our design
focused on keeping transactional-based queries in the
current relational databases, while doing the “heavy
lifting” in the cloud and outputting the results into
relational data stores for quick access.
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 pre-
computed results for common analytics. By combining
sophistication with scalability, the solution helped move
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.
Many such opportunities now exist in Homeland
Security. For example, the National Cybersecurity and
Communications Integration Center (NCCIC) faces the
challenge of making full use of the numerous data
feeds it receives from a variety of government, private-
sector, and other organizations. Because much of the
data is unstructured, or resides in discrete data sets
that are difficult to connect, NCCIC may get only a
partial, delayed picture of cyber and communications
incidents. Booz Allen Hamilton’s Cloud-based Services
would enable NCCIC to consolidate all its available
data—of every type, from all sources—and then
automatically search through its entirety for important
correlations and patterns. With this ability to put all the

6. 5
pieces together and see the full picture, NCCIC could
better respond to incidents in real time, and provide
more accurate situational awareness to stakeholders.
In another example, Cloud-based Services would
enable DHS and the Department of Justice (DOJ) to
integrate the intelligence they gather in their shared
Southwest border mission. Currently, the critical ability
of the two agencies to gain a common operating
picture is hampered not just by the wide range of
structured and unstructured data collected, but also
by the various restrictions, authorities and security
issues around storing, managing and accessing that
data. Through Cloud-based Services, DHS and DOJ can
create a common operating picture while maintaining
the different sets of rules for the data—all in one
system, rather than in many. This ultimately frees the
mission operators and the IT organizations supporting
them from the need for proprietary solutions, and from
the resource-depleting operations and maintenance
costs tied to those solutions.
Cloud-based Services would also allow DHS to break
through major cost barriers with a shared data
environment for the department’s component agencies.
Currently, agencies often share space at data centers.
Cloud-based Services takes a major step forward by
creating an environment in which the agencies share
a common data pool, as well as reusable analytic and
visualization software that taps into the pool. What
occurs is a decoupling of the data from the software,
substantially driving down costs and creating previously
unobtainable economies of scale.
Oversight and Backing
Of course, DHS will need to maintain adequate
governance of its data to help decide which information
is included in the data lake and who is authorized to
access it. To this end, DHS will need a dedicated team
to provide this level of governance. Many commercial
enterprises have turned to a chief data officer (CDO)
to serve this function and increase the transparency
of their data. This idea, or something like it, could
serve DHS well. The CDO and their staff should not
be confused with a chief information officer or chief
technology officer. Where these other two functions
provide general infrastructure support to the rest of the
organization in a commercial setting, CDOs work much
more closely with the lines of business to tap data for
specific objectives.
Financial services firms, among the first to create such
a position, have turned to CDOs to drive their data
management strategy and establish a data governance
structure to determine who owns and manages the
information. Their experiences could be particularly
instructive in a homeland security setting, given the
vast array of data they collect, as well as their need to
safeguard sensitive information.
Indeed, this last consideration has proved problematic
for DHS in the past; it’s easier to protect information
when security is at stake than risk exposing it to
someone without the proper authorization. A critical
part of the CDO’s role would be to define the
parameters for data ownership and stewardship. The
CDO would also help determine legal restrictions on
the use of data. When certain data are combined, it
can create a collision of legal authorities. On its own,
one piece of information may not be classified or law
enforcement sensitive, but combining that information
with other data may yield insights that need to be
protected for national security or prosecutorial reasons.
A strong governance function may also be influential
in securing support from outside the organization.
Whether the solution takes the form of a data lake
or some other vehicle, it likely won’t go far without
a legislative mandate. The legacy structure of its
seven operating components and other agencies has
supported a view that information is a commodity
that must be protected, and only disseminated on
a need-to-know basis. The agency will also face the
reality of convincing incoming political leaders to make
short-term investments for long-term gains. Change

7. 6
management is tough enough for private companies—it
is that much more difficult for federal agencies whose
senior leaders are in a state of constant flux. Even
the best-laid plans can get disrupted or derailed when
those who call the shots inevitably change.
Without a legislative or statutory mandate to serve as
a forcing event, DHS will not likely be able to foment
the degree of change that is needed—at least not at
an acceptable rate. In some ways, the position DHS
finds itself in now is not dissimilar to that faced by
the US Treasury Department in the aftermath of the
credit crisis. At the time, it was clear that the financial
services industry had major gaps in trying to get its
hands around all of the risks being taken with the
advent of credit default swaps and other advanced
financial derivatives. The Treasury Department
needed financial data that was actionable and
would support decision-making at the highest
levels within the department.
To help fill these gaps, Congress created an Office
of Financial Research (OFR) within the Treasury
Department through the Wall Street Reform and
Consumer Protection Act of 2010 (“Dodd-Frank”).
The law created two units within OFR—a data center
and a research and analysis center—to continually
gather up and analyze detailed financial information
collected from a variety of banks and other financial
firms. One of its first steps was to issue a request for
proposals on an industry utility to create and generate
identification codes to help it obtain more granular
data on counterparty risk, long and short positions,
collateral and collateral calls, and derivative positions.
Given the complexity involved with the effort, and the
reliance on private sector contributions, OFR would
not have been possible were it not provided for in
statute. It may be too early to judge whether OFR will
be a success or not, but it does show the power of
a mandate to force organizational change where the
availability of information is restricted.
Conclusion: A Data-Driven Future
The political feasibility of winning such a mandate for
a data-driven approach at DHS is an open question.
To win support for such a mandate, it will be critical
that DHS and its components present the opportunity
to be more effective and cost-efficient in real terms.
DHS will continue to collect and store data regardless
of whether such a mandate exists. The cost of those
efforts will not go away. The question is whether DHS
is empowered to make better use of those resources
by increasing its awareness of the information that
exists. After all, you can’t fight an enemy you don’t
know. And, creating new channels to share information
only works if you know what information you have at
the ready.
The enterprise insights that come from these early
efforts will likely produce some quick victories and
attract the necessary buy-in to build incrementally
from there and start developing a data repository with
actual answers—not just questions. By thinking big,
acting small, and going methodically step-by-step, DHS
has the capability to lead where others continue to
squabble. Information is the key to this transformation.
Treated as a commodity, it will continue to be
stockpiled and stored away. Viewed as an asset,
it will strengthen the nation’s homeland security
enterprise and help DHS deliver on its many
far-reaching mandates.
Contacts
Suzanne Storc
Principal
storc_suzanne@bah.com
Mike Delurey
Principal
delurey_mike@bah.com

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