and space in a way that enables the user to quickly make sense
of something, that is the power,” he declares.
The NGA has been doing this for some time, but on paper,
he continues. With mobile connectivity having spurred users
to want to be able to consume information digitally—and
even interact in real time with data, as opposed to static
products—the agency will “continue to tune” its online delivery without changing its underlying mission.
Ultimately, NGA big data would allow the agency to move
past persistent intelligence into a realm in which analysts and
decision makers “live within the data,” Bottom maintains.
“This means they are not interacting with products,” he
explains. “They are able to work with the data in real time.
The data is updated in real time, and the speed of understanding is going to be updated in real time as well.
“The goal of all of this is to give our decision makers more
time to make decisions,” he summarizes. “The more we can be
anticipatory as an intelligence community, the more time deci-
sion makers have to make decisions—whether at the policy-
maker level or warfighters or first responders.”
As the NGA is moving toward big data, it is focusing its
investments in several areas. One, Bottom says, is a tradecraft
known as structured observation management. With geospa-
tial intelligence, earth observation involves seeking changes
below. Each change represents an observation, Bottom notes,
with each observation representing an artificial or natural
object. The agency is taking a much more deliberate approach
in recording those observations instead of writing imagery
reports, and this leads to more “sense making” that strives to
explain why events are taking place.
One way the agency is addressing this goal is developing
analytics or algorithms to examine data first and then cue the
analyst as to which elements he or she might want to examine
immediately. One of the NGA’s key capabilities is the ability to
“tease out” bits of data in its large imagery files, Bottom maintains. This applies whether the data is earth observation, radar,
hyperspectral or LIDAR information, he says.
Structured observation management, activities-based
intelligence and other NGA initiatives such as next-generation collection are tied together by the concept of analytic models. These constitute a hypothesis of what analysts
expect to see; and while this concept is not new, what is
innovative is that these models would be machine-readable,
Bottom says. This will require coding them in a way that
permits automating the examination of these large data sets
to excerpt what is important, he explains.
“This is a continuous exercise,” Bottom points out. “So,
as things unfold, we are going to continue to tweak those
analytic models because there may be an outcome we didn’t
expect. We may learn something that may cause us to go
back and change or refine a model or come up with new
models. That will be the core in terms of our automation—to
actually figure out what is important.
“Of course, we always are going to continue to automate
the things that people are doing,” he continues. “The trick for
us is to make sure that, as we move from people in the loop,
it helps us become more efficient.”
Bottom says the structured observation management
tradecraft that the NGA is working to put in place has gen-
erated outcomes that have been “hugely impactful from a
mission perspective.” The adoption of the tradecraft itself
is a huge success story, he adds. The NGA has been able to
play a foundational role enabling the high-level directive for
intelligence integration with this tradecraft along with relat-
ed disciplines and tools, particularly those developed for
activities-based intelligence. Bottom offers that these efforts
constitute the fundamental underpinnings for the integrated
The NGA still has a way to go for achieving its goals in big
data, Bottom admits, offering that the agency is only about
20 percent along the path to big data effectiveness. One of the
key issues is figuring out exactly what the tradecraft will be, he
allows. The agency also must determine which investments to
make in tools and other capabilities, including human capital.
These developments occur on different time horizons, Bottom states, so success will not come at once. “We have a lot of
work to do in each of those areas,” he warrants, adding that the
agency is at least 12 to 16 months away from where he would
want it to be. Now that the NGA has its next-level strategy in
place, its progress toward big data should accelerate, he offers.
Skill sets remain the biggest challenge facing the NGA’s big
data efforts, Bottom declares. “[We need to] make sure we have
the right folks in the right places at the right time to move us
forward,” he says. His directorate is well-known for its technical and programmatic expertise, but those capabilities alone
will not be enough. The skill sets that the agency needs are in
areas such as data science, visualization and search capabilities.
Data science is at the core of big data enablers. Visualization is essential because, while the agency always has been
involved with visual information, the increasing amount of
data added to imagery products serves the user best if presented in a visual manner. For search, Bottom points out that
anyone can find information by Googling it online. The challenge is to make sense of those results, and achieving that will
require new skill sets.
Another challenge is not particular to the NGA: limited
resources. “Any investment we make not only has to deliver
mission value, it also has to make us less expensive,” Bottom
says. The need to increase capabilities with the same or fewer
resources has added a sense of urgency to its work.
Meeting both challenges will require trade-offs, Bottom
admits. The agency’s overall strategy outlines the areas
“The goal of all of this is to give our decision
makers more time to make decisions.”
—David Bottom, director of the information technology services directorate at the National Geospatial-Intelligence Agency (NGA)