Multi Discipline Intelligence Production Teams 1

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http://www.data-tactics.com

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Future Concepts in Intelligence:

Multi-Discipline Intelligence Production Teams

Bruce Goldfeder, CSSLPSeptember 26, 2012

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• Data Deluge– Sensors and Sensor Data are increasing at exponential rates– Move beyond traditional sources of data– “The storm of data is 1500% heavier than it was just five years ago while our ability to

process, exploit and disseminate has increased about 30%” Gen Robert Kehler, USSTRATCOM, 2011

Paradigm Shift in the Intel Ecosystem

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Big Data Opportunities

Bruce Weed, IBM Corporation

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• $600 to buy a disk drive that can store all of the world’s music

• 5 billion mobile phones in use in 2010

• 30 billion pieces of content shared on Facebook every month

• 40% projected growth in global data generated per year vs. 5% growth in global IT spending

• 235 terabytes data collected by the US Library of Congress by April 2011

• 15 out of 17 sectors in the United States have more data stored per company than the US Library of Congress

Big data—a growing torrent

McKinsey, Big Data Report, 2011

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“There’s a method to solving data problems that avoids the big, heavyweight solution, and instead, concentrates building something quickly and iterating. Smart data scientists don’t just solve big, hard problems; they also have an instinct for making big problems small.

We call this Data Jujitsu: the art of using multiple data elements in clever ways to solve iterative problems that, when combined, solve a data problem that might otherwise be intractable.”

DJ Patil, Data Jujitsu: The Art of Turning Data into Product, 2012

Work Smartly with Data

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Knowledge Pyramid

Traditional Knowledge Pyramid Path to Actionable InsightsDr. Caron Kogan

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Technical Team Members

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• Intelligence teams tackling the hard problems– Senior members– Mixture of IT, Software, Statistics, and Intelligence

SMEs– Serve as the Vanguard for creating new • Processes• Actionable Data Products• IT Tools• Visualizations

Integrated Data Team

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• Disciplined methods of traditional data mining accelerated with iterative and rapid “what ifs”

• Requirement for unreasonable input – challenge existing truths to find new patterns

• Intimate knowledge of the mission problems that analytics or predictive analysis are addressing

• Ability to communicate findings using the customers language

• Original visualizations required to convey abstract and complicated results

Left and Right Brain

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New Visualizations

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• Integrated Team Supporting Theater Commander– Retired Special Operator– Social Scientist– Quantitative Mathematicians– Software Developer– Data Scientist– IT, Database, and UI personnel

DARPA Example

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Who Is Interested?• CJ-2/CJIOC-A

– Direction from BG Fogarty to support ATFC and Shafafiyat (Jan 2011)

• Afghan Threat Finance Cell– DEA-led fusion center, Treasury and DoD are

deputy leads– Active feedback loop with DEA Office of

Financial Operations• CJIATF-SHAFAFIYAT (BG McMaster)• NSA FTM Analysts

State of the Art• Highly manual analysis• No single agency has full picture• Technologies are limited

Threat Finance Analytics

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Automated tools for rapid analysis with massive multi-int

data

Threat Finance

CorruptionForeign Aid

Drug Economy

State Sponsorship

Capital FlightViolence

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Conduit Description Accts Interval Records Refine Stage

Shaheen Exchange (aka Central Accounts) Physical storefronts of the

exchange. Branches in and out of Afghanistan.

95 2001-2010~1.2 million

Two stages of data cleaning complete; third stage necessary

Shaheen Exchage Daily Balances ~1.8 million First stage of data clean in

progress

Hawala Accounts (aka “B” Computer)

Dubai-based hawala accounts, centrally maintained. 390 1998-2010 434, 401 Two stages of data

cleaning complete

Western Union Shaheen Exchange is a Western Union sub-agent. UNK 2001-2010 106, 176 Two stages of data

cleaning complete

“T” AccountsT accounts are debits, loans and payables to the Shaheen Exchange in Dubai

555 2000-2010 ~421,575 Initial specs and setup – high priority

“L” AccountsL accounts are bank accounts associated with the Sherkhan group of companies

86 2000-2010 ~337,260 Initial specs and setup – high priority

AFRATRecords and stores transactions of international exchange branch locations

UNK 2004-2010 Unknown Not started

ATFC Data

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80,000+ spreadsheets

Millions of records with variable structure

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• Toolset for faster DEA Shaheen Exchange data analysis• Geolocated 9 additional branches in AfPak region that DEA did

not know existed; and 45 overall worldwide• DEA work results from using our data

– Identified transactions with several banks in violation of OFAC sanction designation

– Known cash courier Mr. X (name classified) under investigation as a result

Accomplishments

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• Fast query of stacked large data sets with a user-friendly search and visualization tool

• Tools immediately put to use by DEA/ATFC in support of active and historical criminal cases

• Saved one 24/7 man-year of work that would have been spent simply scanning records

Data Resolution• Cleaned 14,538

spreadsheets – 20% of the data– Sheets prioritized by

user interest– Orders of magnitude

faster processing for threat finance analysis

• Resolved ~88k entities – 12% improvement

Accomplishments (cont.)

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Technique Rows Modified

Neighbor 55K – 52%

Ngram 10K – 9%

Metaphone 97K – 92%

Country: 947 -> 490Western Union: Original number of unique entries in the “country” field was

4.5 times the actual number of countries in

the world!

Proving capabilities to partners in theater and in CONUS has enabled trust

and data acquisition