1. The document discusses how big data and new technologies are changing intelligence collection and analysis. 2. It outlines four "big bets" the agency is making to revolutionize big data exploitation, accelerate operational excellence, support the intelligence community, and drive performance through talent management. 3. Six key technology enablers are also discussed including secure mobility, advanced analytics, customization tools, security services, data storage, and cloud computing.

1. 1

2. Beyond Big Data
Riding the
Technology Wave
Ira A. (Gus) Hunt
!
Chief Technology Officer

3. Our Mission
We are the nation's first line of defense. We accomplish
what others cannot accomplish and go where others
cannot go. We carry out our mission by:
Collecting information that reveals the plans, intentions and
capabilities of our adversaries and provides the basis for
decision and action.
Producing timely analysis that provides insight, warning and
opportunity to the President and decisionmakers charged with
protecting and advancing America's interests.
Conducting covert action at the direction of the President to
preempt threats or achieve US policy objectives.

4. 4 Big Bets
1
Revolutionize Big Data Exploitation
– Acquire, federate, secure and exploit. Grow the haystack, magnify the needles.
2
Accelerate Operational Excellence
– Innovate IT operations and run IT like a business.
3
Serve CIA by supporting the IC
– Assume a leadership role in IC activities that matter to CIA; Build to share
4
Drive Performance through Talent Management
– Focus on continuous learning and diversity of thought, experience, background

5. 6 Key Technology Enablers
0
Secure Mobility
– Immediate, secure and appropriate access to people, data and tools from anywhere at anytime
1
Advanced Mission Analytics—Analytics as a Service
– World-class abilities to discover patterns, correlate information, understand plans and intentions,
and find and identify operational targets in a sea of data. Big Data analytics as a service
2
Enterprise Widgets and Services
– A customizable, integrated and adaptive webtop that lets analysts, ops officers, and
targeters to “have it their way”. Personalization in context.
3
Security as a Service
– One environment, all data, protected and secure.--ubiquitous encryption, enterprise
authentication, audit, DRM, secure ID propagation, and Gold Version C&A.
4
Data Harbor—Data as a Service
– An ultra-high performance data environment that enables CIA missions to acquire,
federate, and position and securely exploit huge volumes data. Data in context.
5
Cloud Computing—Infrastructure as a Service
– Capacity ahead of demand. Large scale, elastic, commodity hosting, storage, and compute

6. It’s a
Big Data
World
6

7. Google
> 100 PB
> 1T indexed URLs
> 3 million servers
> 7.2B page-views/day
7

8. FaceBook
> 1 billion users
> 300PB; +> 500TB/day
> 35% of world’s photographs
8

9. YouTube
> 1000PB
+>72 hours/minute
>37 million hours/year
> 4 billion views/day
9

10. World Population
> 7,057,065,162
10

11. Twitter
> 124B tweets/year
> 390M/day
~4500/sec
11

12. Global Text Messages
> 6.1T per year
> 193,000 per second
> 876 per person per year
12

13. US Cell Calls
> 2.2 T minutes/year
> 19 minutes / person / day
(uncompressed < 1 YouTube/year)
13

14. 3
Driving Forces
14

15. Social
Mobile
Cloud
15

16. + + =
Big Data
16

17. + +
Increases the velocity of
innovation
17

18. + +
Accelerates social
Change
18

19. 19

20. + +
Altered the
Flow
of Information
20

21. 3
Emerging
Forces

22. Nano
Bio
Sensors
22

23. Mobile Sensor Platform
Microphone
Image
3-axis accelerometer
Touch
Light
Proximity
Geolocation
Communicator, Tricorder, Transporter
23

24. Mobile Health Platform
Pacemaker
Blood sugar tester
Insulin controller
Health monitor
Exercise coach
Remote tune-ups
Early warning system
24

25. Mobile Sensor Platform
Identity by 3-axis accelerometer
Gender (71%)
Height--tall or short (80%)
Weight--heavy or light (80%)
You by your gait (100%)
Actitracker—Android App
25

26. + + +
+ + =
The inanimate becomes
sentient
26

27. + + +
+ + =
Smarter Planet
Cars drive themselves
Machines know your needs
27

28. + + +
+ + =
Drive radical efficiencies
Enhance social engagement
Improve information sharing
Enables global reach
Green (automatic routing)
Improve our health
Stop/prevent crime
… 28

29. Sensors are Really Big
1
Sensors are unbounded
2
Sensors are promiscuous
3
Sensors are indiscriminate

30. The Internet of Things is Bigger
1
Everything is Connected
2
Everything Communicates
3
Everything is a Sensor

31. That’s the
Really Big Data
Challenge of the future
31

32. Why
We
Care
32

33. Why
We
Care
33

34. Why
We
Care
34

35. Why
We
Care
35

36. Impact of Big Data
1
Know what we know
2
Discover the gaps in our knowledge
3
Focus targeting to fill the gaps
4
More effective use of expensive or long lead
collection assets
5
Better global coverage to limit surprise
6
Enhance understanding and improve analysis

37. Implications
37

38. 4 Rules of Big Data
1
It’s the data…
- Apologies to James Carville
2
Power to the people
- Apologies to the Black Panthers
3
Latency breeds contempt
- Apologies to Aesop
4
Context, context, context
- Apologies to Lord Harold Samuel

39. It’s the Data…
39

40. Data vs Tools—A History
Lesson
• Sophisticated tools without the data are
useless
• Mediocre tools with the data are frustrating
• Analysts will always opt for frustration over
futility, if that is their only option

41. Our Job
1
Leverage the Big Data world
2
Find the Information that Matters
3
Connect the Dots
4
Understand the Plans of our Adversaries
Safeguard our national security

42. The
Problem
42

43. Our Problem: Which 5K
1
Don’t know the future value of data
2
We cannot connect dots we don’t have
3
Traditional, requirements driven, collection
fails in the Big Data world
- Can’t task for data you don’t know you do need
- The few cannot know the needs of the many
- Global Coverage requires Global Data

44. Characteristics of Big Data
1
More is always better
2
Signal to noise only gets worse
3
Enumeration not modeling
4
Requirements are usually hindsight

45. Data as a Service
• Analysts and operators are not data engineers
• Need insight and understanding
• Ask a question and get a coherent answer
• Cannot know what data sets contain
information of value to them
• Imbue data services and tools with those
smarts
• Smart Data, smart tools, smarter intelligence
45

46. Power to the
People
46

47. Today
• Analytics and tools are hard to use
• Specialists are required to derive value
• Skilled people are in short supply
• Algorithms are dense and arcane
• Require a lot of hand curation
• Built for business not for intelligence
47

48. New Fields of
Expertise
Data Scientist
Information Engineer
48

49. Data Science
* Data science combines elements from many
fields:
Math
Statistics
Data Engineering
Pattern Recognition and Learning
Advanced Computing
Visualization
Uncertainty Modeling
Data Warehousing
High performance computing
* Wikipedia

50. Big Data Democracy Wins
The power of big data
can only be fully realized
when it is in the hands
of the average user
50

51. Tomorrow
• Elegant, powerful and easy to use tools and
visualizations
• Machines to do more of the heavy lifting
• Intelligent systems that learn from the user
• Correlation not search
• “Curiosity layer”– machines that are curious on
your behalf

52. 7 Universal Constructs for
Analytics
People Events
Places Concepts
Organizations Things
Time
52

53. User Built Recipes
53

54. Keep it Simple
• Data Scientists focus on hard problems
• Build reusable components that anyone
can apply—Recipes
• Share them widely—Apps Store/Apps Mall
—Recipe Book
• Let users assemble components their way
• Experiment and fail quickly to succeed faster

55. Latency Breeds
Contempt
55

56. Its All About Speed
• Hadoop/Map Reduce—batch
• Flexible, powerful, slow
• Equivalent of Real-Time Map/Reduce
• Flexible, powerful and fast
• Demel, Caffeine, Impala, Apache Drill, Spanner…
• Recursive Streams processing w/
complex analytics
• In-memory—peta-scale RAM architectures
• Distributed, in-memory analytics

57. Tectonic Technology Shifts
Traditional Processing Mass Analytics/Big Data
Data on SAN Data at processor
Move Data to Question Move Question to Data
Backup Replication management
Vertical scaling Horizontal scaling
Capacity after demand Capacity ahead of demand
DR COOP
Size to peak load Dynamic/elastic provisioning
Tape SAN
SAN Disk
Disk SSD
RAM limited Peta-scale RAM

58. New Computing Architectures
• Data close to compute
• Power at the edge
• Optical Computing/Optical Bus
• End of the motherboard—shared pools of
everything
• Software defined everything—compute,
storage, networking, data center
• Network is the bottleneck and constraint

59. Context,
Context,
Context
59

60. Everything in Your Frame of
Reference
• Widgets—Webtop in context to business
• Schema on Read—Data in context to your
question
• User assembled analytics—answers in
context to your questions
• Elastic computing—computing in context
to your demand

61. Closing
Thoughts
61

62. High Noon
in the
Information Age
62

63. It is nearly within our grasp
to compute on all human
generated information
63

64. FaceBook
> 1 billion users
> 35% of all photographs
64

65. The inanimate is rapidly
becoming sentient
Smarter Planet
Cars drive themselves
Machines know your needs
65

66. 3 Wave of
rd
Computing
Cognitive Machines
Watson
66

67. + + +
+ + =
Moving faster than government can keep up
The legal system is woefully behind
What are your rights? Who owns your data?
Driving the pace of social change
Exponentially increasing cyber threats 67

68. 68