1. Team Sentinel
112 Interviews
Jared Dunnmon Darren Hau Atsu Kobashi Rachel Moore
Problem: Intelligence,
surveillance, reconnaissance
is difficult for 7th Fleet in
contested areas
Solution: Navy needs cheap,
distributed sensors
Problem: Navy is hindered by
outdated, cumbersome
maritime domain awareness
tools
Solution: Navy actually
needs enhanced data fusion,
analytics, and sharing
4 Site Visits
Week 0 Week 9
2. Jared Dunnmon Darren Hau Atsu Kobashi Rachel Moore
Degree
Program &
Department
PhD
Mechanical
Engineering
BS
Electrical Engineering
MS
Electrical Engineering
Joint Degree MBA
and E-IPER MS
GSB
Expertise Experience in
mechanical design,
distributed energy
harvesting,
computational
modeling, machine
learning, and data
analytics, MBA and
previous work
experience at energy
startup Offgrid Electric.
Co-founder of Dragonfly
Systems, a solar
company acquired by
SunPower. Experience in
renewable energy,
power electronics,
reliability, and
manufacturing. Inventor
of multiple U.S. patents.
Record of translating
market needs into viable
product.
Industry experience
as a software
engineer for Nissan's
Autonomous Vehicle
team and experience
in the defense sector
working for
Lockheed Martin.
Academic experience
with machine
learning and data
analytics.
Rachel (Caltech ‘13)
worked extensively
with hardware as
an engineer and
project manager at
a defense
contractor prior to
the GSB.
Team Sentinel
5. Research
- Interviews to assess needs,
organizational dynamics,
procurement strategy
- Site visits to see current practices
- Identify key geographic areas of
interest
Prototype
- Evaluate existing sensor
platforms with commercial partners
- Integrate sensor(s) of interest into
partner product
- Compile existing data resources
- Evaluate ML algorithms
Scaling
- Develop fabrication /
procurement strategy
- Primary: 7th Fleet
decision makers,
ONI intelligence
officers, and
operators
- Secondary: Dual-
use entities such as
Coast Guard,
environmental monitoring,
research
- Tertiary: State Department
Lower cost sensor
solution
Improved coverage
- Persistent presence over
enlarged area
- Design reliability & robustness
via distributed architecture
Actionable
intelligence
- Cross-domain analysis
techniques to integrate multiple
data sources
- Improved UI increases decision
quality and speed
- Provide insights to identify
potential hot spots
Flexible platform
- open architecture
- plug-and-play
- disposable/low-maintenance
- back/forward compatibility
Reduce manpower burden:
- Remove tedious/manual tasks
through automation
- More efficiently use existing
analysts
- Good UI for operators, decision-makers
- Decreased time to ID & differentiate threats
- Increased area coverage + persistence
- Cost savings with respect to existing solutions
- Prototype operability + demonstrated scalability
- Prototype initial sensor platform with single desired capability
- Build multiple units pursuing the same threat group (network effects)
and derive useful insights from analysis tools
- Deploy pilot in operational environment
- Develop fabrication/procurement pipeline + cost models for scaling
Fixed
- Buying proprietary data
- Software tools
- Hardware evaluation + prototyping
equipment
- Evaluation of commercial products
Prototyping
- Existing sensor platforms
- Academic research
Scaling
- Available commercial + military data
- Existing analysis software tools
- AWS
- Need demand from operators and
deployment personnel in 7th Fleet
- Need commanding officer to
confirm decision-making benefits
- Need intelligence officers from
ONI / N2 to confirm effectiveness of
insights
- Need IT approvals to integrate into
systems
- Need support of commercial
partners if want to leverage their
platforms
Beneficiaries
Mission Achievement
Mission Budget/Costs
Buy-In
Deployment
Value
Proposition
Key Activities
Key Resources
Key Partners
Military
- 7th Fleet + designated
sponsor
- Naval Postgraduate School (NPS)
- Office of Naval Research (ONR)
Commercial
- Distributed sensor platform
companies (i.e. Saildrone, AMS)
- Data analytics (i.e. Palantir,
Google)
- Advanced manufacturing
Academic
- Universities (i.e. University of Hawaii)
- National Labs (Lincoln Labs, Sandia)
Other
- IUU fishing + anti-smuggling
stakeholders (i.e. Coast Guard, PNA)
Week 0 Mission: Provide Cost-Effective, Actionable Intelligence at All Times
Testing
- 7th Fleet assets for pilot
- Research barge
Variable
- Travel for site visits, pilots
- R&D personnel
- Manufacturing
- Lower cost sensor
solution
- Actionable
intelligence
- Flexible platform
- Primary: 7th
Fleet decision
makers, ONI
intelligence
officers, and
operators
- Secondary: Dual-
use entities such as
Coast Guard
6. Research
- Interviews to assess needs,
organizational dynamics,
procurement strategy
- Site visits to see current practices
- Identify key geographic areas of
interest
Prototype
- Evaluate existing sensor
platforms with commercial partners
- Integrate sensor(s) of interest into
partner product
- Compile existing data resources
- Evaluate ML algorithms
Scaling
- Develop fabrication /
procurement strategy
- Primary: 7th Fleet
decision makers,
ONI intelligence
officers, and
operators
- Secondary: Dual-
use entities such as
Coast Guard,
environmental monitoring,
research
- Tertiary: State Department
Lower cost sensor
solution
Improved coverage
- Persistent presence over
enlarged area
- Design reliability & robustness
via distributed architecture
Actionable
intelligence
- Cross-domain analysis
techniques to integrate multiple
data sources
- Improved UI increases decision
quality and speed
- Provide insights to identify
potential hot spots
Flexible platform
- open architecture
- plug-and-play
- disposable/low-maintenance
- back/forward compatibility
Reduce manpower burden:
- Remove tedious/manual tasks
through automation
- More efficiently use existing
analysts
- Good UI for operators, decision-makers
- Decreased time to ID & differentiate threats
- Increased area coverage + persistence
- Cost savings with respect to existing solutions
- Prototype operability + demonstrated scalability
- Prototype initial sensor platform with single desired capability
- Build multiple units pursuing the same threat group (network effects)
and derive useful insights from analysis tools
- Deploy pilot in operational environment
- Develop fabrication/procurement pipeline + cost models for scaling
Fixed
- Buying proprietary data
- Software tools
- Hardware evaluation + prototyping
equipment
- Evaluation of commercial products
Prototyping
- Existing sensor platforms
- Academic research
Scaling
- Available commercial + military data
- Existing analysis software tools
- AWS
- Need demand from operators and
deployment personnel in 7th Fleet
- Need commanding officer to
confirm decision-making benefits
- Need intelligence officers from
ONI / N2 to confirm effectiveness of
insights
- Need IT approvals to integrate into
systems
- Need support of commercial
partners if want to leverage their
platforms
Beneficiaries
Mission Achievement
Mission Budget/Costs
Buy-In
Deployment
Key Activities
Key Resources
Key Partners
Military
- 7th Fleet + designated
sponsor
- Naval Postgraduate School (NPS)
- Office of Naval Research (ONR)
Commercial
- Distributed sensor platform
companies (i.e. Saildrone, AMS)
- Data analytics (i.e. Palantir,
Google)
- Advanced manufacturing
Academic
- Universities (i.e. University of Hawaii)
- National Labs (Lincoln Labs, Sandia)
Other
- IUU fishing + anti-smuggling
stakeholders (i.e. Coast Guard, PNA)
Week 0 Mission: Provide Cost-Effective, Actionable Intelligence at All Times
Testing
- 7th Fleet assets for pilot
- Research barge
Variable
- Travel for site visits, pilots
- R&D personnel
- Manufacturing
- Lower cost sensor
solution
- Actionable
intelligence
- Flexible platform
- Primary: 7th
Fleet decision
makers, ONI
intelligence
officers, and
operators
- Secondary: Dual-
use entities such as
Coast Guard
Value Proposition
- Lower cost sensor
solution
- Actionable intelligence
- Flexible platform
Beneficiaries
- Primary: 7th Fleet
decision makers, ONI
intelligence officers, and
operators
- Secondary: Dual-use
entities such as Coast
Guard
Value
Proposition
8. Learning Progression: Week 1
● Week 1
○ Hypotheses
■ This is a problem with insufficient sensing
○ Experiments:
■ Conversations with mentors/stakeholders/contacts
○ Learning:
■ Sensors largely exist, but price point can be too high
■ Government struggles with sheer volume of open-source data
■ Internal information sharing is a big problem
■ Episodic persistence is acceptable--24/7 is not required
○ Proposed solution (MVP)
■ Diagram of entire ISR infrastructure with an emphasis on data aggregation
○ Key Takeaways:
Number of Interviews: 14
Hypothesis:
- Insufficient sensing
capabilities
9. Learning Progression: Week 1
● Week 1
○ Hypotheses
■ This is a problem with insufficient sensing
○ Experiments:
■ Conversations with mentors/stakeholders/contacts
○ Learning:
■ Sensors largely exist, but price point can be too high
■ Government struggles with sheer volume of open-source data
■ Internal information sharing is a big problem
■ Episodic persistence is acceptable--24/7 is not required
○ Proposed solution (MVP)
■ Diagram of entire ISR infrastructure with an emphasis on data aggregation
○ Key Takeaways:
Number of Interviews: 14
Experiments:
- Interviews, site
visits...
10. Learning Progression: Week 1
● Week 1
○ Hypotheses
■ This is a problem with insufficient sensing
○ Experiments:
■ Conversations with mentors/stakeholders/contacts
○ Learning:
■ Sensors largely exist, but price point can be too high
■ Government struggles with sheer volume of open-source data
■ Internal information sharing is a big problem
■ Episodic persistence is acceptable--24/7 is not required
○ Proposed solution (MVP)
■ Diagram of entire ISR infrastructure with an emphasis on data aggregation
○ Key Takeaways:
Number of Interviews: 14
Learnings:
- Sensors largely exist
- Information sharing is a big
problem
- Gov overwhelmed by sheer bulk
of data
11. Learning Progression: Week 1
● Week 1
○ Hypotheses
■ This is a problem with insufficient sensing
○ Experiments:
■ Conversations with mentors/stakeholders/contacts
○ Learning:
■ Sensors largely exist, but price point can be too high
■ Government struggles with sheer volume of open-source data
■ Internal information sharing is a big problem
■ Episodic persistence is acceptable--24/7 is not required
○ Proposed solution (MVP)
■ Diagram of entire ISR infrastructure with an emphasis on data aggregation
○ Key Takeaways:
Number of Interviews: 14
We pivoted in Week 1!
12. Weeks 1 - 3: What’s the problem?
High-level Thinkers
Defense Contractors
Week 1
Information sharing, data
aggregation
13. Weeks 1 - 3: What’s the problem?
INTELLIGENCE
(N2)
High-level Thinkers
Defense Contractors
Week 1
Information sharing, data
aggregation
Week 2
Sensors and deployment?
14. Weeks 1 - 3: What’s the problem?
INTELLIGENCE
(N2)
OPERATIONS
(N3)
High-level Thinkers
Defense Contractors
Week 1
Information sharing, data
aggregation
Week 2
Sensors and deployment?
Week 3
Nope, it really is a data
problem
15. Weeks 1 - 3: Cognitive Dissonance
INTELLIGENCE
(N2)
OPERATIONS
(N3)
High-level Thinkers
Defense Contractors
Week 1
Information sharing, data
aggregation
Week 2
Sensor deployment?
Week 3
Nope, it really is a data
problem
BIG IDEAS:
1. Everyone is right, but priorities are
influenced by their roles.
1. Sensors are great but Navy wouldn’t
know what to do with it.
16. Weeks 1 - 3: Cognitive Dissonance
INTELLIGENCE
(N2)
OPERATIONS
(N3)
High-level Thinkers
Defense Contractors
Week 1
Information sharing, data
aggregation
Week 2
Sensor deployment?
Week 3
Nope, it really is a data
problem
BIG IDEAS:
1. Everyone is right, but priorities are
influenced by their roles.
1. Sensors are great but Navy wouldn’t
be able to effectively use the data.
18. Research
- Interviews to assess needs,
organizational dynamics,
procurement strategy
- Site visits to see current practices
Prototype
- Integrate sensor(s) of interest into
partner product
- Compile existing data resources
- Evaluate relevant ML
algorithms
- Iterate on human-machine
interaction
Strategic Decision Makers
E.g. CPT, VADM, ADM
(PACFLT), ADM (PACOM)
Analysts (N2)
E.g. Jason Knudson, John
Chu, Jed Raskie, Joseph Baba
Operators (N3)
Scheduled this week
Planners (N5)
Need to find these people
- Decreased time to predict hot spots, ID & differentiate threats
- Good UI for operators, decision-makers
- Timely, episodic persistent coverage with easily-deployed system
- Cost savings with respect to existing solutions
- Prototype operability + demonstrated scalability
Hardware
- Acquire initial sensor platform with single desired capability
- Design deployment strategy + platform
- Deploy pilot in operational environment
- Develop fabrication/procurement pipeline + cost models for scaling
Software
- Determine most useful data interface for analysts
- Determine optimal information flow to strategic decision makers
- Develop ML and visualization algorithms
- Build, Test, and Deploy Product
Fixed
- Buying proprietary data
- Software tools
- Evaluation of commercial products
Prototyping
- Existing sensor platforms
- Academic research
Scaling
- Available commercial + military data
- Existing database tools (Palantir,
AWS)
- Need demand from operators and
deployment personnel in 7th Fleet
- Need commanding officer to
confirm decision-making benefits
- Need intelligence officers from
ONI / N2 to confirm effectiveness of
insights
- Need IT approvals to integrate into
systems
- Need support of commercial
partners if want to leverage their
platforms
Beneficiaries
Mission Achievement
Mission Budget/Costs
Buy-In
Deployment
Value
Proposition
Key Activities
Key Resources
Key Partners
Military
- 7th Fleet + designated sponsor
- Naval Postgraduate School (NPS)
- Office of Naval Research (ONR)
- Acquisition Personnel
Commercial
- Distributed sensor platform
companies (i.e. Saildrone, AMS)
- Data analytics (i.e. Palantir, Google)
Academic
- Universities (i.e. University of Hawaii)
- National Labs (Lincoln Labs, Sandia)
Other
- IUU fishing + anti-smuggling
stakeholders (i.e. Coast Guard, PNA)
Week 3 Mission: Provide Cost-Effective, Actionable Intelligence at All Times
Testing
- Research barge
- Access to model analyst data
interface
Variable
- Travel for site visits, pilots
- R&D personnel
- Manufacturing/Development
IMPROVE TACTICAL AND
STRATEGIC DECISION
MAKING VIA BETTER DATA
HANDLING
(1) Rapid Strategic
Decisionmaking via Improved
Reporting
(2) Improved Tactical Decision
Making via Enhanced
Information Sharing
(3) More Effective Analysis via
Searchable, Visualizable Data
Integration
ENHANCE INCOMING DATA
STREAMS
(1) Improved Collection of
Existing Data Streams (e.g.
Fishing Broadcasts)
(2) Predictive Intel through
Machine Learning
Additional Sensing Capability
BETTER DECISION
MAKING:
(1) Improved
Reporting
(2) Enhanced
Information Sharing
(3) Searchable,
Visualizable Data
Integration
BETTER
UTILIZATION OF
DATA:
(1) Improved
Collection of Existing
Data Streams
(2) Predictive Intel
through Machine
Learning
- Strategic
Decision Makers
(e.g. Admirals)
- Intel Analysts
- Operators
- Planners
19. Research
- Interviews to assess needs,
organizational dynamics,
procurement strategy
- Site visits to see current practices
Prototype
- Integrate sensor(s) of interest into
partner product
- Compile existing data resources
- Evaluate relevant ML
algorithms
- Iterate on human-machine
interaction
Strategic Decision Makers
E.g. CPT, VADM, ADM
(PACFLT), ADM (PACOM)
Analysts (N2)
E.g. Jason Knudson, John
Chu, Jed Raskie, Joseph Baba
Operators (N3)
Scheduled this week
Planners (N5)
Need to find these people
- Decreased time to predict hot spots, ID & differentiate threats
- Good UI for operators, decision-makers
- Timely, episodic persistent coverage with easily-deployed system
- Cost savings with respect to existing solutions
- Prototype operability + demonstrated scalability
Hardware
- Acquire initial sensor platform with single desired capability
- Design deployment strategy + platform
- Deploy pilot in operational environment
- Develop fabrication/procurement pipeline + cost models for scaling
Software
- Determine most useful data interface for analysts
- Determine optimal information flow to strategic decision makers
- Develop ML and visualization algorithms
- Build, Test, and Deploy Product
Fixed
- Buying proprietary data
- Software tools
- Evaluation of commercial products
Prototyping
- Existing sensor platforms
- Academic research
Scaling
- Available commercial + military data
- Existing database tools (Palantir,
AWS)
- Need demand from operators and
deployment personnel in 7th Fleet
- Need commanding officer to
confirm decision-making benefits
- Need intelligence officers from
ONI / N2 to confirm effectiveness of
insights
- Need IT approvals to integrate into
systems
- Need support of commercial
partners if want to leverage their
platforms
Beneficiaries
Mission Achievement
Mission Budget/Costs
Buy-In
Deployment
Value
Proposition
Key Activities
Key Resources
Key Partners
Military
- 7th Fleet + designated sponsor
- Naval Postgraduate School (NPS)
- Office of Naval Research (ONR)
- Acquisition Personnel
Commercial
- Distributed sensor platform
companies (i.e. Saildrone, AMS)
- Data analytics (i.e. Palantir, Google)
Academic
- Universities (i.e. University of Hawaii)
- National Labs (Lincoln Labs, Sandia)
Other
- IUU fishing + anti-smuggling
stakeholders (i.e. Coast Guard, PNA)
Week 3 Mission: Provide Cost-Effective, Actionable Intelligence at All Times
Testing
- Research barge
- Access to model analyst data
interface
Variable
- Travel for site visits, pilots
- R&D personnel
- Manufacturing/Development
IMPROVE TACTICAL AND
STRATEGIC DECISION
MAKING VIA BETTER DATA
HANDLING
(1) Rapid Strategic
Decisionmaking via Improved
Reporting
(2) Improved Tactical Decision
Making via Enhanced
Information Sharing
(3) More Effective Analysis via
Searchable, Visualizable Data
Integration
ENHANCE INCOMING DATA
STREAMS
(1) Improved Collection of
Existing Data Streams (e.g.
Fishing Broadcasts)
(2) Predictive Intel through
Machine Learning
Additional Sensing Capability
BETTER DECISION
MAKING:
(1) Improved
Reporting
(2) Enhanced
Information Sharing
(3) Searchable,
Visualizable Data
Integration
BETTER
UTILIZATION OF
DATA:
(1) Improved
Collection of Existing
Data Streams
(2) Predictive Intel
through Machine
Learning
- Strategic
Decision Makers
(e.g. Admirals)
- Intel Analysts
- Operators
- Planners
Value Proposition
- More Educated
Decision-Making
(improved reporting, info
sharing, and
visualization)
- Better Utilization of
Data (fusing disparate
data sources and
predictive models)
Beneficiaries
- Strategic Decision
Makers (e.g. Admirals)
- Intel Analysts (monitor
enemy ships)
- Operators (control US
Navy ships; decisions
based on intel reports)
20. Weeks 4 - 5: This is a REALLY BIG problem
“I’ve been using GCCS for
7 years and I still don’t
know how to filter with it.”
- Surface Warfare Officer
Week 4:
There isn’t really a
Common Operational
Picture...
“Pacific Command, Pacific
Fleet, and 7th Fleet see the
same ship in different
places.”
- PACOM officer
21. Weeks 4 - 5: This is a REALLY BIG problem
“I’ve been using GCCS for
7 years and I still don’t
know how to filter with it.”
- Surface Warfare Officer
Week 4:
There isn’t really a
Common Operational
Picture...
“PACOM, Pac Fleet, and
7th Fleet see the same ship
in different places.”
- PACOM officer
Week 5:
Outdated technology
due to procurement
processes
“Navy acquisition: using
yesterday’s technology...
tomorrow.”
- 7th Fleet N2
22. Customer Discovery - Operations Center Workflow
Hey Max, why is the
ship still in port? This
info isn’t up-to-date.
23. Can you ask them to
update this?
Customer Discovery - Operations Center Workflow
30. Okay, the OS put in a new
latitude and longitude.
Ah, there it is.
Customer Discovery - Operations Center Workflow
31. Weeks 6-7: Other Programs Trying to Address Gaps
● DARPA Insight
● SRI International Cooperative Situational Information Integration
● Maritime Tactical Command and Control (MTC2)
● Global Command and Control System (GCCS-M)
● Command and Control Personal Computer (C2PC)
● Distributed Common Ground System - Navy (DCGS-N)
● ONI Sealink Advanced Analysis
● Resilient Command and Control
32. Weeks 6-7: Other Programs Trying to Address Gaps
● DARPA Insight
● SRI International Cooperative Situational Information Integration
● Maritime Tactical Command and Control (MTC2)
● Global Command and Control System (GCCS-M)
● Command and Control Personal Computer (C2PC)
● Distributed Common Ground System - Navy (DCGS-N)
● ONI Sealink Advanced Analysis
● Resilient Command and Control
Lots of existing programs...
33. Week 7: Classification Wall
You should talk with the program
manager! I’ll send an intro email.
Great, thanks!
34. Week 7: Classification Wall
Hi, can you share anything
about this tool?
Actually...no...
Sorry.
35. Week 7: Found an Analogous Problem
Illegal Fishing
All the same problems and needs…
But without the classification issues!
36. Data & Analytics
- Compile existing data
resources/scope out future ones
- Develop flexible data
fusion/analytics algorithms
Defining C2-F
- Brainstorming what “Command
and Control of the Future” (C2-F or
“MTC2-F”) would be
- Interviewing (customer discovery)
for younger sailors
Software Development
Prototype Testing/Acquisitions
Pursue Information Assurance
Certification
USN Strategic Decision
Makers
USN Analysts (N/J2)
USN Operators (N/J3)
Anti-IUU Fishing Enforcers
(USCG, Partner Nations,
etc.)
Anti-IUU Fishing
Stakeholders (NGOs, Legal
Fishing)
(Commercial entities that
use/would benefit from
enhanced C2-type systems)
USN
- Timely, accurate operational decisions
- Decreased time to predict hot spots, ID & differentiate threats
- Increased engagement and effectiveness of younger sailors
- Up-to-date, reliable info in frontline environment
Anti-IUU Fishing
- Reduction in IUU fishing worldwide due to better deterrence
- Better allocation of scarce / expensive interdiction resources
- Widespread engagement of operators, governments, and the public
USN
- Work with fleet sponsor to get C2-F system on fleet needs list
- Ensure C2-F makes it into FIMES database, engage S&T bridge
personnel to talk with key decision makers
- Work with NWDC, ONR S&T, PACFLT LOEs to test solution
- Engage PACFLT N8/N9 shops to implement modular operational
deployment & update pathways
Anti IUU Fishing
- Work with NGOs, gov’t departments, USCG, operators, etc. to find
key influencers/stakeholders
- Deploy solution where possible,
Fixed
- Existing Software tools/APIs
- Evaluation of commercial products
- Information assurance process
steps
Data & Analytics
- APIs for accessing data (e.g. API for
Global Fishing Watch, AIS), $$$
needed to access this
Defining C2-F
-Ideas/feedback from young sailors
- Need commanding officer to
confirm decision-making benefits
- Need intelligence officers from
ONI / N2 and operators from N3 to
confirm effectiveness of insights
- Need IT approvals to integrate into
systems
- Need support of commercial
partners if we want to leverage their
platforms
-Need support of existing
PMOs/S&T personnel to make sure
we’re not duplicating work
Beneficiaries
Mission Achievement
Mission Budget/Costs
Buy-In
Deployment
Value
Proposition
Key Activities
Key Resources
Key Partners
Military
- PACFLT (7th/3rd Fleet, young E- and
O- who use current C2 tools)
- Program Office for MCT2 (PMW 150)
- Information Assurance Personnel
- NWDC, ONR S&T Advisors, C7F N2,
C7F CIG, C3F N8/9, PACOM CSIG,
OPNAV N2/N6 (Acquisition/Testing)
Anti-IUU Fishing Stakeholders
- IUU fishing + anti-smuggling
stakeholders (i.e. Coast Guard, PNA)
Data/Software/Algorithms
- Data: Skytruth, Pew, Global Fishing
Watch, Capella, TerraSAR
-Software: Palantir Skytruth, USCG,
NPS/ONR, SeaVision, Sea Scout
-Algorithms: Universities (e.g.
Vanderbilt), NPS/ONR, NGOs
Software Development
-AWS, programmers, $$$ for both,
subject matter expertise on
phenomenology of ships, activities
Prototype Testing/Acquisition
- Military Sealift Command ships, 7th
Fleet experimentation ships and
personnel
Information Assurance Certification
-Access to personnel to provide
certification / approval
Variable
- Travel for site visits, pilots,
interviews with sailors
- R&D personnel
- Development
- Data and APIs
- AWS & Distributed Computing
IMPROVE USN DECISIONS &
OPS VIA C2-F WITH IMPROVED
DATA HANDLING, UI/UX,
COMMS, AND HARDWARE
(1) Rapid Strategic
Decisionmaking via Improved
Reporting, Coordination, Visibility
(2) Improved Tactical Decision
Making via Timely, Accurate
Information Sharing
(3) More Effective Analysis via
Searchable, Visualizable, Source-
Flexible Data Integration
(Layering & Filtering)
(4) Increased Analyst Bandwidth
via Predictive Intel and Alerts (e.g.
Machine Learning) Flexibly
Applied to Available Data
(5) Improved Collection of
Existing Data Streams
(6) Increasing Morale &
Engagement for Millenial Sailors
ENHANCE ANTI-IUU FISHING
CAPABILITIES
(1) Improved Detection Using
Data Fusion/Analytics
(2) Enhanced Enforcement via
Improved Communication
(3) Lower Barriers to Engaging
Civilians in Reporting IUU Fishing
Activities
Week 7 Mission: Enabling Rapid Decisions from Heterogeneous Data - Pivot to Proxy
37. Data & Analytics
- Compile existing data
resources/scope out future ones
- Develop flexible data
fusion/analytics algorithms
Defining C2-F
- Brainstorming what “Command
and Control of the Future” (C2-F or
“MTC2-F”) would be
- Interviewing (customer discovery)
for younger sailors
Software Development
Prototype Testing/Acquisitions
Pursue Information Assurance
Certification
USN Strategic Decision
Makers
USN Analysts (N/J2)
USN Operators (N/J3)
Anti-IUU Fishing Enforcers
(USCG, Partner Nations,
etc.)
Anti-IUU Fishing
Stakeholders (NGOs, Legal
Fishing)
(Commercial entities that
use/would benefit from
enhanced C2-type systems)
USN
- Timely, accurate operational decisions
- Decreased time to predict hot spots, ID & differentiate threats
- Increased engagement and effectiveness of younger sailors
- Up-to-date, reliable info in frontline environment
Anti-IUU Fishing
- Reduction in IUU fishing worldwide due to better deterrence
- Better allocation of scarce / expensive interdiction resources
- Widespread engagement of operators, governments, and the public
USN
- Work with fleet sponsor to get C2-F system on fleet needs list
- Ensure C2-F makes it into FIMES database, engage S&T bridge
personnel to talk with key decision makers
- Work with NWDC, ONR S&T, PACFLT LOEs to test solution
- Engage PACFLT N8/N9 shops to implement modular operational
deployment & update pathways
Anti IUU Fishing
- Work with NGOs, gov’t departments, USCG, operators, etc. to find
key influencers/stakeholders
- Deploy solution where possible,
Fixed
- Existing Software tools/APIs
- Evaluation of commercial products
- Information assurance process
steps
Data & Analytics
- APIs for accessing data (e.g. API for
Global Fishing Watch, AIS), $$$
needed to access this
Defining C2-F
-Ideas/feedback from young sailors
- Need commanding officer to
confirm decision-making benefits
- Need intelligence officers from
ONI / N2 and operators from N3 to
confirm effectiveness of insights
- Need IT approvals to integrate into
systems
- Need support of commercial
partners if we want to leverage their
platforms
-Need support of existing
PMOs/S&T personnel to make sure
we’re not duplicating work
Beneficiaries
Mission Achievement
Mission Budget/Costs
Buy-In
Deployment
Value
Proposition
Key Activities
Key Resources
Key Partners
Military
- PACFLT (7th/3rd Fleet, young E- and
O- who use current C2 tools)
- Program Office for MCT2 (PMW 150)
- Information Assurance Personnel
- NWDC, ONR S&T Advisors, C7F N2,
C7F CIG, C3F N8/9, PACOM CSIG,
OPNAV N2/N6 (Acquisition/Testing)
Anti-IUU Fishing Stakeholders
- IUU fishing + anti-smuggling
stakeholders (i.e. Coast Guard, PNA)
Data/Software/Algorithms
- Data: Skytruth, Pew, Global Fishing
Watch, Capella, TerraSAR
-Software: Palantir Skytruth, USCG,
NPS/ONR, SeaVision, Sea Scout
-Algorithms: Universities (e.g.
Vanderbilt), NPS/ONR, NGOs
Week 7 Mission: Enabling Rapid Decisions from Heterogeneous Data - Pivot to Proxy
Software Development
-AWS, programmers, $$$ for both,
subject matter expertise on
phenomenology of ships, activities
Prototype Testing/Acquisition
- Military Sealift Command ships, 7th
Fleet experimentation ships and
personnel
Information Assurance Certification
-Access to personnel to provide
certification / approval
Variable
- Travel for site visits, pilots,
interviews with sailors
- R&D personnel
- Development
- Data and APIs
- AWS & Distributed Computing
IMPROVE USN DECISIONS &
OPS VIA C2-F WITH IMPROVED
DATA HANDLING, UI/UX,
COMMS, AND HARDWARE
(1) Rapid Strategic
Decisionmaking via Improved
Reporting, Coordination, Visibility
(2) Improved Tactical Decision
Making via Timely, Accurate
Information Sharing
(3) More Effective Analysis via
Searchable, Visualizable, Source-
Flexible Data Integration
(Layering & Filtering)
(4) Increased Analyst Bandwidth
via Predictive Intel and Alerts (e.g.
Machine Learning) Flexibly
Applied to Available Data
(5) Improved Collection of
Existing Data Streams
(6) Increasing Morale &
Engagement for Millenial Sailors
ENHANCE ANTI-IUU FISHING
CAPABILITIES
(1) Improved Detection Using
Data Fusion/Analytics
(2) Enhanced Enforcement via
Improved Communication
(3) Lower Barriers to Engaging
Civilians in Reporting IUU Fishing
Activities
Value Proposition
- Data fusion & analytics
with multiple sensor feeds
- Intuitive, easy-to-use UI
Beneficiaries
- …
- anti-IUU fishing enforcers
& stakeholders (i.e. Coast
Guard, NGOs, legal fishers)
38. Week 8: Redefined our Approach/Visit to San Diego
- Procurement +
deployment tricks
- How to fit with
existing tools?
Access to tools,
datasets
IUU Fishing
Navy 7th Fleet, 3rd Fleet
Visit to San Diego!
40. Weeks 8 - 9: Towards the Future
Week 8:
Command &
Control of the
Future (C2-F)
“If I had you four working
for me, I’d have you work
on C2 for your generation.”
- 3rd Fleet
41. Weeks 8 - 9: Towards the Future
Week 8:
Command &
Control of the
Future (C2-F)
“If I had you four working
for me, I’d have you work
on C2 for your generation.”
- 3rd Fleet
Week 9:
Sponsor is excited
about C2-F
“You guys have grasped
what very few people
understand.”
- Sponsor, 7th Fleet
“I’d like to stay involved in
what you are doing moving
forward!”
- Sponsor, 7th Fleet
42. Final MVP - Command & Control of the Future
CIC PACOM
Surface radar contact
but no AIS… This is
odd. Let me ALERT
others.
43. Final MVP - Command & Control of the Future
CIC PACOM
Surface radar contact
but no AIS… This is
odd. Let me ALERT
others.
I see an ALERT from
DDG102. Lets share
the C2 screen and
take a look
44. Final MVP - Command & Control of the Future
CIC PACOM
45. Final MVP - Command & Control of the Future
CIC PACOM
46. Data & Analytics
- Compile existing data
resources/scope out future ones
- Develop flexible data
fusion/analytics algorithms
Defining C2-F
- Brainstorming what “Command
and Control of the Future” (C2-F or
“MTC2-F”) would be
- Interviewing (customer discovery)
for younger sailors
Software Development
Prototype Testing/Acquisitions
Pursue Information Assurance
Certification
USN Strategic Decision
Makers
USN Analysts (N/J2)
USN Operators (N/J3)
Anti-IUU Fishing Enforcers
(USCG, Partner Nations,
etc.)
Anti-IUU Fishing
Stakeholders (NGOs, Legal
Fishing)
(Commercial entities that
use/would benefit from
enhanced C2-type systems)
USN
- Timely, accurate operational decisions
- Decreased time to predict hot spots, ID & differentiate threats
- Increased engagement and effectiveness of younger sailors
- Up-to-date, reliable info in frontline environment
Anti-IUU Fishing
- Reduction in IUU fishing worldwide due to better deterrence
- Better allocation of scarce / expensive interdiction resources
- Widespread engagement of operators, governments, and the public
USN
- Work with fleet sponsor to get C2-F system on fleet needs list
- Ensure C2-F makes it into FIMES database, engage S&T bridge
personnel to talk with key decision makers
- Work with NWDC, ONR S&T, PACFLT LOEs to test solution
- Engage PACFLT N8/N9 shops to implement modular operational
deployment & update pathways
Anti IUU Fishing
- Work with NGOs, gov’t departments, USCG, operators, etc. to find
key influencers/stakeholders
- Deploy solution where possible,
Fixed
- Existing Software tools/APIs
- Evaluation of commercial products
- Information assurance process
steps
Data & Analytics
- APIs for accessing data (e.g. API for
Global Fishing Watch, AIS), $$$
needed to access this
Defining C2-F
-Ideas/feedback from young sailors
- Need commanding officer to
confirm decision-making benefits
- Need intelligence officers from
ONI / N2 and operators from N3 to
confirm effectiveness of insights
- Need IT approvals to integrate into
systems
- Need support of commercial
partners if we want to leverage their
platforms
-Need support of existing
PMOs/S&T personnel to make sure
we’re not duplicating work
Beneficiaries
Mission Achievement
Mission Budget/Costs
Buy-In
Deployment
Value
Proposition
Key Activities
Key Resources
Key Partners
Military
- PACFLT (7th/3rd Fleet, young E- and
O- who use current C2 tools)
- Program Office for MCT2 (PMW 150)
- Information Assurance Personnel
- NWDC, ONR S&T Advisors, C7F N2,
C7F CIG, C3F N8/9, PACOM CSIG,
OPNAV N2/N6 (Acquisition/Testing)
Anti-IUU Fishing Stakeholders
- IUU fishing + anti-smuggling
stakeholders (i.e. Coast Guard, PNA)
Data/Software/Algorithms
- Data: Skytruth, Pew, Global Fishing
Watch, Capella, TerraSAR
-Software: Palantir Skytruth, USCG,
NPS/ONR, SeaVision, Sea Scout
-Algorithms: Universities (e.g.
Vanderbilt), NPS/ONR, NGOs
Week 9 Mission: Creating C2-F - Enabling Rapid Decisions from Heterogeneous Data
Software Development
-AWS, programmers, $$$ for both,
subject matter expertise on
phenomenology of ships, activities
Prototype Testing/Acquisition
- Military Sealift Command ships, 7th
Fleet experimentation ships and
personnel
Information Assurance Certification
-Access to personnel to provide
certification / approval
Variable
- Travel for site visits, pilots,
interviews with sailors
- R&D personnel
- Development
- Data and APIs
- AWS & Distributed Computing
IMPROVE USN DECISIONS &
OPS VIA C2-F WITH IMPROVED
DATA HANDLING, UI/UX,
COMMS, AND HARDWARE
(1) Rapid Strategic
Decisionmaking via Improved
Reporting, Coordination, Visibility
(2) Improved Tactical Decision
Making via Timely, Accurate
Information Sharing
(3) More Effective Analysis via
Searchable, Visualizable, Source-
Flexible Data Integration
(Layering & Filtering)
(4) Increased Analyst Bandwidth
via Predictive Intel and Alerts (e.g.
Machine Learning) Flexibly
Applied to Available Data
(5) Improved Collection of
Existing Data Streams
(6) Increasing Morale &
Engagement for Millenial Sailors
ENHANCE ANTI-IUU FISHING
CAPABILITIES
(1) Improved Detection Using
Data Fusion/Analytics
(2) Enhanced Enforcement via
Improved Communication
(3) Lower Barriers to Engaging
Civilians in Reporting IUU Fishing
Activities
47. Next Steps
Goal: Develop dual-use “Command & Control Tool of the
Future” based on collaborative data aggregation tool for the
IUU fishing use case
We’re going to continue working on this
Navy and sponsor interested
IUU Fishing folks are interested
48. IRL 1
IRL 4
IRL 3
IRL 2
IRL 7
IRL 6
IRL 5
IRL 8
IRL 9
First pass on MMC w/Problem Sponsor
Complete ecosystem analysis petal diagram
Validate mission achievement (Right side of canvas)
Problem validated through initial interviews
Prototype low-fidelity Minimum Viable Product
Value proposition/mission fit (Value Proposition Canvas)
Validate resource strategy (Left side of canvas)
Prototype high-fidelity Minimum Viable Product
Establish mission achievement metrics that matterTeam Assessment :
IRL 5
Post H4D Course
Actions
Team Sentinel intends to
pursue funding to create a
dual use solution for IUU
fishing, with the eventual
goal of getting a variant
adopted by the Navy.
Investment Readiness Level
49. Thank You!
We could not have survived this journey without the support from these outstanding
individuals (and many more!):
Sponsor
● LT Jason Knudson
Military Liaisons
● COL John Chu
● CDR Todd “Chimi” Cimicata
PACOM/Pac Fleet/7th Fleet/3rd Fleet
● CAPTs Andy Hertel, Greg
Hussman, ...
● CDR Rich LeBron, ...
● CAPT Yvette Davids, ...
● LT Kevin Walter, LTJG Vince
Fontana
Coast Guard
● CAPT Chris Conley
● LCDR Jed Raskie
NPS
● CDR Pablo Breuer
● CAPT Scot Miller
Others
● Dean Moon
● Rick Rikoski
● Chuck Wolf
● Richard D'Alessandro
(OGSystems)
● Graham Gilmer (BAH)
DIUx
● Steve Butow, Lauren
Schmidt
53. Research
- Interviews to assess needs,
organizational dynamics,
procurement strategy
- Site visits to see current practices
- Identify key geographic areas of
interest
Prototype
- Evaluate existing sensor
platforms with commercial partners
- Integrate sensor(s) of interest into
partner product
- Compile existing data resources
- Evaluate ML algorithms
Scaling
- Develop fabrication /
procurement strategy
- Develop tactical deployment
strategy
Strategic Decision Makers
E.g. CPT Greg Hussman,
VADM Joseph Aucoin
Acquisition Personnel
We need to find + talk with
these people
Analysts
E.g. Jason Knudson, John
Chu, Jed Raskie
Deployers
We need to find + talk with
these people
Primary: 7th Fleet decision
makers, ONI intelligence
officers, and operators
Secondary: Dual-use entities
such as Coast Guard,
environmental monitoring,
research
Tertiary: State Department
Actionable intelligence
- Predictive vs reactionary intel
through machine learning -
identify potential hot spots
- Simplifying to reduce data
overload
- Improved UI increases decision
quality and speed
Information Sharing
- Open architecture
- Improved information sharing
with differential permissions
- Cross-domain analysis
techniques to integrate multiple
data sources
- Plug-and-play data sources
- Back/forward compatibility
Deployment strategy
- i.e. deploy disposable sensors
off of waveglider
- modularity + distributed
architecture
- deployable from multiple
platforms
Lower cost sensor solution
- disposable/low-maintenance
Improved coverage
- Persistent presence over
enlarged area
- Design reliability & robustness
via distributed architecture
Episodic persistence
- Persistent coverage of a
chokepoint area for a limited time
Reduce manpower burden:
- Remove tedious/manual tasks
through automation
- More efficiently use existing
analysts
- Decreased time to predict hot spots, ID & differentiate threats
- Good UI for operators, decision-makers
- Increased area coverage + persistence
- Episodic persistent coverage with easily-deployed system
- Cost savings with respect to existing solutions
- Prototype operability + demonstrated scalability
Hardware
- Acquire initial sensor platform with single desired capability
- Build multiple units pursuing the same threat group (network effects)
and derive useful insights from analysis tools
- Deploy pilot in operational environment
- Develop fabrication/procurement pipeline + cost models for scaling
Software
- Build data aggregation backend + analytic engine + user-friendly UI
Fixed
- Buying proprietary data
- Software tools
- Hardware evaluation + prototyping
equipment
- Evaluation of commercial products
Prototyping
- Existing sensor platforms
- Academic research
Scaling
- Available commercial + military data
- Existing analysis software tools
- AWS
- Need demand from operators and
deployment personnel in 7th Fleet
- Need commanding officer to
confirm decision-making benefits
- Need intelligence officers from
ONI / N2 to confirm effectiveness of
insights
- Need IT approvals to integrate into
systems
- Need support of commercial
partners if want to leverage their
platforms
Beneficiaries
Mission Achievement
Mission Budget/Costs
Buy-In
Deployment
Value
Proposition
Key Activities
Key Resources
Key Partners
Military
- 7th Fleet + designated sponsor
- Naval Postgraduate School (NPS)
- Office of Naval Research (ONR)
Commercial
- Distributed sensor platform
companies (i.e. Saildrone, AMS)
- Data analytics (i.e. Palantir, Google)
- Advanced manufacturing
Academic
- Universities (i.e. University of Hawaii)
- National Labs (Lincoln Labs, Sandia)
Other
- IUU fishing + anti-smuggling
stakeholders (i.e. Coast Guard, PNA)
Mission: Provide Cost-Effective, Actionable Intelligence at All Times
Testing
- 7th Fleet assets for pilot
- Research barge
Variable
- Travel for site visits, pilots
- R&D personnel
- Manufacturing
54. Research
- Interviews to assess needs,
organizational dynamics,
procurement strategy
- Site visits to see current practices
- Identify key geographic areas of
interest
Prototype
- Evaluate existing sensor
platforms with commercial partners
- Integrate sensor(s) of interest into
partner product
- Compile existing data resources
- Evaluate ML algorithms
Scaling
- Develop fabrication /
procurement strategy
- Develop tactical deployment
strategy
Strategic Decision Makers
E.g. CPT Greg Hussman,
VADM Joseph Aucoin
Analysts (N2)
E.g. Jason Knudson, John
Chu, Jed Raskie, Joseph Baba
Deployers (N3)
We need to find + talk with
these people
ACQUIRING READY-TO-USE
DATA
Episodic persistence
- Persistent coverage of a
chokepoint area for a limited time
(days - 1 mo)
Timely deployment strategy
- i.e. deploy disposable sensors
off of waveglider
- sub-2 hr latency (TBD)
- deployable from multiple
platforms
Lower cost sensor solution
- disposable/low-maintenance
- modularity + distributed
architecture
Open Architecture
- Improved information sharing
with differential permissions
- Object-oriented database that is
easily searchable
- Cross-domain analysis
techniques to integrate multiple
data sources
- Compatible data format (.kmz)
Actionable intelligence
- Predictive vs reactionary intel
through machine learning -
identify potential hot spots
- Simplifying to reduce data
overload
- Improved UI increases decision
quality and speed
Reduce manpower burden:
- Remove tedious/manual tasks
through automation
- More efficiently use existing
analysts
- Decreased time to predict hot spots, ID & differentiate threats
- Good UI for operators, decision-makers
- Timely, episodic persistent coverage with easily-deployed system
- Cost savings with respect to existing solutions
- Prototype operability + demonstrated scalability
Hardware
- Acquire initial sensor platform with single desired capability
- Build multiple units pursuing the same threat group (network effects)
and derive useful insights from analysis tools
- Design deployment strategy + platform
- Deploy pilot in operational environment
- Develop fabrication/procurement pipeline + cost models for scaling
Software
- Determine most useful data interface for analysts
Fixed
- Buying proprietary data
- Software tools
- Hardware evaluation + prototyping
equipment
- Evaluation of commercial products
Prototyping
- Existing sensor platforms
- Existing deployment platforms
- Academic research
Scaling
- Available commercial + military data
- Existing database tools (Palantir,
AWS)
- Need demand from operators and
deployment personnel in 7th Fleet
- Need commanding officer to
confirm decision-making benefits
- Need intelligence officers from
ONI / N2 to confirm effectiveness of
insights
- Need IT approvals to integrate into
systems
- Need support of commercial
partners if want to leverage their
platforms
Beneficiaries
Mission Achievement
Mission Budget/Costs
Buy-In
Deployment
Value
Proposition
Key Activities
Key Resources
Key Partners
Military
- 7th Fleet + designated sponsor
- Naval Postgraduate School (NPS)
- Office of Naval Research (ONR)
- Acquisition Personnel
Commercial
- Distributed sensor platform
companies (i.e. Saildrone, AMS)
- Data analytics (i.e. Palantir, Google)
- Advanced manufacturing
Academic
- Universities (i.e. University of Hawaii)
- National Labs (Lincoln Labs, Sandia)
Other
- IUU fishing + anti-smuggling
stakeholders (i.e. Coast Guard, PNA)
Mission: Provide Cost-Effective, Actionable Intelligence at All Times
Testing
- 7th Fleet assets for pilot
- Research barge
Variable
- Travel for site visits, pilots
- R&D personnel
- Manufacturing
55. Research
- Interviews to assess needs,
organizational dynamics,
procurement strategy
- Site visits to see current practices
- Identify key geographic areas of
interest
Prototype
- Evaluate existing sensor
platforms with commercial partners
- Integrate sensor(s) of interest into
partner product
- Compile existing data resources
- Evaluate relevant ML
algorithms
- Iterate on human-machine
interaction
Strategic Decision Makers
E.g. CPT Greg Hussman,
VADM Joseph Aucoin
ADM Scott Swift (PacFleet)
ADM Harry Harris (PACOM)
Analysts (N2)
E.g. Jason Knudson, John
Chu, Jed Raskie, Joseph Baba
Deployers (N3)
Scheduled this week
Planners (N5)
Need to find these people
- Decreased time to predict hot spots, ID & differentiate threats
- Good UI for operators, decision-makers
- Timely, episodic persistent coverage with easily-deployed system
- Cost savings with respect to existing solutions
- Prototype operability + demonstrated scalability
Hardware
- Acquire initial sensor platform with single desired capability
- Design deployment strategy + platform
- Deploy pilot in operational environment
- Develop fabrication/procurement pipeline + cost models for scaling
Software
- Determine most useful data interface for analysts
- Determine optimal information flow to strategic decision makers
- Develop ML and visualization algorithms
- Build, Test, and Deploy Product
Fixed
- Buying proprietary data
- Software tools
- Hardware evaluation + prototyping
equipment
- Evaluation of commercial products
Prototyping
- Existing sensor platforms
- Existing deployment platforms
- Academic research
Scaling
- Available commercial + military data
- Existing database tools (Palantir,
AWS)
- Need demand from operators and
deployment personnel in 7th Fleet
- Need commanding officer to
confirm decision-making benefits
- Need intelligence officers from
ONI / N2 to confirm effectiveness of
insights
- Need IT approvals to integrate into
systems
- Need support of commercial
partners if want to leverage their
platforms
Beneficiaries
Mission Achievement
Mission Budget/Costs
Buy-In
Deployment
Value
Proposition
Key Activities
Key Resources
Key Partners
Military
- 7th Fleet + designated sponsor
- Naval Postgraduate School (NPS)
- Office of Naval Research (ONR)
- Acquisition Personnel
Commercial
- Distributed sensor platform
companies (i.e. Saildrone, AMS)
- Data analytics (i.e. Palantir, Google)
- Advanced manufacturing
Academic
- Universities (i.e. University of Hawaii)
- National Labs (Lincoln Labs, Sandia)
Other
- IUU fishing + anti-smuggling
stakeholders (i.e. Coast Guard, PNA)
Mission: Provide Cost-Effective, Actionable Intelligence at All Times
Testing
- 7th Fleet assets for pilot
- Research barge
- Access to model analyst data
interface
Variable
- Travel for site visits, pilots
- R&D personnel
- Manufacturing/Development
IMPROVE TACTICAL AND
STRATEGIC DECISION
MAKING VIA BETTER DATA
HANDLING
(1) Rapid Strategic
Decisionmaking via Improved
Reporting
(2) Improved Tactical Decision
Making via Enhanced
Information Sharing
(3) More Effective Analysis via
Searchable, Visualizable Data
Integration
ENHANCE INCOMING DATA
STREAMS
(1) Improved Collection of
Existing Data Streams (e.g.
Fishing Broadcasts)
(2) Predictive Intel through
Machine Learning
Additional Sensing Capability
56. Research
- Interviews to assess needs,
organizational dynamics,
procurement strategy
- Site visits to see current practices
-Understanding current workflow
Prototype
- Evaluate existing sensor
platforms with commercial partners
- Integrate sensor feeds of interest
into prototype platform
- Compile existing data resources
- Create representative “fake”
datasets
- Evaluate relevant ML
algorithms for prediction and
rules for push alerts
- Iterate on human-machine
interaction
Strategic Decision Makers
VADM Joseph Aucoin
ADM Scott Swift (PacFleet)
ADM Harry Harris (PACOM)
Analysts (N/J2)
E.g. Jason Knudson, John
Chu, Jed Raskie, Joseph Baba
Operators (N/J3)
CDR Chris Adams (7th Fleet)
Planners (N/J5)
Need to find these people
- Common and consistent view of the Area of Responsibility (AOR)
- Timely operational decisions
- Decreased time to predict hot spots, ID & differentiate threats
- Reduced time for analysts to find information and draw conclusions
- Prototype operability + demonstrated scalability
Data Fusion/Sensor Integration Software (THIS SECTION IS A
WORK IN PROGRESS!)
- Build solution that integrates with current systems (e.g. GCCS)
- Work with PMs and key influencers to determine optimal
funding/dissemination avenues
- Deploy prototype, confirm buy-in and update features
- Scale deployment, improve product as necessary
Fixed
- Buying proprietary data
- Software tools
- Hardware evaluation + prototyping
equipment
- Evaluation of commercial products
Prototyping
- Existing sensor platforms and feeds
- Existing deployment platforms
- Academic research
- Existing data fusion platforms
Scaling
- Available commercial + military data
- Existing database tools (Palantir,
AWS)
- Need commanding officer to
confirm decision-making benefits
- Need intelligence officers from
ONI / N2 and operators from N3 to
confirm effectiveness of insights
- Need IT approvals to integrate into
systems
- Need support of commercial
partners if want to leverage their
platforms
Beneficiaries
Mission Achievement
Mission Budget/Costs
Buy-In
Deployment
Value
Proposition
Key Activities
Key Resources
Key Partners
Military
- 7th Fleet + designated sponsor
- Naval Postgraduate School (NPS)
- Office of Naval Research (ONR)
- Acquisition Personnel
Commercial
- Distributed sensor platform
companies (i.e. Saildrone, AMS)
- Data analytics (i.e. Palantir, Google)
Academic
- Universities (i.e. University of Hawaii)
- National Labs (Lincoln Labs, Sandia)
Other
- IUU fishing + anti-smuggling
stakeholders (i.e. Coast Guard, PNA)
- Disaster relief agencies
Mission: Enabling Rapid, Well-Informed Decisions from Heterogeneous Data
Testing
- 7th Fleet assets for pilot
- Research barge
- Access to model analyst data
interface
- Access to sample incoming sensor
feeds
Variable
- Travel for site visits, pilots
- R&D personnel
- Manufacturing/Development
IMPROVE TACTICAL AND
STRATEGIC DECISION
MAKING VIA BETTER DATA
HANDLING
(1) Rapid Strategic
Decisionmaking via Improved
Reporting
(2) Improved Tactical Decision
Making via Enhanced
Information Sharing
(3) More Effective Analysis via
Searchable, Visualizable Data
Integration
(4) Predictive Intel and Alerts
(e.g. Machine Learning)
ENHANCE INCOMING DATA
STREAMS
(1) Improved Collection of
Existing Data Streams (e.g.
Fishing Broadcasts)
(2) Painless Incorporation of
Multiple New Sensing
Modalities
57. Research
- Interviews to assess needs,
organizational dynamics,
procurement strategy
- Site visits to see current practices
-Understanding current workflow
Prototype
- Integrate sensor feeds of interest
into prototype platform
- Compile existing data resources
- Create representative “fake”
datasets
- Evaluate relevant ML algorithms
for prediction and rules for push
alerts
- Iterate on human-machine
interaction
Strategic Decision Makers
VADM Joseph Aucoin
ADM Scott Swift (PacFleet)
ADM Harry Harris (PACOM)
Analysts (N/J2)
E.g. Jason Knudson, John
Chu, Jed Raskie, Joseph Baba
Operators (N/J3)
CDR Chris Adams (7th Fleet)
Planners (N/J5)
Jose Lepesuastegui (N25)
- Common and consistent view of the Area of Responsibility (AOR)
- Timely operational decisions
- Decreased time to predict hot spots, ID & differentiate threats
- Reduced time for analysts to find information and draw conclusions
- Prototype operability + demonstrated scalability
Data Fusion/Sensor Integration Software (THIS SECTION IS A
WORK IN PROGRESS!)
- Build solution that integrates with current systems (e.g. GCCS,
QUELLFIRE, FOBM)
- Work with PMs and key influencers to determine optimal
funding/dissemination avenues
- Deploy prototype, confirm buy-in and update features
- Scale deployment, improve product as necessary
Fixed
- Buying proprietary data
- Software tools
- Evaluation of commercial products
Prototyping
- Existing sensor platforms and feeds
- Academic research
- Existing data fusion platforms
Scaling
- Available commercial + military data
- Existing database tools (Palantir,
AWS)
- Need commanding officer to
confirm decision-making benefits
- Need intelligence officers from
ONI / N2 and operators from N3 to
confirm effectiveness of insights
- Need IT approvals to integrate into
systems
- Need support of commercial
partners if want to leverage their
platforms
-Need support of existing PMOs
to make sure we’re not
duplicating work
Beneficiaries
Mission Achievement
Mission Budget/Costs
Buy-In
Deployment
Value
Proposition
Key Activities
Key Resources
Key Partners
Military
- 7th Fleet + designated sponsor
- NPS/ONR
- Acquisition Personnel
- Existing PORs (Insight, PMW-150,
Quellfire, SeaVision, FOBM)
Commercial
- Distributed sensor platform
companies (i.e. Saildrone, AMS)
- Data analytics (i.e. Palantir, Google)
Academic
- Universities (i.e. University of Hawaii)
- National Labs (Lincoln Labs, Sandia)
Other
- IUU fishing + anti-smuggling
stakeholders (i.e. Coast Guard, PNA)
- Disaster relief agencies
Mission: Enabling Rapid, Well-Informed Decisions from Heterogeneous Data
Testing
- 7th Fleet assets for pilot
- Research barge
- Access to model analyst data
interface
- Access to sample incoming sensor
feeds
Variable
- Travel for site visits, pilots
- R&D personnel
-Development
IMPROVE TACTICAL AND
STRATEGIC DECISION
MAKING VIA BETTER DATA
HANDLING
(1) Rapid Strategic
Decisionmaking via Improved
Reporting and Coordination
(2) Improved Tactical Decision
Making via Timely, Accurate
Information Sharing
(3) More Effective Analysis via
Searchable, Visualizable Data
Integration (Layering &
Filtering)
(4) Predictive Intel and Alerts
(e.g. Machine Learning)
ENHANCE INCOMING DATA
STREAMS
(1) Improved Collection of
Existing Data Streams (e.g.
Fishing Broadcasts)
(2) Painless Incorporation of
Multiple New Sensing
Modalities
(3 Integration of Incoming Data
Streams with Existing Object-
Oriented Database
58. Research
- Interviews to assess needs,
organizational dynamics,
procurement strategy
- Site visits to see current practices
-Understanding current workflow
Connecting People and
Programs
- Ensuring tool developers and
users are aware of one another
- Finding functional gaps to fill
Prototype
- Compile existing data resources
- Create representative “fake”
datasets
- Evaluate relevant ML algorithms
for prediction/rules for push alerts
- Iterate on human-machine
interaction
Strategic Decision Makers
VADM Joseph Aucoin
ADM Scott Swift (PacFleet)
ADM Harry Harris (PACOM)
Analysts (N/J2)
E.g. Jason Knudson, John
Chu, Jed Raskie, Joseph Baba
Operators (N/J3)
CDR Chris Adams (7th Fleet)
Planners (N/J5)
Jose Lepesuastegui (N25)
- Common and consistent view of the Area of Responsibility (AOR)
- Timely operational decisions
- Decreased time to predict hot spots, ID & differentiate threats
- Reduced time for analysts to find information and draw conclusions
- Prototype operability + demonstrated scalability
Data Fusion/Sensor Integration Software
- Build solution that integrates with current systems (e.g. GCCS,
QUELLFIRE, FOBM, EWBM, INSIGHT)
- Work with PMs and key influencers to determine optimal
funding/dissemination avenues and integration with current tool
pipeline
- Deploy prototype, confirm buy-in and update features
- Scale deployment, improve product as necessary
Fixed
- Buying proprietary data
- Software tools
- Evaluation of commercial products
Prototyping
- Existing sensor platforms and feeds
- Academic research
- Existing data fusion platforms
Scaling
- Available commercial + military data
- Existing database tools (Palantir,
AWS)
- Need commanding officer to
confirm decision-making benefits
- Need intelligence officers from
ONI / N2 and operators from N3 to
confirm effectiveness of insights
- Need IT approvals to integrate into
systems
- Need support of commercial
partners if we want to leverage their
platforms
-Need support of existing
PMOs/S&T personnel to make
sure we’re not duplicating work
Beneficiaries
Mission Achievement
Mission Budget/Costs
Buy-In
Deployment
Value
Proposition
Key Activities
Key Resources
Key Partners
Military
- 7th Fleet + designated sponsor
- NPS/ONR
- Acquisition Personnel
- Existing PMOs/PORs
- Other Fleets
Commercial
- Distributed sensor platform
companies (i.e. Saildrone, AMS)
- Data analytics (i.e. Palantir, Google)
Academic
- Universities (i.e. University of Hawaii)
- National Labs (Lincoln Labs, Sandia)
Other
- IUU fishing + anti-smuggling
stakeholders (i.e. Coast Guard, PNA)
- Disaster relief agencies
Mission: Enabling Rapid, Well-Informed Decisions from Heterogeneous Data
Testing
- 7th Fleet assets for pilot
- Research barge
- Access to model analyst data
interface and in-development tools
- Access to sample incoming sensor
feeds
Variable
- Travel for site visits, pilots
- R&D personnel
-Development
IMPROVE TACTICAL AND
STRATEGIC DECISION
MAKING VIA BETTER DATA
HANDLING
(1) Rapid Strategic
Decisionmaking via Improved
Reporting and Coordination
(2) Improved Tactical Decision
Making via Timely, Accurate
Information Sharing
(3) More Effective Analysis via
Searchable, Visualizable Data
Integration (Layering &
Filtering)
(4) Predictive Intel and Alerts
(e.g. Machine Learning)
ENHANCE INCOMING DATA
STREAMS
(1) Improved Collection of
Existing Data Streams (e.g.
Fishing Broadcasts)
(2) Painless Incorporation of
Multiple New Sensing
Modalities
(3 Integration of Incoming Data
Streams with Existing Object-
Oriented Database
59. Research
- Interviews to assess needs,
organizational dynamics,
procurement strategy
- Site visits to see current practices
-Understanding current workflow
Connecting People and
Programs
- Ensuring tool developers and
users are aware of one another
- Finding functional gaps to fill
Prototype
- Compile existing data resources
- Create representative “fake”
datasets
- Evaluate relevant ML algorithms
for prediction/rules for push alerts
-Create demo of flexible data
fusion/analytics for IUU fishing
Strategic Decision Makers
Analysts (N/J2)
Operators (N/J3)
Planners (N/J5)
- Timely operational decisions
-Common and consistent view of the Area of Responsibility (AOR)
=Flexible integration of new feeds into COP and analytics
- Decreased time to predict hot spots, ID & differentiate threats
- Reduced time for analysts to find information and draw conclusions
- Prototype operability + demonstrated scalability
Data Fusion/Sensor Integration Software
- Build solution that integrates with current systems (e.g. GCCS,
QUELLFIRE, FOBM, EWBM, INSIGHT)
- Work with PMs and key influencers to determine optimal
funding/dissemination avenues and integration with current tool
pipeline
- Deploy prototype, confirm buy-in and update features
- Scale deployment, improve product as necessary
Fixed
- Buying proprietary data
- Software tools
- Evaluation of commercial products
Prototyping
- Existing sensor platforms and feeds
- Academic research
- Existing data fusion platforms
Scaling
- Available commercial + military data
- Existing database tools (Palantir,
AWS)
- Need commanding officer to
confirm decision-making benefits
- Need intelligence officers from
ONI / N2 and operators from N3 to
confirm effectiveness of insights
- Need IT approvals to integrate into
systems
- Need support of commercial
partners if we want to leverage their
platforms
-Need support of existing
PMOs/S&T personnel to make sure
we’re not duplicating work
Beneficiaries
Mission Achievement
Mission Budget/Costs
Buy-In
Deployment
Value
Proposition
Key Activities
Key Resources
Key Partners
Military
- 7th Fleet + designated sponsor
- NPS/ONR
- Acquisition Personnel
- Existing PMOs/PORs
- Other Fleets
Commercial
- Distributed sensor platform
companies (i.e. Saildrone, AMS)
- Data analytics (i.e. Palantir, Google)
Academic
- Universities (i.e. University of Hawaii)
- National Labs (Lincoln Labs, Sandia)
Other
- IUU fishing + anti-smuggling
stakeholders (i.e. Coast Guard, PNA)
- Disaster relief agencies
Mission: Enabling Rapid, Well-Informed Decisions from Heterogeneous Data
Testing
- 7th Fleet assets for pilot
- Research barge
- Access to model analyst data
interface and in-development tools
- Access to sample incoming sensor
feeds
Variable
- Travel for site visits, pilots
- R&D personnel
-Development
IMPROVE TACTICAL AND
STRATEGIC DECISION
MAKING VIA BETTER DATA
HANDLING
(1) Rapid Strategic
Decisionmaking via Improved
Reporting and Coordination
(2) Improved Tactical Decision
Making via Timely, Accurate,
Information Sharing
(3) More Effective Analysis via
Searchable, Visualizable,
Source-Flexible Data
Integration (Layering &
Filtering)
(4) Predictive Intel and Alerts
(e.g. Machine Learning) Flexibly
Applied to Available Data and
Rapidly Updateable to Account
for New Sources
ENHANCE INCOMING DATA
STREAMS
(1) Improved Collection of
Existing Data Streams (e.g.
Fishing Broadcasts)
(2) Painless Incorporation of
Multiple New Sensing
Modalities
(3 Integration of Incoming Data
Streams with Existing Object-
Oriented Database
60. Data & Analytics
- Compile existing data
resources/scope out future ones
- Develop flexible data
fusion/analytics algorithms
Defining C2-F
- Brainstorming what “Command
and Control of the Future” (C2-F or
“MTC2-F”) would be
- Interviewing (customer discovery)
for younger sailors
Software Development
Prototype Testing/Acquisitions
Pursue Information Assurance
Certification
USN Strategic Decision
Makers
USN Analysts (N/J2)
USN Operators (N/J3)
Anti-IUU Fishing Enforcers
(USCG, Partner Nations,
etc.)
Anti-IUU Fishing
Stakeholders (NGOs, Legal
Fishing)
(Commercial entities that
use/would benefit from
enhanced C2-type systems)
USN
- Timely, accurate operational decisions
- Decreased time to predict hot spots, ID & differentiate threats
- Increased engagement and effectiveness of younger sailors
- Up-to-date, reliable info in frontline environment
Anti-IUU Fishing
- Reduction in IUU fishing worldwide due to better deterrence
- Better allocation of scarce / expensive interdiction resources
- Widespread engagement of operators, governments, and the public
USN
- Work with fleet sponsor to get C2-F system on fleet needs list
- Ensure C2-F makes it into FIMES database, engage S&T bridge
personnel to talk with key decision makers
- Work with NWDC, ONR S&T, PACFLT LOEs to test solution
- Engage PACFLT N8/N9 shops to implement modular operational
deployment & update pathways
Anti IUU Fishing
- Work with NGOs, gov’t departments, USCG, operators, etc. to find
key influencers/stakeholders
- Deploy solution where possible,
Fixed
- Existing Software tools/APIs
- Evaluation of commercial products
- Information assurance process
steps
Data & Analytics
- APIs for accessing data (e.g. API for
Global Fishing Watch, AIS), $$$
needed to access this
Defining C2-F
-Ideas/feedback from young sailors
- Need commanding officer to
confirm decision-making benefits
- Need intelligence officers from
ONI / N2 and operators from N3 to
confirm effectiveness of insights
- Need IT approvals to integrate into
systems
- Need support of commercial
partners if we want to leverage their
platforms
-Need support of existing
PMOs/S&T personnel to make sure
we’re not duplicating work
Beneficiaries
Mission Achievement
Mission Budget/Costs
Buy-In
Deployment
Value
Proposition
Key Activities
Key Resources
Key Partners
Military
- PACFLT (7th/3rd Fleet, young E- and
O- who use current C2 tools)
- Program Office for MCT2 (PMW 150)
- Information Assurance Personnel
- NWDC, ONR S&T Advisors, C7F N2,
C7F CIG, C3F N8/9, PACOM CSIG,
OPNAV N2/N6 (Acquisition/Testing)
Anti-IUU Fishing Stakeholders
- IUU fishing + anti-smuggling
stakeholders (i.e. Coast Guard, PNA)
Data/Software/Algorithms
- Data: Skytruth, Pew, Global Fishing
Watch, Capella, TerraSAR
-Software: Palantir Skytruth, USCG,
NPS/ONR, SeaVision, Sea Scout
-Algorithms: Universities (e.g.
Vanderbilt), NPS/ONR, NGOs
Mission: Creating C2-F--Enabling Rapid Decisions from Heterogeneous Data
Software Development
-AWS, programmers, $$$ for both,
subject matter expertise on
phenomenology of ships, activities
Prototype Testing/Acquisition
- Military Sealift Command ships, 7th
Fleet experimentation ships and
personnel
Information Assurance Certification
-Access to personnel to provide
certification / approval
Variable
- Travel for site visits, pilots,
interviews with sailors
- R&D personnel
- Development
- Data and APIs
- AWS & Distributed Computing
IMPROVE USN DECISIONS &
OPS VIA C2-F WITH IMPROVED
DATA HANDLING, UI/UX,
COMMS, AND HARDWARE
(1) Rapid Strategic
Decisionmaking via Improved
Reporting, Coordination, Visibility
(2) Improved Tactical Decision
Making via Timely, Accurate
Information Sharing
(3) More Effective Analysis via
Searchable, Visualizable, Source-
Flexible Data Integration
(Layering & Filtering)
(4) Increased Analyst Bandwidth
via Predictive Intel and Alerts (e.g.
Machine Learning) Flexibly
Applied to Available Data
(5) Improved Collection of
Existing Data Streams
(6) Increasing Morale &
Engagement for Millenial Sailors
ENHANCE ANTI-IUU FISHING
CAPABILITIES
(1) Improved Detection Using
Data Fusion/Analytics
(2) Enhanced Enforcement via
Improved Communication
(3) Lower Barriers to Engaging
Civilians in Reporting IUU Fishing
Activities
61. Data
- Compile existing data
resources/scope out future ones
Defining C2-F
- Brainstorming what “Command
and Control of the Future” would
be by interviewing younger sailors
Software Development
- Develop flexible data
fusion/analytics algorithms, and an
intuitive UI for millennials
Information Assurance
Prototype Testing/Procurement
Contracting, Acquisitions
Maintenance and Support
USN Strategic Decision
Makers
USN Analysts (N/J2)
USN Operators (N/J3)
Anti-IUU Fishing Enforcers
(USCG, Partner Nations,
etc.)
Anti-IUU Fishing
Stakeholders (NGOs, Legal
Fishing)
(Commercial entities that
use/would benefit from
enhanced C2-type systems)
USN
- Timely, accurate operational decisions
- Decreased time to predict hot spots, ID & differentiate threats
- Increased engagement and effectiveness of younger sailors
- Up-to-date, reliable info in frontline environment
Anti-IUU Fishing
- Reduction in IUU fishing worldwide due to better deterrence
- Better allocation of scarce / expensive interdiction resources
- Widespread engagement of operators, governments, and the public
USN
- Work with fleet sponsor to get C2-F system on fleet needs list
- Ensure C2-F makes it into FIMS database, engage S&T bridge
personnel to talk with key decision makers
- Work with NWDC, ONR S&T, PACFLT LOEs to test solution
- Engage PACFLT N8/N9 shops to implement modular operational
deployment & update pathways
Anti IUU Fishing
- Work with NGOs, gov’t departments, USCG, operators, etc. to find
key influencers/stakeholders
- Deploy solution where possible,
Fixed
- Existing Software tools/APIs, Data
- IA process steps
- Travel for site visits, pilots,
interviews with sailors
- R&D personnel
- AWS & Distributed Computing
- Overhead
Data & Analytics
APIs for accessing data (e.g. API for
Global Fishing Watch, AIS), $$$
needed to access this
Defining C2-F
Ideas/feedback from young sailors
Hackathon w/ Navy and DIUx support
Software Development
AWS, programmers, $$$ for both, SME
on phenomenology of ships, activities
- Need commanding officer to
confirm decision-making benefits
- Need intelligence officers from
ONI / N2 and operators from N3 to
confirm effectiveness of insights
- Need IT approvals to integrate into
systems
- Need support of commercial
partners if we want to leverage their
platforms
-Need support of existing
PMOs/S&T personnel to make sure
we’re not duplicating work
Beneficiaries
Mission Achievement
Mission Budget/Costs
Buy-In
Deployment
Value
Proposition
Key Activities
Key Resources
Key Partners
Data
Skytruth, Pew, GFW, TerraSAR
Defining C2-F
7th,3rd Fleet junior officers, sailors
Software development
Palantir Skytruth, NPS/ONR,
SeaVision, Sea Scout, Universities (e.g.
Vanderbilt), NGOs
Information Assurance
GSA, NWDC
Prototype Testing/Procurement
USFF (NWDC), NAVSEA, SPAWAR,
C7F CIG, PACFLT CSIG, IA contact
Contracting, Acquisitions
-IP Lawyer, subs with gov experience
-DIUx, C3F N8/9, PACFLT N8/N9
Mission: Creating C2-F--Enabling Rapid Decisions from Heterogeneous Data
Information Assurance
Access to personnel to provide
certification / approval
Prototype Testing/Acquisition
Navy testing venue and exercise
(e.g. Trident Warrior), Military Sealift
Command ships, 7th Fleet
experimentation ships and
personnel
Contracting, Acquisitions
Domain knowledge of software
contracting and IP from lawyers,
subs
Variable
- Maintenance and Support
- Integration with existing
systems and processes
IMPROVE USN DECISIONS &
OPS VIA C2-F WITH IMPROVED
DATA HANDLING, UI/UX,
COMMS, AND HARDWARE
(1) Rapid Strategic
Decisionmaking via Improved
Reporting, Coordination, Visibility
(2) Improved Tactical Decision
Making via Timely, Accurate
Information Sharing
(3) More Effective Analysis via
Searchable, Visualizable, Source-
Flexible Data Integration
(Layering & Filtering)
(4) Increased Analyst Bandwidth
via Predictive Intel and Alerts (e.g.
Machine Learning) Flexibly
Applied to Available Data
(5) Improved Collection of
Existing Data Streams
(6) Increasing Morale &
Engagement for Millenial Sailors
ENHANCE ANTI-IUU FISHING
CAPABILITIES
(1) Improved Detection Using
Data Fusion/Analytics
(2) Enhanced Enforcement via
Improved Communication
(3) Lower Barriers to Engaging
Civilians in Reporting IUU Fishing
Activities
63. Learning Progression: Week 1
● Week 1
○ Hypotheses
■ This is a problem with insufficient sensing
○ Experiments:
■ Conversations with mentors/stakeholders/contacts
○ Learning:
■ Sensors largely exist, but price point can be too high
■ Government struggles with sheer volume of open-source data
■ Internal information sharing is a big problem
■ Episodic persistence is acceptable--24/7 is not required
○ Proposed solution (MVP)
■ Diagram of entire ISR infrastructure with an emphasis on data aggregation
○ Key Takeaways:
64. Learning Progression: Week 2
● Week 2
○ Hypotheses
■ Information sharing is a core problem
■ Predictive analytics for hotspots will add value
■ Sensor platforms for our needs exist
○ Experiments:
■ Conversations with C7F N2: MOC description (reservist), data fusion skepticism/deployment
emphasis (N2 Chief C7F), maybe use partner nation radar (IUU fishing operator)
○ Learning:
■ 7th Fleet wants details about surface ships--A2/AD is a problem because they can’t deploy
normal sensor packages
■ Predicting hotspots is not useful--they know where these are!
■ Information sharing within the Maritime Operations Center (MOC) is not optimal
■ Sensors exist, but cannot be deployed in timely fashion!
65. Learning Progression: Week 3
● Week 3
○ Hypotheses
■ Sensor deployment is the major issue
○ Experiments:
■ Visit to NPS
○ Learning:
■ N3 (Ops) owns N2 and N6--we had only been speaking to N2
■ Pete: what decisions are people actually trying to make?
■ Ship-based radar is all that’s automated--data fusion is very manual!
■ Our problem came from PACOM->PACFLT->7th Fleet...affects how we think about it
■ Lots of single-purpose data fusion tools exist--don’t fall into that trap--how do you do modular
updates without creating single-use tools?
■ There are specific systems (GCCS) that we should be thinking about learning more about
66. Learning Progression: Week 4
● Week 4
○ Hypotheses
■ Data fusion/aggregation is the problem--need to find out more about specific needs
○ Experiments:
■ Conversations with variety of stakeholders (N0, N2, N3, N6, J5, J8, etc.)
○ Learning:
■ PACOM, PACFLT, 7th Flt see different things--COP is not really a COP
■ Analysts do data layering manualy on GCCS, and there’s usually too much there to be useful
■ Automation would be helpful (and our own algorithms could be useful)
■ Common, easily searchable database would be desirable
■ Don’t actually care that much about A2/AD! Subset of a bigger problem!
○ Proposed solution (MVP)
■ Updated previous MVP--now we include automated push alerts and clickable vessel-specific
67. Learning Progression: Week 5
● Week 5
○ Hypotheses
■ We have identified a clear problem with data fusion, interaction--need to understand exactly
where the biggest pain points are, map customer workflow precisely, think about acquisiton
paths
○ Experiments:
■ Visit to USCG ops center
■ Acquisition discussions with C7F sponsor
■ Interviews with GCS users/operators and GCCS support contractors
○ Learning:
■ Customer workflow nailed down (JIOC SWO)
■ Functional org chart nailed down
■ Navy POR acquisition path laid out
■ POR-POR interaction within C2 system mapped Proposed solution (MVP)
68. Learning Progression: Week 6
● Week 6
○ Hypotheses
■ The overall C2 system needs to be modernized--there are programs in existence to do this
○ Experiments:
■ Send out spreadsheet to contact list, get understanding of awareness of current programs
■ Speak with ONR research staff about existing programs
○ Learning:
■ Intel is not the same as COP
■ Most users do not have a good sense of which programs already exist for modernizing various
parts of the C2 system (Quellfire, EWBM, ADAPT, etc.)
■ Very difficult to get UNCLASS-level details on C2 programs
■ anti-IUU Fishing (Illegal, Unregulated, Unreported) is a great analog use case for desirable Navy
C2 functionality
○ Proposed solution (MVP)
69. Learning Progression: Week 7
● Week 7
○ Hypotheses
■ Flexible integration of heterogeneous new sensor feeds into COP would be useful
■ IUU problem is a good analog for Navy COP
○ Experiments:
■ Interviews with PACOM COP/GCCS experts, IUU fishing stakeholders
○ Learning:
■ Wide variety of sensor feeds (drones, social media, etc.) exist that cannot be effectively
integrated into GCCS/COP
○ Proposed solution (MVP)
■ MarineTraffic.com/Global Fishing Watch--would capabilities like this be of use to the Navy?
○ Key Takeaways:
70. Learning Progression: Week 8
● Week 8
○ Hypotheses
■ New COP product is a worthwhile direction to go--acquisition and testing will be best done
through 3rd Fleet
○ Experiments:
■ Conversations with C3F
○ Learning:
■ Trident Warrior/NWDC are the best organizations to engage for testing/evaluation
■ Information assurance is an important step in the deployment process
■ Requirements are sourced from the fleets, acquisition occurs via SecDef budget
■ Substantial demand for IUU fishing-type technology in CIC--clickable order of battle for different
ships
■ They want our week 4 MVP!
○ Proposed solution (MVP)
71. Learning Progression: Week 9
● Week 1
○ Hypotheses
■ This is a problem with insufficient sensing
○ Experiments:
■ Conversations with mentors/stakeholders/contacts
○ Learning:
■ Sensors largely exist, but price point can be too high
■ Government struggles with sheer volume of open-source data
■ Internal information sharing is a big problem
■ Episodic persistence is acceptable--24/7 is not required
○ Proposed solution (MVP)
■ Diagram of entire ISR infrastructure with an emphasis on data aggregation
○ Key Takeaways:
74. Data Acquisition
Contextualized
Database
Week 2 MVP: Sensor Deployment System
Deployment
Last Month
Today
Object-oriented
Database
Query
- What data is most useful to capture?
- What sensor modalities can capture?
- What products exist?
- What deployment options exist?
- What is easiest to deploy?
- What is “good-enough” time to data
acquisition?
- What is the deployment process?
- Is .kmz format all that is necessary for
compatibility?
- What do companies like Palantir do
today?
78. Week 4 MVP: Vessel-Level Info & Predictive Analytics
79. Week 5 MVP
Modular Device
● Local storage of historical data→less bandwidth usage + ability to do better
pattern recognition, alerts
GCCS /
ADS
80. Week 6 - MVP: “Software Domain Awareness”
Program POC Organization
Function &
Goals
To be used
by whom?
Security
Level Status Contract History Inputs
Technical
Details
CSII
Insight
MTC2
Quellfire
DCGS-N
Increment 2
C2PC
HAMDD
SeaVision
GCCS
EWBM
RC2
(Resilient
C2)
81. Week 7 MVP: Modular Intake, Algorithm, and Display
82. Week 7 MVP: Modular Intake, Algorithm, and Display
83. Week 7 MVP: Modular Intake, Algorithm, and Display
84. Week 7 MVP: Modular Intake, Algorithm, and Display
85. Week 8 MVP: Shareable Data & Analytics
CIC PACOM
Surface radar contact
but no AIS… This is
odd. Let me ALERT
others.
86. Week 8 MVP: Shareable Data & Analytics
CIC PACOM
Surface radar contact
but no AIS… This is
odd. Let me ALERT
others.
I see an ALERT from
DDG102. Lets share
the C2 screen and
take a look
87. Week 8 MVP: Shareable Data & Analytics
CIC PACOM
88. Week 8 MVP: Shareable Data & Analytics
CIC PACOM
94. Core Navy Procurement Process
PACOM
To win a war, we need to have awareness of potential adversary's
disposition of forces within the area we intend to operate and be able
to maintain that through all phases of the conflict (Joint Intelligence
Preparation of the Environment)
PACFLT Use the Navy in 3rd and 7th Fleet to conduct JIPOE
7th Fleet Direct ships, aircraft, submarines, marines, and other sensors to
conduct JIPOE
7th Fleet
N2
Task, Collect, Process, Exploit, and Disseminate and maintain JIPOE
for C7F
7th Fleet
N2, LT
Knudson
Identify potential operational gaps and determine possible ways to fill
those gaps
1. Operational Requirements flow down from PACOM and is interpreted at each level:
Operational
Requirements
95. USFF
PACFLT
7th Fleet Do I have the tools to accomplish my Operational Requirement?
Yes No
YAY, Done
Does PACFLT have the money and/or resources to fund it?
Send Acquisitions Requirement to PACFLT
Yes No
YAY. Validated and resourced.
Done.
PACFLT “endorses” requirement, sends to US Fleet
Forces Command
Is USFF able to fund or resource this requirement?
Yes No
YAY. Validated and resourced.
Done.
Send to OPNAV
OPNAV Is there an existing Program of Record?
No
YAY. Done
Make new POR and include in Navy’s budget via
SECNAV, SECDEF.. Send to Congress.
Congress Budget approved?
Yes
Acquisition
Requirements
96. Congress Budget approved?
Yes
OPNAV
PMO
USFF
Force
Commands
PACFLT
7th FLEET
Money flows from SECDEF to SECNAV to CNO/OPNAV
Primes/ NAC
Program Management Office decides who to tap for production/development
A government contractor (Boeing, Lockheed, etc.) or Naval Acquisition Command
(SPAWAR, NAVSEA, etc.) builds this system
Product made available to US Fleet Forces Command to issue to Navy units
SURFFOR, SUBFOR, and IFOR man, train, and equip using 2-year money
No GG
PACFLT receives resources from the appropriate force command
7th FLEET GETS SOMETHING!!!! …. Many YEARS later…. YAY!!!!
Program
Execution
97. Customer Discovery - Get/Keep/Grow Diagram
Awareness Interest Consideration Purchase Keep Unbundling Up-sell Cross-sell Referral
Activity
&
People
- Evangelist &
advocate from
originator Flt
- ???
Corey
Hesselberg,
CDR Jason
Schwarzkopf,
MIOC watch
standers
- Buy-in from
flag officers
- ADM Swift,
VADM Aucoin,
RADM Piersey
- N8/9
- Dave
Yoshihara
(PacFlt N9)
- 7th Fleet ???
- Maintainers
(N6)
- Bob Stevenson
(PacFlt N6)
- 7th Fleet ???
N/A Expanding
COP & intel
extensions /
functionality
within 7th
Fleet
Expanding
user base
within 7th
Fleet
Expanding
tool set to
other fleets
Metrics % people who
have heard of
program
before vs after
*how to
reassess?
# people who
say “we want
this”
Seems
binary… any
recommendati
ons?
# Systems
outfitted
?? ?? ?? # users within
7th Fleet using
tool
# fleets using
tool
99. Week 8 - MVP?
Modular Device
● Local storage of historical data→less bandwidth usage + ability to do better
pattern recognition, alerts
GCCS /
ADS
100. Week 8 - MVP? Deployment Method!
Modular Device
● Local storage of historical data→less bandwidth usage + ability to do better
pattern recognition, alerts
“C2-F”
101. Cost Flows
Database ($80k)
Analytics Engine
($120k)
Translation (ETLs) ($100k)
AIS VMS Radar SAR Sat
UI ($80k)
Information
Assurance
($240k)
Testing
($480k)
Maintenance
and Support
(VC)
Assume 10 data streams, need
cost validation on streams
$380K $240K $480K $???
Total: $1.1 MM + Var Costs
102. Customer Discovery DeploymentProduct Development Navy
Testing
Initial
Testing
Information
Assurance
Maintenance
& Support
Key Activities, Resources, and Partners
TRL 1 TRL 2 TRL 3 TRL 4 TRL 5 TRL 6 TRL 7 TRL 8 TRL 9
103. 3 Year Financial/Ops/Funding Timeline
2016 2017 2018 2019
Q3 Q4 Q1 Q2
Cash
Reserves
Phase
ProductGov’tCom’l
Milestones
Q1 Q2Q3 Q4 Q1 Q2Q3 Q4
TRL
1
TRL
2
TR
L 3
TRL 4
TRL 5
TRL 6
TRL 7
TRL 8
TRL 9
POC
Wireframe
Prototype
Beta
Prototype
Marketable
Product
Beta
Prototype
Released to first
customers (<3)
Commercial
Product Launch;
2 contracts
Test in Navy env Navy-wide Deployment Maintenance and Support
V2.0 Commercial
Product Launch;
5 contracts
signed
Initialize
System Development/
Customer Relationship Development Launch at scale
Head
count
4
10
20
50
15
customers;
V2.5 launch
CONTRACT
SIGNED CONTRACT
RENEWED
SBIR/
DIUx
Series A
(In-Q-Tel,
Impact
Investor(s))
2 com’l
contracts
$250k
$0
$1.25M DoD Contract
$2M
$5M
104. 3 Year Financial/Ops/Funding Timeline
2016 2017 2018 2019
Q3 Q4 Q1 Q2
Cash
Reserves
Phase
ProductGov’tCom’l
Milestones
Q1 Q2Q3 Q4 Q1 Q2Q3 Q4
TRL
1
TRL
2
TR
L 3
TRL 4
TRL 5
TRL 6
TRL 7
TRL 8
TRL 9
POC
Wireframe
Prototype
Beta
Prototype
Marketable
Product
Beta
Prototype
Released to first
customers (<3)
Commercial
Product Launch;
2 contracts
Test in Navy env Navy-wide Deployment Maintenance and Support
V2.0 Commercial
Product Launch;
5 contracts
signed
Initialize
System Development/
Customer Relationship Development Launch at scale
Head
count
4
10
20
50
15
customers;
V2.5 launch
CONTRACT
SIGNED CONTRACT
RENEWED
SBIR/
DIUx
Series A
(In-Q-Tel,
Impact
Investor(s))
2 com’l
contracts
$250k
$0
$1.25M DoD Contract
$2M
$5M
CAVEAT:
This is what the timeline would look like if we
worked on the project full time.