Download - Sentinel Week 3 H4D Stanford 2016
Digital and searchable platform that includes latest and greatest intel on Chinese naval vessels from published resources, State Dep’t, NSA, etc
Digital and searchable platform that includes intel on Chinese naval vessels from published resources, State Dep’t, NSA, etc, collected annually
Hard copy paper document that includes intel on Chinese naval vessels from published resources, State Dep’t, NSA, etc, produced annually
Hard copy paper document available on Amazon
Digital and searchable platform that includes latest and greatest intel on Chinese naval vessels from published resources, State Dep’t, NSA, etc
Digital and searchable platform that includes intel on Chinese naval vessels from published resources, State Dep’t, NSA, etc, collected annually
Hard copy paper document that includes intel on Chinese naval vessels from published resources, State Dep’t, NSA, etc, produced annually
Hard copy paper document available on Amazon
Team Sentinel
● Team members:
○ Jared Dunnmon
○ Darren Hau
○ Atsu Kobashi
○ Rachel Moore
● Cumulative # of interviews: 27 + 12
○ Users: 6 Experts: 6
● Our “minimum viable problem”/What we do: Enable more efficient and informed strategic decisions by filling in intelligence gap about surface ships in an A2/AD environment via
○ Increased number of data streams (i.e. incorporate open source data)
○ Automated data aggregation (i.e. from disparate sources) and analysis
○ Enhanced intel through contextualization
○ Improved UI/UX
● Why it matters:
○ A2/AD prevents deployment of traditional ISR
○ Current assets are incapable of providing timely insight throughout 7th Fleet’s operational domain
○ Overall data aggregation platforms in PACOM appear to be extremely manual
● Military Liaisons
○ John Chu (Colonel, US Army)
○ Todd Cimicata (Commander, US Navy)
● Problem Sponsor
○ Jason Knudson (Lieutenant, US Navy 7th Fleet)
● Tech Mentors include:
○ Palantir (TBD)
JIOC
J1 J2 J3
J4 J5
J7
J6
J8 J9
N1 N4 N7N6 N8 N9
VADM Joe Aucoin
ADM Scott Swift
ADM Harry Harris Jr
N2/N39Intel and Info Ops
N3Operations
N5 Planning
N22Op/Intel Overwatch
N23Collection Operations
N391Fleet Cryptology &
Information Operations
N31Current Operations
N32Fleet Oceanographer
N33Future Operations
N34AT/CIP/NWS
N52Fleet Doctrine Strategy
N53Deliberate Plans
Division
N54Maritime Assessments
N55Functional Plans
Division
Director (CPT Greg Husmann)
Deputy Director(CDR Silas Ahn)
Director (CPT Wes Bannister)
Deputy Director(CDR Chris Adams)
Director
Deputy Director
LT Jason Knudson
Directorate (N/J/A/G)
Description
1 Manpower and Personnel
2 Intelligence
3 Operations
4 Logistics, Engineering, Security & Cooperation
5 Planning
6 C4: Command, Control, Communication, Cyber
7 Training & Exercises
8 Resources & Assessments
9 Civil, Military Cooperation
Customer Discovery
Hypotheses Experiments Results Action
Data aggregation + layering is secondary to sensor availability
- Site visit @ NPS (CMDR Breuer, CAPT Verheul, Higgins, Brutzman, Miller)- Interview with Ostrander (U of Hawaii)- NYTimes article
- Extremely keen on solving sharing and aggregation problem- “Ship-based radar is the only part that is automated...AIS is integrated verbally”
- Visit a CG version of a MOC- Speak with Palantir about their products- Evaluate recommended data fusion products such as Pacific Disaster Center
This is a 7th Fleet problem
- Engagement with Knudson- Site visit @ NPS- Interview with LT. COL Oti
- This is a PACOM problem; 7th Fleet was tasked with finding a solution- This impacts not only J2 and J3, but also J5 (planning)
- Speak with PACOM J2, J3, J5
N2/J2 are our ultimate beneficiaries
- Site visit @ NPS- Engagement with Chu- Interview with Oti
- A data aggregation and layering platform would benefit a lot of organizations- N/J3 is the key organization; N/J2 and N/J6 support N/J3- Also impacts work of N/J5
- Interview with Deputy N3 TONIGHT- Speak with N/J3, N/J5
Customer Discovery
Hypotheses Experiments Results Action
Data aggregation + layering is secondary to sensor availability
- Site visit @ NPS (CMDR Breuer, CAPT Verheul, Higgins, Brutzman, Miller)- Interview with Ostrander (U of Hawaii)- NYTimes article
- Extremely keen on solving sharing and aggregation problem- “GCCS is susceptible to garbage-in, garbage-out”
- Visit a CG version of a MOC- Speak with Palantir about their products- Evaluate recommended data fusion products such as Pacific Disaster Center
This is a 7th Fleet problem
- Engagement with Knudson- Site visit @ NPS- Interview with LT. COL Oti
- This is a PACOM problem; 7th Fleet was tasked with finding a solution- This impacts not only J2 and J3, but also J5 (planning)
- Speak with PACOM J2, J3, J5
N2/J2 are our ultimate beneficiaries
- Site visit @ NPS- Engagement with Chu- Interview with Oti
- A data aggregation and layering platform would benefit a lot of organizations- N/J3 is the key organization; N/J2 and N/J6 support N/J3- Also impacts work of N/J5
- Interview with Deputy N3 TONIGHT- Speak with N/J3, N/J5
Customer Discovery
Hypotheses Experiments Results Action
Data aggregation + layering is secondary to sensor availability
- Site visit @ NPS (CMDR Breuer, CAPT Verheul, Higgins, Brutzman, Miller)- Interview with Ostrander (U of Hawaii)- NYTimes article
- Extremely keen on solving sharing and aggregation problem- “The fact that commercial satellite imagery can identify ships is unsettling to Navy”
- Visit a CG version of a MOC- Speak with Palantir about their products- Evaluate recommended data fusion products such as Pacific Disaster Center
This is a 7th Fleet problem
- Engagement with Knudson- Site visit @ NPS- Interview with LT. COL Oti
- This is a PACOM problem; 7th Fleet was tasked with finding a solution- This impacts not only J2 and J3, but also J5 (planning)
- Speak with PACOM J2, J3, J5
N2/J2 are our ultimate beneficiaries
- Site visit @ NPS- Engagement with Chu- Interview with Oti
- A data aggregation and layering platform would benefit a lot of organizations- N/J3 is the key organization; N/J2 and N/J6 support N/J3- Also impacts work of N/J5
- Interview with Deputy N3 TONIGHT- Speak with N/J3, N/J5
Customer Discovery
Hypotheses Experiments Results Action
Data aggregation + layering is secondary to sensor availability
- Site visit @ NPS (CMDR Breuer, CAPT Verheul, Higgins, Brutzman, Miller)- Interview with Ostrander (U of Hawaii)- NYTimes article
- Extremely keen on solving sharing and aggregation problem- “...drives me crazy that every individual use case becomes an opportunity to build a new single-purpose fusion tool”
- Visit a CG version of a MOC- Speak with Palantir about their products- Evaluate recommended data fusion products such as Pacific Disaster Center
This is a 7th Fleet problem
- Engagement with Knudson- Site visit @ NPS- Interview with LT. COL Oti
- This is a PACOM problem; 7th Fleet was tasked with finding a solution- This impacts not only J2 and J3, but also J5 (planning)
- Speak with PACOM J2, J3, J5
N2/J2 are our ultimate beneficiaries
- Site visit @ NPS- Engagement with Chu- Interview with Oti
- A data aggregation and layering platform would benefit a lot of organizations- N/J3 is the key organization; N/J2 and N/J6 support N/J3- Also impacts work of N/J5
- Interview with Deputy N3 TONIGHT- Speak with N/J3, N/J5
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 MakersE.g. CPT Greg Hussman, VADM Joseph AucoinADM 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 AchievementMission 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
Products& Services
- Timely data- Good UI/UX for
presenting data
- Cheaper acquisition- Streamlined
reporting process- Increased coverage
area and persistence Customer Jobs
Gains
Pains
Gain Creators
Pain Relievers
- Improved deployment strategy
- Good UI/UX- Platform incorporates
more data streams
- Allocate assets- Identify, eliminate
threats- Predict hot spots- Safety of team- Projecting peace,
stability in region
- More informed decisions
- Faster decisions
- Poor quality/lack of data- Time consuming system- Latency of data ->
insight
Admiral/Strategic Decision Maker
Value Proposition Canvas
Products& Services
- Contextualized, object-oriented database
- Algorithms for processing, analyzing data
- Ability to search for trends across database
- Faster deployment of sensors
- Integration of data sources
- Automation of data analysis
- Improved UX/UI
Customer Jobs
Gains
Pains
Gain Creators
Pain Relievers
- Contextualized, object-oriented database
- Compatible data format- Incorporate multiple data
streams
- Collect & analyze data
- Communicate findings
- Piece together contextualized awareness
- More actionable insights
- Faster identification & response times
- Easy-to-use
- Incorporation of context is manual/mental
- Poor quality / lack of data- Latency of data -> insight
Analyst (N2)
Value Proposition Canvas
Products& Services
- Low cost, disposable sensors
- Improved deployment strategy
- N/A
- Disposable- Reduced expense- N/A
Customer Jobs
Gains
Pains
Gain Creators
Pain Relievers
- No hardware to deploy so no risk of asset or personnel loss
- Autonomous operation
- Deploy sensors in timely manner
- Monitor status- Maintenance- N/A
- Reduced manpower, time- Reduced operator error- N/A
- High manpower, time- Operator error- Safety concern for deploying
in unfriendly territory
Operations (N3)
Value Proposition Canvas
Customer Workflow
MVP
AIS Weather
MVP
AIS Weather
MVP
AIS Weather
Questions?
Customer Workflow
N2
N3
N2(“owns”
the intel)
N3(“owns”
the assets)
Ready-To-Use DataDeployment
Data Acquisition
Data Analysis
Data
Order/Decision
Data Acquisition
ContextualizedDatabase
Last Week’s MVP
Deployment
Last Month
Today
Object-orientedDatabase
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?