Sentinel Lessons Learned H4D Stanford 2016

Engineering

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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difficult for 7th Fleet in contested areas Solution: Navy needs cheap, distributed sensors Problem: Navy is hindered by outdated, cumbersome maritime domain awareness tools Solution: Navy actually needs enhanced data fusion, analytics, and sharing 4 Site Visits Week 0 Week 9 Trying to boil the ocean → Identified the real problem → Where can we best fit it?/How bad the problem is → Don’t try to integrate with existing tools, build C2-F! Intro: 1-4 [Rachel] Week 0: 5-7 [Rachel] Week 1-3: 8-19 [Atsu] Week 4-5: 20-30 (22-30 are the animation slides) [Darren] Week 6-7: 31-37 [Rachel] Week 8-9: 38-45 [Jared] Conclusion: 46-49 [Jared] 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 Slide 2 – Team members – name, background, expertise and your role on the team. Name of mentors and their affiliation. Interview Breakdown Over 10 Weeks Emotional Journey So many problems, so little time... Classified. Illegal Fishing Analog 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 DARREN Top points: key activities, value prop, mission achievement. Segway is Buy-In 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 DARREN Top points: key activities, value prop, mission achievement. Segway is Buy-In Boiling the ocean? What enabled us to get to the heart of this? Visit to NPS! → 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: Sensors aren’t the problem--data aggregation is--we pivoted before week 1! Needed to talk to more end-users--had identified operators, analysts, and acquisition as beneficiaries, but had only talked to analysts Diagrams to Include MVP MMC Team /Mentor Composition Number of Interviews: 14 Hypothesis: Insufficient sensing capabilities What enabled us to get to the heart of this? Visit to NPS! → 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: Sensors aren’t the problem--data aggregation is--we pivoted before week 1! Needed to talk to more end-users--had identified operators, analysts, and acquisition as beneficiaries, but had only talked to analysts Diagrams to Include MVP MMC Team /Mentor Composition Number of Interviews: 14 Experiments: Interviews, site visits... What enabled us to get to the heart of this? Visit to NPS! → 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: Sensors aren’t the problem--data aggregation is--we pivoted before week 1! Needed to talk to more end-users--had identified operators, analysts, and acquisition as beneficiaries, but had only talked to analysts Diagrams to Include MVP MMC Team /Mentor Composition Number of Interviews: 14 Learnings: Sensors largely exist Information sharing is a big problem Gov overwhelmed by sheer bulk of data What enabled us to get to the heart of this? Visit to NPS! → 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: Sensors aren’t the problem--data aggregation is--we pivoted before week 1! Needed to talk to more end-users--had identified operators, analysts, and acquisition as beneficiaries, but had only talked to analysts Diagrams to Include MVP MMC Team /Mentor Composition Number of Interviews: 14 We pivoted in Week 1! What enabled us to get to the heart of this? Visit to NPS! → Weeks 1 - 3: What’s the problem? High-level Thinkers Defense Contractors Week 1 Information sharing, data aggregation What enabled us to get to the heart of this? Visit to NPS! → 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? What enabled us to get to the heart of this? Visit to NPS! → 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 What enabled us to get to the heart of this? Visit to NPS! → 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: Everyone is right, but priorities are influenced by their roles. Sensors are great but Navy wouldn’t know what to do with it. What enabled us to get to the heart of this? Visit to NPS! → 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: Everyone is right, but priorities are influenced by their roles. Sensors are great but Navy wouldn’t be able to effectively use the data. What enabled us to get to the heart of this? Visit to NPS! → Getting out of the building! 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 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) 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 We had great insight into what the problem is; we feel smart for finding it → drill down into meat of problem (technical side); lots of roadblocks to implementing solutions qiuckly on acquisitions side; → as we’re digging down, we hear a lot that there’s a lot of other programs addressing these solutions; → 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 We had great insight into what the problem is; we feel smart for finding it → drill down into meat of problem (technical side); lots of roadblocks to implementing solutions qiuckly on acquisitions side; → as we’re digging down, we hear a lot that there’s a lot of other programs addressing these solutions; → Customer Discovery - Operations Center Workflow Hey Max, why is the ship still in port? This info isn’t up-to-date. Can you ask them to update this? Customer Discovery - Operations Center Workflow Yeah, hold on... Customer Discovery - Operations Center Workflow Customer Discovery - Operations Center Workflow PacFleet unit manager Hey Lauren, can you tell them to update this ship’s location? Customer Discovery - Operations Center Workflow 7th Fleet Hey Phil, can you get the new position for these guys? Customer Discovery - Operations Center Workflow Sure! Customer Discovery - Operations Center Workflow *Brrring* Customer Discovery - Operations Center Workflow Okay, the OS put in a new latitude and longitude. Ah, there it is. Customer Discovery - Operations Center Workflow 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 As we learned more about the extent of the problem, people kept mentioning other programs that seemed to address the gaps we identified We wanted to learn more about these programs’ technical and deployment details to better understand where we could add the most value 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... Logos for other programs Week 7: Classification Wall You should talk with the program manager! I’ll send an intro email. Great, thanks! We hit classification wall - no one would tell us any details on these programs Week 7: Classification Wall Hi, can you share anything about this tool? Actually...no... Sorry. We hit classification wall - no one would tell us any details on these programs Week 7: Found an Analogous Problem Illegal Fishing All the same problems and needs… But without the classification issues! 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 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) 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! While we were in San Diego, further need validation but also realized that there is an even bigger underlying issue Weeks 8: Visit to San Diego While we were in San Diego, further need validation but also realized that there is an even bigger underlying issue Don’t just be a band-aid for existing outdated disparate tools; there’s a need for an entirely new millenial-friendly C2 paradigm At 3rd Fleet, we asked about procurement/testing and also about existing tools -> they’re response was to not bother with existing programs and to completely redesign C2 for future. Also had last minute visit to DDG102 - learned about pains of C2 for users on the ship CIC 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 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 Final MVP - Command & Control of the Future CIC PACOM Surface radar contact but no AIS… This is odd. Let me ALERT others. 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 Final MVP - Command & Control of the Future CIC PACOM Final MVP - Command & Control of the Future CIC PACOM 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 Modular update to algorithm Modular update of visualization based on data 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 Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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 matter Team 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 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 Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) Thanks for listening! Questions? Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) Appendix Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) Mission Model Canvii Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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 Week 1 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 Week 2 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 Week 3 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 Week 4 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 Week 5 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 Week 6 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 Week 7 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 Week 8 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 Week 9 Learning Progression Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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: Sensors aren’t the problem--data aggregation is--we pivoted before week 1! Needed to talk to more end-users--had identified operators, analysts, and acquisition as benficiaries, but had only talked to analysts Diagrams to Include MVP MMC Team /Mentor Composition Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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! Proposed solution (MVP) System of low-cost sensors rapidly deployable by UUV along with backend common database Key Takeaways: We thought that the key problems were identifying a low cost sensor solution, enabling timely deployment, putting data into an open-source, commonly formatted database N2 director said he’d seen lots of data fusion products, but was never impressed We pivoted again! Diagrams to Include MVP, Customer Workflow Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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 Proposed solution (MVP) Data fusion (AIS+METOC) Key Takeaways: Data fusion is an enormous problem, both in importance and in scope Needed to talk to N3 Got good sense of high-level system workflow and organizational charts MMC starts to take form--focus on enhancing incoming data streams and data sharing Diagrams to Include MVP, org chart, MMC. workflow Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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 information Key Takeaways: Found out that UI/UX is a big problem for users of COP/C2 systems Data overload is a common problem Diagrams to Include MVP Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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) MVP A hard drive containing historical data locally--alleviates bandwidth, allows better pattern recognition/alerts Key Takeaways: Storage and bandwidth are major issues, hardware tied to POR Many programs coming down the pipe to fix various parts of C2, also many problems! Application-style updates are not currently done...it’s all OS style updates Diagrams to Include Customer Discovery, Systemfunctions and needs, acqusition path, MMC, functional org chart Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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) Spreadsheet with list of C2 programs and info Key Takeaways: For actual product development, illegal fishing use case is a better place to start than Navy C2/COP Diagrams to Include Get/Keep/Grow MVP Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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: Developing a COP-type system for IUU Fishing would be a good dual-use case Diagrams to Include Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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) Future C2--integrating social media feeds, etc, into COP Key Takeaways: Need to create future C2 for millenial users Diagrams to Include MVP Pictures MMC Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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: Sensors aren’t the problem--data aggregation is--we pivoted before week 1! Needed to talk to more end-users--had identified operators, analysts, and acquisition as benficiaries, but had only talked to analysts Diagrams to Include MVP MMC Team /Mentor Composition Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) MVPs Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) Analytics Engine Improved UI/UX Broadly-Accessible Database Week 1 MVP: Distributed Sensing Architecture 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? Hypothesis we’re addressing: Week 3 MVP AIS Weather Hypothesis we’re addressing: Week 4 MVP: Layering Hypothesis we’re addressing: Week 4 MVP: Push Alerts Hypothesis we’re addressing: Week 4 MVP: Vessel-Level Info & Predictive Analytics Hypothesis we’re addressing: Week 5 MVP Modular Device Local storage of historical data→less bandwidth usage + ability to do better pattern recognition, alerts GCCS / ADS Hypothesis we’re addressing: 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) MVP Hypothesis we’re addressing: Week 7 MVP: Modular Intake, Algorithm, and Display Hypothesis we’re addressing: Week 7 MVP: Modular Intake, Algorithm, and Display Hypothesis we’re addressing: Week 7 MVP: Modular Intake, Algorithm, and Display Hypothesis we’re addressing: Week 7 MVP: Modular Intake, Algorithm, and Display Hypothesis we’re addressing: Week 8 MVP: Shareable Data & Analytics CIC PACOM Surface radar contact but no AIS… This is odd. Let me ALERT others. 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 Week 8 MVP: Shareable Data & Analytics CIC PACOM Week 8 MVP: Shareable Data & Analytics CIC PACOM Key Diagrams Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) Customer Workflow N2 N3 N2 (“owns” the intel) N3 (“owns” the assets) Contextualized Data Deployment Data Acquisition Data Analysis Data Order/Decision MVP JIOC J1 J2 J3 J4 J5 J7 J6 J8 J9 N1 N4 N7 N6 N8 N9 VADM Joe Aucoin ADM Scott Swift ADM Harry Harris Jr N2/N39 Intel and Info Ops N3 Operations N5 Planning N22 Op/Intel Overwatch N23 Collection Operations N391 Fleet Cryptology & Information Operations N31 Current Operations N32 Fleet Oceanographer N33 Future Operations N34 AT/CIP/NWS N52 Fleet Doctrine Strategy N53 Deliberate Plans Division N54 Maritime Assessments N55 Functional 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 Workflow Hypothesis we’re addressing: N2 Analysis Strategic Decisions CUB Task Forces Data Acquisition (among other things) N3 Operational Decisions Information aggregation + analysis platform 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 Operational Requirements flow down from PACOM and is interpreted at each level: Operational Requirements 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 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 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 recommendations? # Systems outfitted ?? ?? ?? # users within 7th Fleet using tool # fleets using tool Map of System Functions and Needs QUELLFIRE GCCS (1) FOBM STORAGE/COMMS CST GCCS (3) GCCS (2) STORAGE/COMMS STORAGE/COMMS Sensors Sensors Sensors .oth-.json Translator Visualization Analytics Ship-to-Ship Sharing Long-Term Storage KEY NEEDS FUNCTIONS & PROGRAMS SHIP 2 SHIP 3 SHIP 1 So we decided we needed to better understand the existing tool ecosystem... Week 8 - MVP? Modular Device Local storage of historical data→less bandwidth usage + ability to do better pattern recognition, alerts GCCS / ADS Hypothesis we’re addressing: Week 8 - MVP? Deployment Method! Modular Device Local storage of historical data→less bandwidth usage + ability to do better pattern recognition, alerts “C2-F” Hypothesis we’re addressing: 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 UI - 4 man month Analytics engine - 6 man months Database - 4 man months Translation ETLs - 2 week/source (10 sources) Integration/buffer - 2 man month Info Assurance - 12 man months Testing - 24 man months Maintenance and Support - VC Total: 57 man months Customer Discovery Deployment Product 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 3 Year Financial/Ops/Funding Timeline 2016 2017 2018 2019 Q3 Q4 Q1 Q2 Cash Reserves Phase Product Gov’t Com’l Milestones Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 TRL 1 TRL 2 TRL 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 (
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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 Trying to boil the ocean → Identified the real problem → Where can we best fit it?/How bad the problem is → Don’t try to integrate with existing tools, build C2-F! Intro: 1-4 [Rachel] Week 0: 5-7 [Rachel] Week 1-3: 8-19 [Atsu] Week 4-5: 20-30 (22-30 are the animation slides) [Darren] Week 6-7: 31-37 [Rachel] Week 8-9: 38-45 [Jared] Conclusion: 46-49 [Jared] 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 Slide 2 – Team members – name, background, expertise and your role on the team. Name of mentors and their affiliation. Interview Breakdown Over 10 Weeks Emotional Journey So many problems, so little time... Classified. Illegal Fishing Analog 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 DARREN Top points: key activities, value prop, mission achievement. Segway is Buy-In 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 DARREN Top points: key activities, value prop, mission achievement. Segway is Buy-In Boiling the ocean? What enabled us to get to the heart of this? Visit to NPS! → 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: Sensors aren’t the problem--data aggregation is--we pivoted before week 1! Needed to talk to more end-users--had identified operators, analysts, and acquisition as beneficiaries, but had only talked to analysts Diagrams to Include MVP MMC Team /Mentor Composition Number of Interviews: 14 Hypothesis: Insufficient sensing capabilities What enabled us to get to the heart of this? Visit to NPS! → 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: Sensors aren’t the problem--data aggregation is--we pivoted before week 1! Needed to talk to more end-users--had identified operators, analysts, and acquisition as beneficiaries, but had only talked to analysts Diagrams to Include MVP MMC Team /Mentor Composition Number of Interviews: 14 Experiments: Interviews, site visits... What enabled us to get to the heart of this? Visit to NPS! → 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: Sensors aren’t the problem--data aggregation is--we pivoted before week 1! Needed to talk to more end-users--had identified operators, analysts, and acquisition as beneficiaries, but had only talked to analysts Diagrams to Include MVP MMC Team /Mentor Composition Number of Interviews: 14 Learnings: Sensors largely exist Information sharing is a big problem Gov overwhelmed by sheer bulk of data What enabled us to get to the heart of this? Visit to NPS! → 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: Sensors aren’t the problem--data aggregation is--we pivoted before week 1! Needed to talk to more end-users--had identified operators, analysts, and acquisition as beneficiaries, but had only talked to analysts Diagrams to Include MVP MMC Team /Mentor Composition Number of Interviews: 14 We pivoted in Week 1! What enabled us to get to the heart of this? Visit to NPS! → Weeks 1 - 3: What’s the problem? High-level Thinkers Defense Contractors Week 1 Information sharing, data aggregation What enabled us to get to the heart of this? Visit to NPS! → 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? What enabled us to get to the heart of this? Visit to NPS! → 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 What enabled us to get to the heart of this? Visit to NPS! → 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: Everyone is right, but priorities are influenced by their roles. Sensors are great but Navy wouldn’t know what to do with it. What enabled us to get to the heart of this? Visit to NPS! → 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: Everyone is right, but priorities are influenced by their roles. Sensors are great but Navy wouldn’t be able to effectively use the data. What enabled us to get to the heart of this? Visit to NPS! → Getting out of the building! 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 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) 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 We had great insight into what the problem is; we feel smart for finding it → drill down into meat of problem (technical side); lots of roadblocks to implementing solutions qiuckly on acquisitions side; → as we’re digging down, we hear a lot that there’s a lot of other programs addressing these solutions; → 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 We had great insight into what the problem is; we feel smart for finding it → drill down into meat of problem (technical side); lots of roadblocks to implementing solutions qiuckly on acquisitions side; → as we’re digging down, we hear a lot that there’s a lot of other programs addressing these solutions; → Customer Discovery - Operations Center Workflow Hey Max, why is the ship still in port? This info isn’t up-to-date. Can you ask them to update this? Customer Discovery - Operations Center Workflow Yeah, hold on... Customer Discovery - Operations Center Workflow Customer Discovery - Operations Center Workflow PacFleet unit manager Hey Lauren, can you tell them to update this ship’s location? Customer Discovery - Operations Center Workflow 7th Fleet Hey Phil, can you get the new position for these guys? Customer Discovery - Operations Center Workflow Sure! Customer Discovery - Operations Center Workflow *Brrring* Customer Discovery - Operations Center Workflow Okay, the OS put in a new latitude and longitude. Ah, there it is. Customer Discovery - Operations Center Workflow 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 As we learned more about the extent of the problem, people kept mentioning other programs that seemed to address the gaps we identified We wanted to learn more about these programs’ technical and deployment details to better understand where we could add the most value 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... Logos for other programs Week 7: Classification Wall You should talk with the program manager! I’ll send an intro email. Great, thanks! We hit classification wall - no one would tell us any details on these programs Week 7: Classification Wall Hi, can you share anything about this tool? Actually...no... Sorry. We hit classification wall - no one would tell us any details on these programs Week 7: Found an Analogous Problem Illegal Fishing All the same problems and needs… But without the classification issues! 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 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) 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! While we were in San Diego, further need validation but also realized that there is an even bigger underlying issue Weeks 8: Visit to San Diego While we were in San Diego, further need validation but also realized that there is an even bigger underlying issue Don’t just be a band-aid for existing outdated disparate tools; there’s a need for an entirely new millenial-friendly C2 paradigm At 3rd Fleet, we asked about procurement/testing and also about existing tools -> they’re response was to not bother with existing programs and to completely redesign C2 for future. Also had last minute visit to DDG102 - learned about pains of C2 for users on the ship CIC 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 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 Final MVP - Command & Control of the Future CIC PACOM Surface radar contact but no AIS… This is odd. Let me ALERT others. 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 Final MVP - Command & Control of the Future CIC PACOM Final MVP - Command & Control of the Future CIC PACOM 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 Modular update to algorithm Modular update of visualization based on data 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 Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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 matter Team 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 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 Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) Thanks for listening! Questions? Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) Appendix Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) Mission Model Canvii Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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 Week 1 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 Week 2 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 Week 3 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 Week 4 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 Week 5 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 Week 6 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 Week 7 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 Week 8 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 Week 9 Learning Progression Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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: Sensors aren’t the problem--data aggregation is--we pivoted before week 1! Needed to talk to more end-users--had identified operators, analysts, and acquisition as benficiaries, but had only talked to analysts Diagrams to Include MVP MMC Team /Mentor Composition Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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! Proposed solution (MVP) System of low-cost sensors rapidly deployable by UUV along with backend common database Key Takeaways: We thought that the key problems were identifying a low cost sensor solution, enabling timely deployment, putting data into an open-source, commonly formatted database N2 director said he’d seen lots of data fusion products, but was never impressed We pivoted again! Diagrams to Include MVP, Customer Workflow Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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 Proposed solution (MVP) Data fusion (AIS+METOC) Key Takeaways: Data fusion is an enormous problem, both in importance and in scope Needed to talk to N3 Got good sense of high-level system workflow and organizational charts MMC starts to take form--focus on enhancing incoming data streams and data sharing Diagrams to Include MVP, org chart, MMC. workflow Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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 information Key Takeaways: Found out that UI/UX is a big problem for users of COP/C2 systems Data overload is a common problem Diagrams to Include MVP Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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) MVP A hard drive containing historical data locally--alleviates bandwidth, allows better pattern recognition/alerts Key Takeaways: Storage and bandwidth are major issues, hardware tied to POR Many programs coming down the pipe to fix various parts of C2, also many problems! Application-style updates are not currently done...it’s all OS style updates Diagrams to Include Customer Discovery, Systemfunctions and needs, acqusition path, MMC, functional org chart Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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) Spreadsheet with list of C2 programs and info Key Takeaways: For actual product development, illegal fishing use case is a better place to start than Navy C2/COP Diagrams to Include Get/Keep/Grow MVP Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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: Developing a COP-type system for IUU Fishing would be a good dual-use case Diagrams to Include Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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) Future C2--integrating social media feeds, etc, into COP Key Takeaways: Need to create future C2 for millenial users Diagrams to Include MVP Pictures MMC Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) 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: Sensors aren’t the problem--data aggregation is--we pivoted before week 1! Needed to talk to more end-users--had identified operators, analysts, and acquisition as benficiaries, but had only talked to analysts Diagrams to Include MVP MMC Team /Mentor Composition Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) MVPs Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) Analytics Engine Improved UI/UX Broadly-Accessible Database Week 1 MVP: Distributed Sensing Architecture 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? Hypothesis we’re addressing: Week 3 MVP AIS Weather Hypothesis we’re addressing: Week 4 MVP: Layering Hypothesis we’re addressing: Week 4 MVP: Push Alerts Hypothesis we’re addressing: Week 4 MVP: Vessel-Level Info & Predictive Analytics Hypothesis we’re addressing: Week 5 MVP Modular Device Local storage of historical data→less bandwidth usage + ability to do better pattern recognition, alerts GCCS / ADS Hypothesis we’re addressing: 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) MVP Hypothesis we’re addressing: Week 7 MVP: Modular Intake, Algorithm, and Display Hypothesis we’re addressing: Week 7 MVP: Modular Intake, Algorithm, and Display Hypothesis we’re addressing: Week 7 MVP: Modular Intake, Algorithm, and Display Hypothesis we’re addressing: Week 7 MVP: Modular Intake, Algorithm, and Display Hypothesis we’re addressing: Week 8 MVP: Shareable Data & Analytics CIC PACOM Surface radar contact but no AIS… This is odd. Let me ALERT others. 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 Week 8 MVP: Shareable Data & Analytics CIC PACOM Week 8 MVP: Shareable Data & Analytics CIC PACOM Key Diagrams Slide 1 ● Team name ● A few lines of what your initial idea was ● The size of the opportunity (TAM/SAM) ● Total number of interviews personally conducted (include any email interactions or survey numbers in parentheses) Customer Workflow N2 N3 N2 (“owns” the intel) N3 (“owns” the assets) Contextualized Data Deployment Data Acquisition Data Analysis Data Order/Decision MVP JIOC J1 J2 J3 J4 J5 J7 J6 J8 J9 N1 N4 N7 N6 N8 N9 VADM Joe Aucoin ADM Scott Swift ADM Harry Harris Jr N2/N39 Intel and Info Ops N3 Operations N5 Planning N22 Op/Intel Overwatch N23 Collection Operations N391 Fleet Cryptology & Information Operations N31 Current Operations N32 Fleet Oceanographer N33 Future Operations N34 AT/CIP/NWS N52 Fleet Doctrine Strategy N53 Deliberate Plans Division N54 Maritime Assessments N55 Functional 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 Workflow Hypothesis we’re addressing: N2 Analysis Strategic Decisions CUB Task Forces Data Acquisition (among other things) N3 Operational Decisions Information aggregation + analysis platform 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 Operational Requirements flow down from PACOM and is interpreted at each level: Operational Requirements 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 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 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 recommendations? # Systems outfitted ?? ?? ?? # users within 7th Fleet using tool # fleets using tool Map of System Functions and Needs QUELLFIRE GCCS (1) FOBM STORAGE/COMMS CST GCCS (3) GCCS (2) STORAGE/COMMS STORAGE/COMMS Sensors Sensors Sensors .oth-.json Translator Visualization Analytics Ship-to-Ship Sharing Long-Term Storage KEY NEEDS FUNCTIONS & PROGRAMS SHIP 2 SHIP 3 SHIP 1 So we decided we needed to better understand the existing tool ecosystem... Week 8 - MVP? Modular Device Local storage of historical data→less bandwidth usage + ability to do better pattern recognition, alerts GCCS / ADS Hypothesis we’re addressing: Week 8 - MVP? Deployment Method! Modular Device Local storage of historical data→less bandwidth usage + ability to do better pattern recognition, alerts “C2-F” Hypothesis we’re addressing: 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 UI - 4 man month Analytics engine - 6 man months Database - 4 man months Translation ETLs - 2 week/source (10 sources) Integration/buffer - 2 man month Info Assurance - 12 man months Testing - 24 man months Maintenance and Support - VC Total: 57 man months Customer Discovery Deployment Product 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 3 Year Financial/Ops/Funding Timeline 2016 2017 2018 2019 Q3 Q4 Q1 Q2 Cash Reserves Phase Product Gov’t Com’l Milestones Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 TRL 1 TRL 2 TRL 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 (
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