Download - Sentinel Week 7 H4D Stanford 2016
Team Sentinel
● Team members:○ Jared Dunnmon○ Darren Hau○ Atsu Kobashi○ Rachel Moore
● Cumulative # of interviews: 73 + 12○ Users: 3 Experts: 9
● What we do: Enable rapid, well-informed decisions by establishing a common maritime picture from heterogeneous data
○ Open and automated data aggregation (i.e. incorporate open source data)○ Flexible layering and filtering with improved UI/UX○ Enhanced intel through contextualization and easily accessible, common database○ Identifying deviations from baseline by utilizing historical data
● Why it matters:○ Information overload○ A2/AD prevents deployment of traditional ISR in a timely manner○ Data aggregation platforms and database access in PACOM appear extremely manual
● Military Liaisons○ --- (Colonel, US Army)○ --- (Commander, US Navy)
● Problem Sponsor○ --- (Lieutenant, US Navy 7th Fleet)
● Tech Mentors include:○ --- (Palantir)
QUOTE OF THE WEEK
“THAT’S CLASSIFIED.”
Contents
1. Customer Discovery
2. Mission Model Canvas
3. Value Props
4. Mission Achievement
5. MVP
Hypotheses Experiments Results Action
Significant data about many data fusion programs available at the unclass level
IN PROGRESS
- Interviews with a variety of stakeholders at PACOM-MVP from last week: asking our contacts to fill out a spreadsheet indicating their awareness of different data fusion tools
- Suboptimal response levels on ask from last week (possibly due to major 7th Fleet operations)-Will continue this week
- Visit to San Diego- Continue pushing this efforts this week
IUU fishing problem is a good proxy problem for 7th Fleet MDA with more widely available data sources
PARTIALLY VALIDATED
- Interviews with PACOM COP and GCCS expert-Interviews with IUU fishing stakeholders with previous Navy experience-Research on existing IUU fishing tools
- Access to open-source visualization/analysis tool from UN Fishing and Agriculture Organization- Understanding of ship behavior patterns (AIS on buoys, insights on ship tracking/movements)-Won’t be able to understand everything Navy does on MDA analytics, but a good start
- Working with nonprofits to get access to marine ship data
-Use IUU fishing problem to demonstrate rapid development of flexible data fusion/analytics capability
Customer Discovery
Hypotheses Experiments Results Action
Physical “sensors” are the only way to add new data feeds for 7th Fleet
INVALIDATED
- Interviews with NPS researchers-Interview with PACOM SME on GCCS and COP-Interview with MFIC personnel
- Distributed marine sensors are one possible way to think about this capability-Drones + ship recognition emerging-Social media an emerging data source
-Considering integration of these additional future/non-traditional data sources into our MVP
Flexible integration of available sensor feeds into both COP and intel functions would be a useful capability
VALIDATED
-Interviews with NPS personnel researching applicability of UAVs to MDA-Interviews with IUU (Illegal, Unreported, Unregulated) fishing stakeholders on analog problem-Interview with PACOM SME on GCCS and COP
- Right now, a key general problem (exacerbated by A2/AD) is that data feeds that are available can change rapidly- COP and intel systems that allow for flexibility in visualization and analysis enable new sensor prototyping and effective use of existing data
- Focusing MVP on a way to flexibly and seamlessly use whatever data is available (algorithms and visualization)
Customer Discovery
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)
IUU People (working on it)
- 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 AchievementMission Budget/Costs
Buy-In
Deployment
Value Proposition
Key Activities
Key Resources
Key PartnersMilitary- 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
Products& Services- Timely data- Good UI/UX
for presenting data
- Streamlined reporting process
- Improved coordination across ranks
- Utilizes current tool pipeline
Customer Jobs
Gains
Pains
Gain Creators
Pain Relievers
- Good UI/UX- Platform
incorporates more data streams
- Platform is robust and can handle drop out of data streams
- Allocate assets- Identify,
eliminate threats
- Predict hot spots
- Safety of team- Projecting
peace, stability in region
- More informed decisions
- Faster decisions- Decisions made
on most up-to-date info
- Poor quality/lack of data
- Latency of data -> insight
Admiral/Strategic Decision Maker
Value Proposition Canvas
Customer persona:
● 3 or 4 star admiral● Born in late 1950’s● Have their own office on-base● Gives out challenge coins
● 30,000 ft view thinker● Spent entire professional career
in Navy (deeply ingrained culture)
Products& Services
- Contextualized, object-oriented database
- Algorithms for processing, analyzing data
- Ability to search for trends across database
- Integration of disparate data sources
- Automation of data analysis- Improved UX/UI- Predictive notifications- Filtering and layering
features- Tool architecture is flexible
and intelligent
Customer Jobs
Gains
Pains
Gain Creators
Pain Relievers
- Compatible data format
- Incorporate multiple data streams with existing object-oriented database
- Integration into current processes is simple
- Collect & analyze data
- Communicate findings
- Piece together contextualized awareness
- More actionable insights- Faster identification & response times- Easy-to-use- Information continues to be processed
and visualized even if data streams are added/dropped (no Christmas Light effect)
- Incorporation of context is manual/mental
- Poor quality / lack of data- Latency of data -> insight- Long onboarding processes
Analyst (N2)
Value Proposition Canvas
Customer persona:● Sits in front of computer all day● Job is normally boring with bursts
of excitement● Some may have constantly
varying hours/schedules
● “19 year old from Oklahoma”● Regimented schedule● May or may not like what they do
Products& Services
- N/A
- Actually a common operating picture!
- Data is actually synced across databases
- Tool architecture is flexible and intelligent
Customer Jobs
Gains
Pains
Gain Creators
Pain Relievers
- No hardware to deploy so no risk of asset or personnel loss
- Fewer change orders- Training and integration
with current processes is simple
- Utilize assets and human capital in order to obtain ISR data on adversary or regions of interest
- Timely and enhanced allocation and deployment of assets
- Information continues to be processed and visualized even if data streams are added/dropped (no Christmas Light effect)
- High manpower, time- Operator error- Safety concern for
deploying in unfriendly territory
- Struggle to redeploy systems on short notice (<12 hours) = frustration
- Long onboarding processes
Operations (N3)
Value Proposition Canvas
Customer persona:
● General sense that N2 and N6 “work” for them
● “19 year old from Oklahoma”● Regimented schedule
● May or may not like what they do
(green indicates that validation is still needed)
Beneficiary Mission Achievement
Intelligence Analysts
- Decreased time to aggregate data, classify and differentiate surface vessel activity, and present conclusions.
- Flexible integration of new feeds into analytics
Operators - Real-time (no lags of 2-6 hours!) common operational picture (COP) of surface vessel activity with notifications of abnormal activity based on algorithms and analyst conclusions.
- Flexible integration of new feeds into COP
Strategic Decision-maker
- Up-to-date information on activity in AOR- Better understanding of what data contributed to analysis in CUB.
Mission Achievement
MVP: Modular Intake, Algorithm, and Display
MVP: Modular Intake, Algorithm, and Display
MVP: Modular Intake, Algorithm, and Display
MVP: Modular Intake, Algorithm, and Display
Thank you!
Any questions?
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 NEEDSFUNCTIONS
& PROGRAMS
SHIP 2 SHIP 3SHIP 1
MVP: Software Domain Awareness
Program POC OrganizationFunction & 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
EWBMRC2 (Resilient C2)
Sample In-Development Product: ONR/CTI EWBM Tool
MVP (3 weeks ago)
MVP (3 weeks ago)
MVP (3 weeks ago)
MVP (4 weeks ago)
AIS Weather
MVP (4 weeks ago)
AIS Weather
MVP (4 weeks ago)
AIS Weather
Customer Workflow
N2
N3
N2(“owns”
the intel)
N3(“owns”
the assets)
Ready-To-Use DataDeployment
Data Acquisition
Data Analysis
Data
Order/Decision
Customer Workflow
Key Acquisition Paths
● Several potential deployment strategies
○ Linking in with an existing POR (PMW-150, etc.)
■ Pros: Allocated funding, long-term integration plans
■ Cons: Long timescale, getting in the door
■ POCs: ONI, SPAWAR (Stan Kowalski), Primes
■ Source of Excitement: TBD
○ Rapid Acquisition Pathways (Limited Objective Experiments, Rapid Reaction Technology Office)
■ Pros: Speed, Close to user, Don’t have to go through Navy (other services work)
■ Cons: Limited spending authority
■ POCs: 7th Fleet (Jason Knudson), DHS (Chuck Wolf)
■ Source of Excitement: Rapid deployment, changed acquisition model
○ DARPA
■ Pros: Development mindset, existing programs (Insight) that are well-aligned, deployment authority/capability to pay for deployment to end-users
■ Cons: stepping on toes, limited number of PMs
■ POCs: Craig Lawrence (ADAPT)
■ Source of Excitement: Directly solving a problem as opposed to many-year process
Sample Deployment Path (Software, POR Path)
1. Operational testing to make sure meets military specs (engage SPAWAR for this)a. Ensure NSA-standard Information Assurance (IA)
i. Lock down system and codeii. Make sure no category 1,2,3 in code - backdoors, exceptions, etc.
b. Observe appropriate NIST protocols (TBD)2. First, limited deployment to evaluate functionality (on testbed system or specific asset)3. Then, if integrated into a POR:
a. Deployed on whatever platform is neededb. Moves into sustainment phasec. Think about disposal & replacement--we want continuous improvement!
4. IT installs where requireda. Technical support install software and make sure up and runningb. Maintains communications systems and networks
5. Personnel training for system operation and maintenancea. CTMs focus on maintaining classified systems & special collections abilities
WE WILL BE GETTING MORE DETAIL ON THIS GOING FORWARD!