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Week 11 Insights

Our team has edited the CPC interface, based on feedback from last week, and we created the CG interface. This week, we received our first round of feedback on the CG feedback. Both the CPC and CG interfaces are aesthetically appealing, but we need to hone in on what visuals are needed and not needed. For the CG interfaces, we need to identify whether comparing their cohorts’ performance with other CG’s cohorts’ performance and the must-have information they need to see quickly. For the CPC interfaces, we now allow soldier performance to be viewed by class and cohort which has been well-received.





Primary Data Users

  1. Cognitive Performance Coaches
  2. Data Analysts
  3. Research Psychologist

Other Beneficiaries

  1. Instructors (Cadre)
  2. SWCS Commander






Phil Williams | CEO of Phil Williams LLC

  • Agrees that organizing by classes provides a better picture than cohorts b/c people who drop out of certain cohorts and then are categorized to a different cohort
  • For the normal distribution chart, Phil is interested in the specific characteristics that differentiate the top 5% v. the top 50% and lowest 5%?
  • “Cohort Comparison Chart” could include – how do my cohorts compare w/ CG John M, CG X, CG Y? This provides a comparison to CG’s how they are doing to past CG’s
Bruce MacDowell Maggs | Professor of Duke Computer Science


  • DARPA is interested in blockchain for storing soldier demographic information
  • DARPA wants additional research or case studies of how blockchain could work to store information and has convened blockchain researchers to learn more
Michael Jelen | Berkeley Research Group


  • Try combining the macro and micro-level data so that only the most important information is present.
Adam Beauregard | United States Navy


  • Everything looks fine.
  • Doubtful that the team CPC will go through 12 pages of graphs. Pick out the most important ones.
  • Consider making a dashboard.
Mitch Heath | CEO at Teamworks


  • Try figuring out which graphs are the most important.
  • Everything seems aesthetically good.
  • Try also to find filtered categories.
  • Willing to invite people to the location to meet the team.
  • Wants us to send info about H4D demo day on April 18th.
Joe Blanton | Colonel United States Army


  • The ability for the platform to tailor to the preference of different commander is very important
  • Present the so-what and the purpose before explaining the why; assuming a busy and inpatient commander only has less than a minute of time
  • An algorithm that take in key indicator to show the changes of weekly key result
  • Have evidence and data to back-up what is the drive of attrition
  • Regression and projection would help a lot
Anubhav Mehrotra | VP of Product Management at Live Nation

Factors used to test level of engagement and emotion change:

  • Galvanic skin response
  • Position shift in alpha brainwave
  • Movement synchrony
Lieutenant Colonel Thomas Academic Instruction Director | SWEG


  • Agrees that trainees don’t need access. Reports can be generated for them
  • Make sure our efforts are coordinated with Oscar
  • Still concerns over data storage





  1. Classes, rather than cohorts, are a better way to categorize soldier performance due to the soldier drop out from cohorts.
  2. CG’s may be interested in having more options – filtering out by certain soldier/cohorts or comparing their performance to other CG’s.
  3. We still need to drill in on what specific graphs are useful for CPC and start taking out or having the option to filter out the less useful ones.





  1. We need to talk to cadre’s, but there has been pushback on why speaking to cadre’s is relevant to our problem.
  2. Instead of guessing what the CG wants, we need to interview a CG to truly tailor the platform to his/her needs.
  3. We would like to understand better whether the CG wants more data on the selection or training aspects of the high attrition rate.





  1. We have scheduled a meeting to speak with Commander Rice which will help us understand the 1) type of data 2) amount of data 3) types of insights that would be most useful for higher ups.
  2. We do not want students to have access to our platform as, from interviews, the CPC’s can work individually with students and show them the platform during meetings.
  3. If we cannot interview cadre’s within the next week, then we need to remove them as a beneficiary. We will continue reaching out to the contact our problem sponsor has provided to get interviews with cadre.


Week 10 Insights

Our team has made further changes to our prototype based on feedback from last week. This week we have received feedback on what should be included when considering holistic performance. The visuals are well received, but our client wants to know more about how the data is being put together, where our golden ratio numbers are coming from etc. Our CPC interface is looking pretty good, but we need to work on what the research psychologist and the CG should be able to see regarding performance data.





Primary Data Users

  1. Cognitive Performance Coaches
  2. Data Analysts
  3. Research Psychologist

Other Beneficiaries

  1. Instructors (Cadre)
  2. SWCS Commander






Trevor O’brien | Information Technology


  • Comparison timeline. Weekly is good, but monthly or even quarterly could be useful for visualization.
  • General feedback, good compared it to spear interface.
  • Maybe too many tabs at the moment. Navigation may get too complicated.
  • Interested in looking at the tactical training.
Shawn Zeplin | Director of Behavioral Health at Duke University Athletics

  • Mental health and athletic performance are not separated – he works with both aspects of student athletes to improve sports performance
  • Athletes, mostly male, do not trust him, because they have been socialized that showing emotion is weak
    • He spends time at their practices to build trust
Phil Williams | CEO of Phil Williams LLC

  • The focus on holistic performance is b/c the pressure and psychological stress that was put on the trainee
    • Soldiers who were going to therapy didn’t want people to know they were going to therapy so that is why there is holistic focus
  • Compare soldier performance w/ marine or navy corp people – what information can you correlate among different branches (pushups, metabolic rate)?
  • The platform addresses two things they need 1) real-time data 2) cadre v. cadre & soldier v. soldier & military v. navy v. air force
Oscar Gonzalez | Research Psychologist


  • Holistic Performance – Rader Chart need to have both
    • Physical domain
    • Psychological domain
  • Exercise & Performance – what other factor contribute to performance difference?
    • Diet
  • Percentage representation is vague, need to specify what’s the unit time
  • Need to clearly label each graph and each measurement need to have a unit
  • Need to define what ideal means
  • The baseline graph shouldn’t be a continue graph – > use bar chart instead
  • The position of the sliding bar is confusing
Constance Garcia | Data Manager


  • Heart rate variability monitors & eye tracking are the only ones that CPC’s use right now
  • Surveys are handed out to the entire cohort
  • Surveys change year from year – data analysts can upload surveys into the platform so they can compare whether surveys are comparable
Alexandra Hanson | Research Analyst

  • We don’t want to target specific instructors graduation rates bc it sets up competitive field between instructors
  • For CGs, we want to  create four quadrants – CPC, Cadre, physical, academic/survey data
    • CG’s don’t need to know information on specific individuals


  • Currently 6 cohorts in a year, will change to 4 cohort next year
  • CPC don’t care that much about Cohort performance and organize information by cohort won’t help them, what they want is organize by course(However research psychologist really want the information to be organized by cohort, this is what Oscar care about) — All depends on who is the target user
  • Cohort sorting would be the least use feature since few soldiers make through the entire pipeline still stay within his/her original cohort
    • Organize by course make much more sense
  • Want data visualization for each course
  • Want comparison among different pipeline: Green Beret, Civil Affairs, Psychological Operation
  • There are total of 12 CPC on the team, each coach between 80 – 300 students
  • Based on Aspen, CPC don’t care about individual profile and overall they are not interested in individual performance that much, they care about course
    • Not that frequent meet with individual students (sometimes not at all, sometimes 10-20 per week, usually only meet with the students who are struggling and those who outperformed)
    • Instead of displaying individual performance they want course performance
  • 7 phases, one phase will broke into many sub-courses. Ex: there are five courses within officer specific training
  • Based on Aspen, data team and research psychologist don’t have the training and don’t know how to run the analysis that they want —> CPC input data and run analysis themselves —> the product should allow them to input data, organize data, output result and run analysis
  • Whatever information accessible to research psychologist should also be made accessible to CPC
  • Overall, Aspen loves the prototype and she gave very positive feedback: incredible, easy-to-use, highly developed, awesome job
Daniel Gajewski Performance Integrator


  • Visuals are good. Good incorporation of feedback from last conversation
  • Showing that we are able to take multiple files from soldiers and integrate them into aggregate data is promising.
  • Ideally, based on the visualization we have shown, if we have say 8 soldiers in a particular cohort and 7 exercises, this would mean using 56 files to look at holistic and aggregate performance (for simplicity, there would likely be more than one file per training exercise for one soldier).
Mark Manturo | Research Psychologist


  • Holistic Performance
    • Leadership ability
    • Physical fitness test score
    • Ability to learn
    • Tactical proficiency
    • Deploymacy
    • Problem solving
  • What is the standard of “idea”? In navy they have NSS-navy grading score
    • Put something in in terms: historical score (minimal score, average score, reference line)
    • Standard deviation
  • What want to see on the commander’s page:
    • Peer evaluation
    • Student’s hobby and what activities do they do in their spare time
      • Looking for endurance athletes and team player; attract intelligent and all-rounded people
    • Answers to questions such as: Am I producing the next generation? Do I have enough people? Do I need a policy change
Seth Spradley | Data Analyst


  • Says visually it looks good, interface is intuitive.
  • Offered to help debug the software system when we develop it further.
  • Progression tab, overlay box should be moved to upper left hand corner rather than in the middle of the graph.
Maj Arth | Joint Special Operations Command


  • Ask if there is an operational psychologist
  • Have several “mini-traits”
  • Layout the overall situation. Tell your sponsor to pick a specific problem to go after. Can’t solve all the military problems.
Dillon Buckner | Military Intelligence Observer, United States Army


  • Pitch to companies that have a similar solution and try to get buy-in from them.
  • Get rid of red text from the cohort home page. Red means bad in the military
  • It would be more ideal to show a passing rate for each class.





  1. Mental health component is a large part of performance and soldiers tend to be skeptical of mental health professionals. (cultural/socialization issue)
  2. While targeting specific instructor graduation rates would be useful data, it would also create a moral hazard due to competition to graduate the most.
  3. Cross service comparisons for special forces performance metrics would potentially be useful, if we can get the data.





  1. We need to make sure that different users have what they need for their profiles on our software platform.
  2. When looking at ideal performance (golden ratio), we need to be more specific about what that means for different training exercises/metrics
  3. For self reported data, surveys change from year to year. If this is the case, then how do we accurately compare survey data over time?





  1. We need to think about what the SWCS CG wants to see on his interface. Ideally it should be simple and robust enough for him to see trends and make policy decisions.
  2. We need to add more granularity to the analysis. Even though there are 7 main exercises, there are multiple sub exercises. These also will be likely multi day measurements as well, meaning that we will need to integrate more files.
  3. Do we want students to have login access? We have received conflicting feedback on this and need to make a decision soon.


Week 9 Insights

This week we used our interviews to get further feedback on our prototype. Based on last week’s feedback we added more interfaces and visualization tools. This task was spearheaded by Bettie. The response this week has been positive. We discovered that when dealing with analytics, it is important to have benchmarks to compare. These “golden references” are soldiers who are the highest performers. If we can break down these soldiers into groups based on characteristics, we may be able to help our client figure out what is seperating the highest performers from the rest of the pack.





Primary Data Users

  1. Cognitive Performance Coaches
  2. Data Analysts
  3. Research Psychologist

Other Beneficiaries

  1. Instructors (Cadre)
  2. Trainees
  3. SWCS Commander






Lieutenant Colonel Thomas Academic Instruction Director | SWEG


  • Briefly viewed prototype but had computer issues
  • Acquisition process for our product may not be too difficult if the product is relatively inexpensive, could likely be purchased through a simple contract/credit card.
  • Issue is, who has the money? LTC Thomas would likely have to lobby either SWCS, USASOC or SOCOM. Process could take months to years depending on politics and who has the money.
Mitch Health | CEO at TeamWorks

  • These are the questions we should be asking:
    1. “What is the one thing you want if you could see w/in 20 seconds?”
    2. “What do you think is the most important takeaway from this page?”
    3. “If you were presenting a page for your boss, then what would you include in there?”
  • Rank list of priorities for the person to minimize having too many features
Phil Williams | CEO of Phil Williams LLC

  • Compare trainees to the “golden references”
  • Need golden references for gender, age group – we need to create golden references by other groups
  • Figure out average performance of high performers
Morgan Hall | SOCEP CPC


  • “It’s all about progression – what is the baseline is and how do I progress on these various indices from that baseline?”
  • Operational GB who have been here many years and have had many deployments would be the golden reference
  • Would use it most likely when he is having a one on one with the soldier to see which areas they think they’re struggling in
Constance Garcia | Data Manager


  • Put the following into soldier info: age, rank, years of service, deployment & how many years deployed, highest education, prior & current MOS (military occupational specialty)
  • LOVES: holistic performance overview (spider web graph) – she loves it b/c it shows how each part plays into the other
  • Don’t use personal ID information b/c they don’t want this to go on their record – take out “John A.”
Travis Nicks | Former Navy Submarine nuclear operator for 10 years


  • Based on his 10 years experience working on technology innovation at Navy, it’s very hard to get devices approved to connect to the internet due to risks of cybersecurity threat
    • Even get approved, there will be many constraints on the use case
  • Metrics can be used to measure anxiety level besides hrv includes:
    • Breathing rate
    • Sweat
    • Saliva – hydration level
    • Stand still -motion range
Barbara Plotkin | CPT USSOCOM former instructor


  • We need to be more hands off during captain’s career course training
  • Mission analysis is always taken in the form of a problem statement
  • Funding begins in October, 4 cycles of CCC, then ends on the end of September
  • Comptroller looks at historical data and decides if the needs of CCC are being met with the current level of funding.
Seth Spradley | Data Analyst


  • Prototype holds up well visually. Graph presentation is very effective.
  • Interested in seeing more of the achievement history in the soldier performance tab
  • Was confused if we were solely focussing on Green Berets or are also including civil affairs and psyops. (regardless, this system should be able to scale to meet their needs)
Alexandra Hanson | Research Analyst

  • “There has been a lot of people pushing for this information, but it has been hard to get units to share information” (about developing golden references)
  • The quality of candidates that we were getting almost twenty years ago is remarkably different than the quality of candidates that we are getting today – seventeen years ago, 9/11 happened so we had wall street bankers, Master’s degrees candidates so the quality of education that people are coming in with are very different.
  • Visually represent in an intuitive large amounts of data – CPC’s need a platform that brief them on data in an intuitive and self explanatory way
Gabriella Shull | Duke Biomedical Engineering PhD  @ BX/NC


  • Visual attention EEG
  • Can refer us to PhD in cognitive performance and neuron science if needed
  • Look into cognitive brain science
    • What affect focus and attention  





  1. Having the prototype stand out visually is key to selling it as a solution.
  2. While there are many competing wants and needs, we should prioritize the most important features and not include everything.
  3. “Golden Standard” for recruits needs to be established in order to provide benchmarks for analysis and can be compared to things such as the group average.





  1. Full deployment of the MVP may still involve a hardware component that is not anywhere near development ready.
  2. Rapid deployment is also linked to funding availability. At this point in time this would mean looking for unobligated funds in either SWCS, USASOC or SOCOM.
  3. Network security/device approval process is still a major hurdle.





  1. We need to start narrowing down metrics in order to build a robust data system that can run the statistical analysis that our client wants.
  2. Once we have our metrics figured out, how do we go about linking soldier strengths and weaknesses to possible training interventions?
  3. Who do we really need to get to in order to sell our product?


Week 8 Insights

We utilized our 10 interviews this week to accomplish two main goals: (1) collect feedback on our software prototype, a data management and visualization platform (2) validate our hypothesis that having a data management platform that enables our beneficiaries to visualize the output would help them to make data-driven decisions. Betty spent her weekend on prototyping and delivered an interactive prototype. The team walked through the prototype with project sponsor Lieutenant Colonel Thomas and interviewed key beneficiaries such as HDP director Jim, research psychologist Oscar, and Constance and Alexandra from the data team. We have received both positive and constructive feedback from interviews. These valuable feedback provide us with a much clearer idea on what kind of function and product feature they want and in what direction shall we further develop the prototype.





Primary Data Users

  1. Cognitive Performance Coaches
  2. Data Analysts
  3. Research Psychologist

Other Beneficiaries

  1. Instructors (Cadre)
  2. Trainees
  3. SWIC Commander






Lieutenant Colonel Thomas Academic Instruction Director | SWEG


  • Want the product to answer the following questions:
    1. How many people are going through pipeline in a day?
    2. How satisfied are trainees interested?
    3. What are the machines that are used the most?
  • ROI, bottom line – (they are investing money and want to know how money is creating results)
Oscar Gonzalez Research Psychologist | SWEG


  • time series analysis, want to see changes over time
  • comparisons over different groups (age bracket, ranks, years of experience, MOS/job description)
  • Want the product to answer the following questions:
    1. What is the exercise that provides the biggest performance increase?
    2. What is the performance machine/training/process that creates the biggest change?
    3. Who are the people that have the biggest change and smallest change?
    4. When do people start showing performance training over time?
Constance Garcia Data Manager


  • The ability to run statistical analysis
  • Really like how the prototype shows who has “touched the data”
  • Use excel to run basic analysis with a set of metrics for most of the time
  • SPSS isn’t on the network so they must use the software on a stand alone computer
Major Mike Williams Special Operations Command Officer  


  • 75 Ranger Regiment: as new soldiers came in, they would put them where they were relative to the baseline

-”the average soldier can do 65 pushups per minute” -> here’s a regime on how you can get to the average pushup

  • we need to create a normal distribution curve for people who complete the training
  • Reach back out to Major Mike Williams when we need help w/ statistical analysis
Dr. Greg Dale Sport Psychologist & Leadership Director | Duke


  • Three focus areas for Duke sports: leadership, culture, performance
  • Works with students on: expectations, evaluation, awareness of consequences
  • Teach students how to be aware of their nervous feelings and physical changes to perform better
Daniel Gajewski Performance Integrator


  • Issues still to tackle: data centralization from multiple sources, time stamping, aggregation…
  • Finding a way to get data from the field back to the lab is ideal. Hardware solution is still necessary.
  • Synchronizing data to an event during training is more important than having the program run advanced statistical analysis. (for Dan at least)
Trevor O’brien Information Technology


  • Would like to see graphical user interface, how it was developed. Step by step. (less useful right now since it’s more of a visual representation)
  • Showing what different profiles for different users would look like would be useful.
  • Making sure the user interface is user friendly is key.
  • Trevor is more of a hands on learner, could help us with development if we let him in.
  • Provided some data on spear that may be helpful.
  • Unrelated: Spear is likely going to get nixed, but could be helpful for us to use.
 Rick Dietrich SOCEP Director


  • Really likes how simply and intuitive the software is to use.
  • Nexus 10 has a biotrace+ software that connects all sorts of devices:
    • EEG
    • ECG
    • Galvanic Skin Conductor
    • EMG
    • Blood Oxygen Level
    • Blood Volume Pulse
    • Extremity Temperature
    • SCP (Brian Score)
  • Kubios is the software that gathers information from the Nexus
  • Look at MindMedia
 Alexander Hanson Data Analyst


  • Concerned about the data storage capabilities of the platform – they had to move away from excel b/c it couldn’t store the data
  • Merging, regression, multiple analysis of variance is important
  • Emphasized that auto population of data is pivotal to her job
Jim Arp HDP Director


  • Concerned about data storage and scalability





  1. The main users of the software product is the data team and the research psychologist
  2. Excel is the current data analysis tool. SPSS is only on one standalone Mac that is not connected to the internet. Few complex analysis has been done
  3. Interviewees all like the design of the current prototype but would like to see more personalized page with differentiated product feature for each beneficiary E.g. the home page for CPC and data team would be different since CPC mainly want to see the result in a better data visualization form to facilitate their training while data team want to run actual analysis
  4. Time series analysis is the most important analysis they want to have on the prototype so that they could see changes over time
  5. While being able to run statistical analysis on our product would be a big plus, beneficiary want us to position it as a data storage/management tool
  6. The bottom line is the ROI: they are investing money and want to know how money is creating results)
  7. Being able to answer the following four questions from our prototype would be the key:
  • What is the exercise that provides the biggest performance increase?
  • What is the performance machine/training/process that creates the biggest change?
  • Who are the people that have the biggest change and smallest change?
  • When do people start showing performance training over time?





  1. The biggest problem is how will we get the data from the hardware that we used as inputs for the software prototype
  • All current devices have bluetooth and they could transmit the data to the computers if connected to internet
  • But none of the current devices are connected to internet and there is little we could do about it
  1. Given the time constraints and skills available to the team, we think the best way to tackle this complex problem and to deliver a prototype that could solve both the input and the output problem is to leverage existing commercial solution for the hardware and focus on software prototyping to create a data management and visualization platform. Nevertheless the premise is these device are connected to the internet.
  2. The process of getting both software and hardware approved is very complicated and could take 6 month or even more.
  3. This is still true: that the structure and procedure for data collection, analysis, and storage is changing week to week within HDP. Week to week, we learn of new developments in many of our beneficiaries’ roles as it relates to data analysis, connection, and storage.
  4. The prototype could proceed to different directions and the feedback we received from interviewees compose a wide range of product feature requests and also has some conflicting information.
  5. This is still true: We do not have the devices with us so it is difficult to test our hardware MVP.





  1. We need to validate the possibility of having current existing devices connected to internet since they would change the way how we shall approach the problem
  2. The solution has to be a hardware + software product to solves both the input and the output problem
  3. While we are figuring out the hardware part, we should continue refining the software prototype
  4. We will commit to do more research on other commercial offering and how compatible that would be with HDP devices
  5. We will continue to commit to identifying companies that are willing to send new devices and data specs so that we more easily gauge the feasibility of creating a device from scratch


Week 7 Insights

We optimized our interviews to validate our assumption that (1) an Arduino or particle.io-based micro controller system would be pass the security laws at Fort Bragg (2) the micro controller system shown below is scalable and would fulfill the design constraints set by LTC Phillip Thomas and James Arp and (3) to discuss the feasibility of the proposed system both in terms of necessary skills and the time that we had available to work on an original prototype. In order to be fully prepared, we also explored commercial solutions in case we wouldn’t be able to deliver a proper prototype.


Primary Data Users

  1. Cognitive Performance Coaches
  2. Data Analysts
  3. Research Psychologist

Other Beneficiaries

  1. Instructors (Cadre)
  2. Trainees
  3. SWIC Commander


Vatrina Madre Information Technology Director | SWEG


  • We need to figure out if our device will be on the lang or NIPR networks.
  • Requirements for approval: cannot create vulnerabilities, must be compatible with Windows 10, cannot create risk
  • Bluetooth can be approved, although it is hard.
Maj Arth Commander’s Action Group, Director | Joint Special Operations Command


  • Military regulations are unclassified and open to the public -> ask LTC if he can send us rule regulation governing connectivity standards
  • Interview someone who pushed for new connectivity rules
  • Military bureaucracy creates more stringent rules as there are more and more rules as you go down the chain of command
Rachel Feher Congressional Research Service


  • Advisory Board – works specifically in healthcare consulting, look at what information or research on what healthcare is doing
  • Talk to people at Duke Hospital – rehabilitation department
  • Soldiers at Walter Reed National Military  Medical Center are meeting with so many doctors, and they have a central database for which to track each patient.  
Dr. Lawrence Appelbaum Director of Human Performances Lab | Duke


  • Different radio frequencies mean that we need to time sync
  • Proprietary information from different sensor makers will make synching difficult.
  • Arduino syncs by sending out an orientation pulse from each device and then it gets a timestamp
  • Tobii (lots of data), hrv (very little).
Yao Yuan ECE Student | Duke


  • Recommended Firefly hardware DIY platform
  • Recommended tutorial for learning circuit design
  • Willing to help with hardware prototyping if we needed it
Mitch Heath CEO | Teamworks


  • Conduct more MVPs and really try to understand your problem sponsors prototyping problem
Kyle Janson ECE BME Student | Duke


  • Willing to put us in contact with other ECE people working on data problems
  • Might not need to use a microcontroller. Consider looking at 3rd party companies that offer data integration but not necessarily collection
Mark Palmeri MD PhD and ECE Professor | Duke


  • Learning the skills to create this component/ device might take more time than is allotted to you. It depends on how much time the team has willing to give.
  • Willing to give time to go over possible device companies that are doing data integration
Trevor O’brien SWEG IT | SWCS

  • Hardware solution (chip set) could work, but may only be a short term patch with high future sustainment costs.
  • A network/web app solution, ideally with a 3rd party vendor would be ideal.
  • A thin client solution, where the software is hosted on a server and the devices only need storage and RAM could save up to $15 million.
Rich Diviney Retired Navy SEAL/Seal Team 6 Instructor

  • SEAL school has similar issue with attrition, A/S at 87%.
  • We should know whether or not attrition is coming from people giving up or from failing out. Special operator recruitment has changed in part due to pop culture influence, this leads to recruits who want to be hot shots and get disillusioned early on (in that case they are getting the wrong people).
  • Focus could change who is being recruited so that they get the right mental profile. Even though this seems like an obvious problem but school is generally blind to it (outsider perspective useful).


  1. The data analysts and the data managers would still be the main point of contacts to receive the raw data
  2. Bluetooth is now possible. From our interview with Vatrina Madre, we learned that there are two networks that we can operate on: Lang and NIPR. NIPR is more “black and white” and Lang is more lenient as far as devices to connect.
  3. The new facility will be up and running in 2-5 years.
  4. Creating a prototype from scratch seems infeasible given the time frame and the resources available. One commercial solution in particular, StelLife (introduced to us by Steve McClelland) is a strong candidate due to high data integration capabilities and scalability.


  1. The biggest problem is that given the time constraints and skills available to the team, creating a prototype from scratch seems less feasible. The team is stretched in regards to managing workflow given the high amount of deliverables and the capabilities of the team.
  2. This is still true: that the structure and procedure for data collection, analysis, and storage is changing week to week within HDP. Week to week, we learn of new developments in many of our beneficiaries’ roles as it relates to data analysis, connection, and storage.
  3. This is still true: We do not have the devices with us so it is difficult to test the effectiveness of our MVP. We have tried to ask for dummy data, but we faced barriers regarding confidentiality.
    1. We also have not been able to get in contact with vendors to supply a possible solution.


  1. We have mapped out a new workflow in which half of the team works on interviews while the other half of the team works on prototyping. We are currently going to reconsider this workflow at the end of this class in case team members feel overworked.
  2. We will commit to identifying companies that are willing to send new devices and data specs so that we more easily gauge the feasibility of creating a device from scratch
  3. We will look more into StelLife and other possible commercial products as a means of providing a solution to our problem sponsor.


Week 6 Insights

We utilized our ten interviews this week to accomplish two goals: 1) gauge reactions to our MVP 2) understand necessary security and training environments that limit our MVP. Additionally, we talked to experts in the field like Phil Williams and Dr. Janson to understand relevant research and tools to build our MVP. Through conversations with experts, we drew a MVP as a team and tested the desirability of our MVP during interviews. In this process, we naturally learned of more limitations that we must account for as we continue to iterate on our MVP. At the end of this week, we have a more clear idea of how to modify our current MVP to address additional pain point and limitations that we learned during interviews.





Primary Data Users

  1. Cognitive Performance Coaches
  2. Data Analysts
  3. Research Psychologist

Other Beneficiaries

  1. Instructors (Cadre)
  2. Trainees
  3. SWIC Commander









  1. Due to recent procedural changes, the data analyst and data manager would be the main individuals receiving the raw data.
  2. A SD card cannot be directly injected into the military computers, because a SD card is not an approved device. Rather, the data analyst would inject the SD card into a personal computer and send the dataset to his/her military computer.
  3. The biometric devices will be used within the new training facility under normal temperature and terrain conditions. Therefore, the solution we provide does not have to withstand extremely tough conditions.
  4. The new training facility will accomodate for the needs of our MVP, if it proves to be useful and relevant.





  1. Our MVP must work within the security constraints. Many interviewees emphasized that our solution may need to work without wifi and/or bluetooth which has limited solutions to transfer data from our MVP to the data analysts’ computers.
  2. The structure and procedure for data collection, analysis, and storage is changing week to week within HDP. Week to week, we learn of new developments in many of our beneficiaries’ roles as it relates to data analysis, connection, and storage.
  3. We do not have the devices with us so it is difficult to test the effectiveness of our MVP. We have tried to ask for dummy data, but we faced barriers regarding confidentiality.





  1. We need to learn about the specifications of our devices by speaking to the companies that created these devices. Only by understanding device specifications can we build a MVP that can receive info from all devices.
  2. We must understand what data each device stores and seek to create dummy data to test our MVP.
  3. We will research about Zigbee to determine storage capabilities, non-wireless communication capabilities, and costs.
  4. We need to identify other similar commercial solutions for our problem.


Name Title Email Date Interview/Interviewee Takeaways
Phil Williams CEO of Phil Williams LLC; Advanced Research, RDT&E, Leap Ahead Technology, On the Move Communications phil.williams@LInkToPhil.com 2/9/19 AJ/BX
  1. Compatibility of raspberry Pi with the current three hardware
  2. Use case scenario of the product (environmental factor)
  3. Philp would be extremely helpful for us if we actually want to deliver a working product (Even by the end of day, all we deliver is just a MVP, it would be great we make detail recommendation and plans to LTC Thomas and his team about how to carry this project further)
LTC Jesse Marsalis Program Manager Special Programs jesse.r.marsalis.mil@mail.mil 2/11/19 AJ; TL
  1. Specific job – develop and acquire capabilities within Special Operations command
  2. Might be good person to speak to once we have a MVP
Alexandra Hanson Data Analyst alexandra.hanson.ctr@socom.mil 2/12 BX
  1. Need a system that could incorporate both the biometrics data and the cognitive data
  2. Other unit is also using tablet for self-assessment, HDP is working on funding, the fastest turnaround would be 6 month
  3. Would like us to research and provide a comparison on different tablet offering, ex: iPad vs Samsung
  4. Military like action item, end the report/presentation with a list of action items
  5. Present the solution in an incremental plan
  • Step 1:
  • Step 2:
  • Step 3: if have $$$/enough resources, you could..
SFC Jeffery West Cadre Language School. Non-Commissioned officer jeffrey.l.west@socom.mil 2/12/19 NC
  1. 92% of trainees get to +1 area after 3 week training program
  2. Human feedback is the best. It can’t be beat by a computer. Trainees need to interact with people from that culture.
  3. Government employees here not contractors.
  4. Military personnel are constantly on rotational assignment (3 years)
Constance Garcia Data Manager constance.garcia.ctr@socom.mil AJ 2/12/19 -they would have to use personal devices – to get the data from the Zigbee, we have to plug in SD card to personal devices and then send dataset to work laptops

– on a scale of 1 – 10 of how helpful would this be? 10

-Alexandra and Constance will be using the SD cards from the Zigbee & do analysis from time to time rather than constant

LTC Phillip Thomas Director of Academic Instruction, SWEG phillip.thomas2@socom.mil All 2/12/19
  1. Identify other commercial solution
  2. Asking for spec
  3. Bottom line: our proposed solution need to be flexible enough to accommodate all kinds of data, including cognitive data and allowing data team to manually input self-assessment survey
  4. LTC Thomas wonders where the repository of the data that going to end up


Brian Hackett Founder of the Learning Forum bhackett@thelearningforum.org TL 2/12/19 – Experience working with the Navy attempting data collection, but process broke down.

– cpc’s and other contractors working with the navy SEALs were underpaid/undertrained, led to friction.

– lack of communication between SEAL school and SWIC, even if they were working on the same thing.

-nail in the coffin for the SEALs  was privacy concerns, killed any data sharing.

– He has connections to other people who did work the SEALs. (Potential leads).

Haig Nalbantian Senior Partner, Mercer

Workforce Sciences Institute

haig.nalbantian@mercer.com TL 2/13/19 – pioneer of Internal Labor Market (ILM) analysis.

– worked with the Navy and used ILM to help identify key skills to improve upon.

– didn’t work with special forces, but has contacts that may be useful.

Kyle Janson MEng Biomed kyle.janson@duke.edu NC 2/12/19
James Arp HDP Director james.arp@socom.mil BX/NC 2/13/19
  1. The facility will accommodate our proposed solution
  2. Getting rid of the middle step of placing micro-computer to soldiers’ jacket
  3. Talking to industry expert and learning how tech company solve similar problems
Barbara Plotkin CPT USSOCOM former instructor barbara.j.plotkin@socom.mil NC 2/12/19
  1. A device that allows quick insights would be helpful for the soldiers.

Week 5 Insights

This week our hope was to better understand some of the reasons for attrition through the special forces pipeline and try to see where we can provide value added, if possible. We found where a lot of the attrition is coming from. Of last year’s class of 1200 who applied, only around 500 made it all the way through. SWIC’s CG wanted a graduation rate of around 800. He began an initiative called Performance Integrative Training (PIT), as a way for soldier who don’t pass a module the first time to get help to achieve learning goals. PIT is based on HDP models for cognitive performance. This demonstrates that there is optimism in HDP’s training methods and there will be buy in for a data driven solution.




Primary Data Users

  1. Cognitive Performance Coaches
  2. Data Analysts
  3. Research Psychologist

Other Beneficiaries

  1. Instructors (Cadre)
  2. Trainees
  3. SWIC Commander
  4. Performance Integrated Training (PIT)




Week 5 Title Contact Key Takeaways Interviewer
Major Chuck Schumacher Operations Officer, SWEG charles.schumacher@socom.mil Performance Integrative Training (PIT), SWIC Commander’s initiative to get trainees through failed modules. Based on HDP work.

Class size of 50-60, 50% success rate.

JC Crenshaw SOFCCC Course Manager john.crenshaw@socom.mil Right now there are 11 instructors.

Only 33% of enlistees and 40% of Officers make it through assessment and selection.

Highest attrition from physical/mental assessment and culminating exercise.

Col. Joe Blanton Program Executive Officer, SOF Support Activity joseph.blanton@duke.edu   acquisition team works with communication team to determine what devices can be put on the network. Necessary step for any new equipment we’d be bringing them. AJ
Captain Oscar Gonzalez HDP Research Psychologist oscar.gonzalez@usuhs.edu Concerns arising over scope. Are we still trying to solve the problem that was posed?

Is the data we are collecting useful for providing an MVP?

Ian Ankney Lead CPC
CPCs are not involved in assessment selection. The new facility may be up to six years away.

Cadre operate fairly idiosyncratically. Since they don’t all measure the same things this leads to confusion. Soldiers are often told they are being measured one way when it is actually something else is being measured.

Michael Jelen H4D Course Advisor michaeljelen@gmail.com Rather than a central server, utilizing edge computing may be a better and lest costly solution. This would involve getting the biometric devices to sync to something the soldiers could wear ideally. TL
Major Amar Mohamadou SWEG Executive Officer mohamadou.amar@socom.mil Each SWIC dropout costs the army $30,000-$35,000.

Recruit Class was 1200 this year.

PIT saved $1 million this year.

Lieutenant Adam M. Beauregard Lieutenant, Navy adambeauregard@gmail.com We should think about what kind of data to collect,

determine what format the solution should be based on.

Focus on really understanding quantitative metrics for green beret training

What insights do we want the instructors to be able to easily obtain from the data?

Focus on wearable tech.

Major Ben Spain Major, Air Force benjamin.spain@gmai There is a price threshold: below the price point, commander can make purchasing decisions for software. Above that point then need to go through the bidding process.

There is an entire unit working on the bidding process, (highly complex).

Lieutenant Colonel Ormond Brendan SWEG Deputy Commander, Language Group brendan.ormond@socom.mil How do we deal with measuring intangibles like leadership?

Problems with shrinking recruiting class, both witin the army for SWIC and in the general US population.

Any solution is cost prohibitive, not only in procurement but also in time spent on implementing and maintaining any data system.




  1. Attrition is concentrated around Physical/psychological assessment, small unit tactics and the culminating exercise. Cost to the Army for relocation of trainees is around $30k.
  2. Without a way to measure some of the intangible elements in selection, like leadership, it will be difficult to get buy in for a data driven solution.
  3. The idea of a central server for data is becoming less feasible. A better solution might involve wearable tech that syncs to the biometric devices via Bluetooth.


  1. Now that we have a picture of where the data collection process is breaking down, as well as a general picture of where soldiers are experiencing difficulty in the pipeline, we need to begin developing a prototype. To get more insight about this, we need to find models of effective data management that we think, given SWEG’s constraints can be applicable to our problem.

Week 3/4 Insights

In the past two weeks, we have interviewed 20 beneficiaries which include a mix of primary and secondary data users, as well as higher ups(generals and commanders). Besides, we had the very precious opportunity to meet with general Dempsey, the 18th Chairman of the Joint Chiefs of Staff and the 37th Chief of Staff of the Army, and general McChrystal, the joint Special Operations Command in the mid-2000s. We have gained a broader view of how military adopt technology from these two prestigious military leaders. Moreover, we have visited Fort Bragg on 1/29, which we gained hands-on experience on current in-placed tracking devices and data analytics tools.




We organized our beneficiaries in three main groups listed below:

Primary Data Users

  1. Cognitive Performance Coaches
  2. Data Analysts
  3. Research Psychologists

Secondary Data Users

  1. Physical Therapists
  2. Spiritual Advisors
  3. Cognitive Performance Coaches
  4. Interpersonal Coaches
  5. Strength Trainers
  6. Dietitians
  7. Other Sports Medicine Specialists

Higher Ups (General and Support Staff at SWCS)

  1. General Santiago
  2. Support Staff/ Board that drives downstream policy changes.




Name Title Email Takeaway Interviewer
Hector Agayuo SWEG Command Sergeant Major aguayoh@socom.mil Lack of bandwidth of personnel to collect data
He looks outliers or errors (compare that with the actual situation)
Which means he need to know each soldiers quite well, how would he be able to do that?
He goes through 6 months of CCC data in 2-3 hours (110 surveys) 
Greg Santiago Operations Specialist, SWEG gregorio.santiago@socom.mil Logistic & operation planning does not involve a lot of data related to our project

Same biometrics data may means different things as each individual is unique. How to assess data can be much more difficult than how to collect them  

MAJ Chuck Schemacher; Operations Officer, SWEG charles.schumacher@socom.mil Is SWIC measuring the right stuff? Why do many students who pass Assessment and Selection drop out later on?

Brass want more graduates, cadre want better quality graduates.

New generation of soldiers are different to train, causes friction.

Bob Jones Communications Language School Director HDP operates here to evaluate the trainees effectiveness at negotiation NC+BX
James Arp HDP Director james.arp@socom.mil Funding comes from:


We have to justify the value of the training programs to the 2,3, and 4 star general commands

General McChyrstal  (x2) he joint Special Operations Command in the mid-2000s Must change the culture as well as the technology within the military All
Mike Taylor HHC 1st Sergeant michael.s.taylo More robust data collection/analyis would help determine what training interventions work and what does not.

cooperation/information sharing is generally facilitated by relationships, if they aren’t there, then generally different units won’t communicate.

General Dempsey the 18th Chairman of the Joint Chiefs of Staff and the 37th Chief of Staff of the Army Need to focus on what problem we can solve

Technology is changing the landscape of the federal gov and DoD

Focus on the cultural aspect of getting military

COL Bill Rice SWEG Commander william.rice@socom.mil Higher-ups at SWEG don’t have much contact with the committees.

SWEG doesn’t have a committee that makes data-driven decisions.

Peer feedback, instructor feedback, and some data informs human performance results.

30 day ideal innovation turnover for new devices. He would like to be able to test new hardware/software without jumping through the regulation/ permission hoops.

Use case:

Immediate feedback mechanism as the top use case priority from Commander Rice point of view. Have expert as a reference point for novices

using big data/database to make key decision such as selection process/recruiting. Track each trainee’s performance/data throughout the training process

Major Chuck Is SWIC measuring the right stuff? Why do many students who pass Assessment and Selection drop out later on?

Brass want more graduates, cadre want better quality graduates.

New generation of soldiers are different to train, causes friction.

Stephen M. Mannino, Edd Human performance program coordinator stephen.m.mannind@socom.mil Showed the database that strength and conditioning coaches use to advise

Strength and conditioning coaches have to download to share the database with other data users (physical therapists, CPC’s, data analysts, research psychologists)

Justin Jones Strengthen conditioning coach justin,jones@socom.mil Names are declassified – only coaches have access to the names

Database’s real time feedback has been helpful in readjusting physical training

Taylor McKinney Physical therapist Accessing the medical database always leads to problems

No protocol for how physical therapists make data-informed decisions

Sara Butler Physical therapist Would really like a database that they can get to without taking up so much time

Did not interact much with CPC’s, data analysts, and research psychologist

Alexandra Hanson Data Analyst alexandra.hanson.ctr@socom.mil Getting buy-in from higher ups is necessary to help increase funding and implementation

Acquisition for different accounts at SWEG is siloed which means that different solutions will not arrive at the same time.

Constance Garcia Data Manager constance.garcia.ctr@socom.mil Government shutdowns affects contractors.
Daniel Gajewski Performance Integrator Daniel.gajewski@socom.mil The Toby eye tracking device just arrived and they are still working on calibrating it

The current output of that eye tracking

device is just a video

Has another software platform for further analysis but they haven’t start yet

Dawne Edmonds Process Improvement and Project Management

US Army Special Operations Command

Dawne.edmonds@socom.mil There’s no authoritative data source. It means that there is not a single data source that the analysts rely and trust upon.


They tend to type all the data all over again. This is a constant problem. Army are multi-service.

Oscar Gonzalez Research Psychologist oscar.gonzalez@usuhs.edu Is in the process of holding CPC’s more accountable for inputting and collecting data

Working on concerns from data analysts about the large amount of time spent toward data input






  • Data utilization is actually quite low. The decision making at the committee level are mostly based on intuition and past experience
    • Many data (both biometrics and cognitive) are not collected due to lack of tracking devices
    • Even for those collected data, most of them are not being utilized
  • Most data are self-reported in the form of survey or self-assessment, which involves basis
  • Most database are not shared internally, many secondary data users work in silo and do not have access to each other’s database




  1. Secondary users have three or four different data systems for one function
  2. Secondary users have to get data manually
  3. There is no central database for the data from biometric devices
  4. Secondary data users work in silo
  • Hard for secondary data users to collaborate with each other
  • Hard for secondary data users to collaborate with primary data users
  1. Lack of a systematic workflow among primary data users
  • Some CPC collect and scrub data, some don’t
  • CPC does not have a standard process or expectation on data-related work
  1. Errors in data collection (ex: bear incident)
  2. No plan for biometric devices and what outcomes they should have
  3. Training feedback
  • Soldiers do not receive real-time feedback
  • Soldiers do not receive specific feedback on training performance
  1. Who gets access to which data
  2. The regular maintenance of the system & platform and periodic update




  1. We need to define the scope of the problem and narrow down the problems
  2. Define the key pain point and focus on the corresponding problem
  3. Re-identify key beneficiaries based on the problem
  4. Identify use cases for the re-identified key beneficiaries
  5. Prioritize use case
  6. Start brainstorming potential solution for each use case


Week 2 Insights




To simplify our business model canvas and arrange for a more direct interview strategy, we organized our beneficiaries in three main groups listed below:

Primary Data Users

  1. Cognitive Performance Coaches
  2. Data Analysts
  3. Research Psychologists

Secondary Data Users

  1. Physical Therapists
  2. Spiritual Advisors
  3. Cognitive Performance Coaches
  4. Interpersonal Coaches
  5. Strength Trainers
  6. Dietitians
  7. Other Sports Medicine Specialists

In identifying this group, we also learned that a representative from each of the 7 groups in the secondary data users is weighing in on the facility design and architecture so that their work needs are met.

Higher Ups (General and Support Staff at SWCS)

  1. General Santiago
  2. Support Staff/ Board that drives downstream policy changes.
# Name Position Dep. Email Note Take, Interviewer Date Notes
11 Vatrina Madre Information Technology Director SWEG vatrina.mardre@socom.mil NC/BX 1/16
  • Testing process before getting approval
  • 3-6 month on average to get approval
  • The new equipment need be on the Disa, if not need to submit to Disa (take 2-3 month), then go through certificate to operate
12 Aspen Ankney SOCEP CPC SWEG aspen.ankney.ctr@socom.mil AJ/BX 1/14/19 -data manager needs to collab w/ CPC to make sense of the data or else data goes to waste (the current collaboration isn’t quite efficient/smooth

-CPC -> data analyst ->research psychologist is ideal rather than what is happening now

-CPC: 70% is working with data (cleaning/adjusting spreadsheet/running analysis)

20% is actual data collection of it, do hard copy,

10-15% of the time spend with other CPC, learning  what are they working on

5% Research, finding the norm, what other methods

-only 40-50% CPC’s collect performance enhancement data

13 Ian Ankney SOCEP CPC SWEG ian.t.ankney.ctr@socom.mil BX MOVED TO NEXT WEEK – SICK
14 Constance Garcia Data Manager SWEG constance.garcia.ctr@socom.mil BX,AJ -lack of communication between CPC and data analysts

-CPC’s drop off data in person, and data analysts have to manually put in data (data input takes up 70% of her time inputting data)

-sometimes CPC want data to be inputted and not analyzed

– Besides lacking an integrated data platform(from the tech side), it seems human factors(lack of communication and collaboration) and inefficient processing (manual input, no standard protocol of the format of the data) also contribute to the problem.

15 Curtis Price Deputy to the Commander SWEG curtis.price@socom.mil AJ; NC 1/15/19
  1. Curtis sees the organism approach as a more efficient manner of human development
  2. Problem is in resource prioritization. HDP has different priorities. Training, injuries. His priority is within longer term, longitudinal studies.
  4. Good contact for people outside of HDP
16 Dr. Tom Duncan Performance Integrator SWEG tommy.duncan.ctr@socom.mil


BX; AJ 1/16/19 -Oscar has a budget

-Oscar has been sitting in on meetings about religiousness for the performance integrator (moving toward the ideal model now)

-discrepancies with how many CPC’s collect data – this person estimates 50% collects data

17 Alexandra Hanson Research Analyst SWEG alexandra.hanson.ctr@socom.mil TL -We need to be able to analyze the problem from a military context. Data gets lost due to personnel turnover, lack of SOPs to stop this from happening

-Security is the primary concern, more so than sharing. Data being abused is already a problem. Countermeasures from cyber attacks and EMPs are also a consideration.

-Any solution we provide should be evaluated on whether or not more Spec Ops soldiers are coming out of the program. Ineffective data collection is actually hurting some recruits, washing them out based on technicalities

18 Dr. Morgan Hall SOCEP CPC SWEG Morgan.hall.ctr@socom.mil TL, AJ 1/16/19 -some devices are “shiny” but aren’t effective or necessarily research based

-CPC’s are well integrated with each other but not with the data analysts and research psychologists

-CPC send all the raw data to the data analyst -> no standard procedure for CPC’s of what to do with data  

19 LTC (Dr.) Mike Devries Command Psychologist SWCS michael.r.devries@socom.mil AJ/BX 1/13/19
  • Special workfare school split between special training, education and medical
  • Two option: a research report either go to SWIC HQ or publish to a journal
  • Clinical psychologist(similar to industrial psychologist) collecting psychological data at an earlier stage, does not collaborate well with CPC.
20 Dr. Megan Brunnelle Head Physical Therapist SWEG NC 1/17/19 -Three Systems: army medical systems, SPEAR, medical imaging system + Profile system; Cognitive and conditioning notes are manual

-Accessing the server is hard – “not a day goes by that I don’t have trouble with the system”

-Talk to five people: sports medicine, physical therapist, strength and conditioning, dieticians, CPC, interpersonal coaches, spiritual advisors

21 Kelvin Bronson S6 Information Technology US Army JFK Special Warfare Center and School


kelvin.bronson@socom.mil NC 1/16/19
  1. New facility is still in development and design
  2. Hard to get approval to share information outside of the organization(intranet to internet)




  • The relationship between the CPC’s, research analysts, psychologists is more nuanced than previously expected. Half of CPC’s don’t complete data collection and most of the time, CPC’s are scrubbing data (~70% of time).
  • Another beneficiary group has been identified: the secondary data user group. These are the physical therapists, spiritual advisors, dietitians, etc.. These are people that aren’t directly present for data collection during training but still used the collected data.
  • PAIN POINT: “Not a day goes by where we don’t run into problems with the citrix server. It’s usually hard to log on.” It’s clear that the citrix server for all the other data servers is inefficient and inconsistent. Targeting other secondary beneficiary users would allow deeper understanding of the issues at human dynamics and performance (HDP).




  1. We need to consider a whole other world of potential beneficiaries: the secondary data users. These are people that interact with the data after training and use insights from training to inform their own functions. For instance, the physical therapists use training data to provide more perspective on a patient’s physical well-being, recovery time, and supplementary exercises.
  2. There’s nothing to tell an incoming CPC what their specific function is within a data collection study. Our team should pursue interviews in areas that provide insight about transitioning incoming contractors.

Week 1 Insights

After interviewing ten customers, we know that the training facility will use various devices that tracks eye movement, heart rate variability, motion, and other human performance metrics. However, each hardware collects and stores data separately, and there currently exists no way for the data to be stored in a single location. The military lacks an efficient way to store this data which leads to the lag in the analysis of the data. More specifically, we discovered that data is in the hands of the Cognitive Performance Coordinator (CPC) who gets the raw data must spend a lot of time scrubbing the data before giving the data to the data analysts. The data analysts support the research psychologists in evaluating the training program. Our customers emphasized that the data should be continuously updated as new data occurs.

Next week, we plan on interviewing potential trainees. All of our interviews this week were with individuals who would be facilitating or leading the training program. Speaking with trainees will allow a more holistic picture of the training program. Additionally, we plan on speaking to more data analysts. The data analyst can address our questions of their daily technical functions as well as their inability to work with raw data. Apart from more diverse interviewees, we plan to clarify on the timeline in training site construction, type of biometric devices that are currently used and future biometric devices that will be used, and why the data analysts cannot scrub the raw data.


Name Job Description/ Role Designation Key Takeaways Email
Oscar Gonzalez Research Psych -training needs military person and CPC
-facility not yet built-psychologists would like to analyze rather than clean data
SSG Trevor Obrien S6 Information Technology US Army Special Operations Command(USASOC) -technology are in different versions-device to device communication is important

-SPEAR: both a hardware and software solution

Rick Dietrich SOCEP DIR -Cognitive Performance Coordinator (both Dan Sproles & Dan Gyetsky) gets raw data -> scrubs data -> gives data to data analysts-raw data is on CPC’s laptop so CPC’s have to scrub data to transform it into something that data analysts can use – it takes a lot of time for CPC to scrub data Frederick.d.dietrich@socom.mil
Phillip Thomas Director of Academic -equipment must be approved for continuous software updates (otherwise manual updates on an unconnected computer)-SOCOM initiative – all data is inputted manually right now phillip.thomas2@socom.mil
Jim Arp HDP Director US Army Special Operations Command(USASOC)

Special Warfare Education Group


-smart system automatically collects and uploads data-problem: measure impact of the training programs james.arp@socom.mil
Kelvin Bronson S6 Information Technology US Army JFK Special Warfare Center and School(SWCS) – high demand for coaches (want to serve more students) and currently coaches/CPC(especially before Oscar came) need to take time away from training students to process data.-Thus, one of the main goal of our solution should be to save their time and energy so that they focus on their main task. kelvin.bronson@socom.mil
Steve Mannino THOR3 US Army JFK Special Warfare Center and School(SWCS) -training facility can house 5000-number of staff available is a weakness for training

-SPEAR: not user friendly enough

Seth Data Analyst -Dealing with Silos: Either SOCOM or USASOC commander could initiate changes for information sharing. stephen.m.mannino@socom.mil
Dan Gajewski SOCEP CPC US Army Special Operations Command(USASOC)

Special Warfare Education Group


-Event-based data tracking during an activity.-Find a way to relate data each other in real time.

-Come back to him after a few interviews.

Dan Sproles SOCEP CPC US Army JFK Special Warfare Center and School(SWCS) -Limitations: soldier to coach ratio. (1:40 or even 1:200). Even new tech solutions need to work with scale.-Scaling of coaches will likely take more time, extra strain on coaches in the beginning. DANIEL.J.SPROLES.ctr@socom.mil