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.

 

 

• BENEFICIARIES

 

Primary Data Users

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

Other Beneficiaries

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

 

 

• INTERVIEWS & KEY TAKEAWAYS

 

 

Lieutenant Colonel Thomas Academic Instruction Director | SWEG phillip.thomas2@socom.mil Want the product to answer the following questions: How many people are going through pipeline in a day? How satisfied are trainees interested? 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 oscar.gonzalez@usuhs.edu 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: 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?

Constance Garcia Data Manager constance.garcia.ctr@socom.mil 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   mike.s.williams1@gmail.com 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 gdale@duke.edu 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 Daniel.gajewski@socom.mil 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 trevor.obrien@socom.mil 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 Frederick.d.dietrich@socom.mil 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 Frederick.d.dietrich@socom.mil 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 james.arp@socom.mil Concerned about data storage and scalability

 

 

• KEY INSIGHTS

 

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?

 

 

• KEY PROBLEMS

 

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.

 

 

• KEY DECISIONS

 

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