In collaboration with Leighann Vinesett and Aimsley McDaniel we helped the Laboratory for Analytic Sciences at North Carolina State University come up with tools to help Junior Target Digital Network Analysts in their process.

When searching through massive amounts of data points and inputting queary searches it is really difficult to identify when there are missing data points or data gaps. 

In the Kaffeinate investigation, Josh’s main goal is to gather information about the drone’s flight path on and before April 10th. Last Friday, Josh received the drone’s damaged RFID chip and attempted to query information from it but was unable to begin his analysis before the end of the day. He resumes this inquiry on Monday, picking up where he left off.

Josh has been working for about 7 months as a network analyst and is taking college classes after-hours for a degree in Data Science.

In the investigation of the bombing in Macondo, Josh is tasked with analyzing data retrieved from a RFID chip extracted from the drone in order to find details about the drone’s flight path.

It helped us understand the workflow and struggles that Junior TDNA’s encounter in their day to day, which allowed us to establish Josh’s pain points:

• I’ve got a blank space baby...Due to the lack of system notifications for gaps or errors, Josh often doesn’t know a gap exists. is lack of clarity leads to potential oversight and inefficient analysis.
  
  
• It’s me, hi! I’m the problem, it’s me...
  When Josh is able to identify a gap or error, he is left wondering if it is due to his query syntax or a system error. This leads to an over reliance on tradecraft and external consultation.

• Now we got problems, I don’t think we can solve them...
  Josh has limited time in his day to attempt to solve errors and gaps. This impedes timely resolution and reduces his overall productivity.

From our interviews, it was clear that we needed to build a system that communicated to Junior Analysts when there were errors when they conducted queries. We proposed two directions, one which visualized where the errors occured in the data and the other that walked the analyst through the solving the problem. Wer proposed both concepts to our interviewees and decided to combine both directions! 

One of my favorite parts about this project was the opportunity to develop stronger working relationships with friends! Working with big data can be hard, but when working with others you create memories that will last a lifetime!