Pain Points of Research
Understanding the world of academia
Our assignment was focused around helping a company, uReveal, improve and broaden their product to include more industries in their customer space and make the entire product more usable. Their main product claims to unlock the door to superior analytics for instant insights and essentially make much of the job of data scientists much easier and less time consuming. Our team was assigned to focus our research around academics.
Our goal was to take the information that we learned about academic researcher workflows and incorporate it into a world with uReveal’s product.
Team
Jayanth Prathipati, Stephanie Liao, Clare Carroll, Nora Tane, Neeraj Verma
Our team worked collaboratively on all parts of our class assignment.
Data Gathering Process
Interviews
We recruited three participants for our study which were comprised of a PhD graduate students, a professor and a librarian. We created a user protocol with the diversity of these participants in mind.
We created some questions that served as a guideline for our interview. In reality, we used them as starting points, but followed the conversations the questions created further.
Guideline Questions
- What are your job responsibilities and tasks?
- Can you walk us through the last time you went through [selected process or task]?
- What is your least favorite part of this task?
- What do you enjoy the most?
- How often do you have to do this task?
- How did you learn to do this task?
- How long did it take for you to learn this task?
- How long have you been doing this task?
- Did you use any other processes before?
Interpretation
Our team then interpreted each interview session with assigned roles including the Observer, the Recorder, and the Modeler. We went through the step by step details of what happened in each interview.
The Modeler creates Sequence-Flow models by following each coherent sequence of events, creating a track for each person or server that plays a role or communicates with the subject.
The Observer should watch what the Recorder is writing down and how the Modeler is creating models so that everything accurately reflects what happened during each observation.
The Recorder writes down any observations, insights, and breakdowns the team is generating.
Below are some examples of the notes that we took in our interpretation session:
U1–32 — I am frustrated because I have to pull out information from hundreds of papers and other studies to find patterns.
U1–33 — I gather information from literature in different documents, organized by theme and source.
U2–6 — I want to know if I could replicate my experiment results on different data sets, not just those from my own experiment.
U2–37 — I believe my data analysis tool is beneficial because it gives users easy access to analytical tools built on top of the platform.
U3–1 — I search for different websites based on the topic which I choose through experience
U3–41 — I want everyone involved in a particular request to know the same thing at the same time.
Modeling
From our interpretation notes and flow models that we generated, we created several models.
Flow Models
Our flow models were developed for each interviewee. These were pretty much digitized from the Modeler during the interpretation sessions.
Consolidated Flow Model
From these individual flow model, we created a consolidated flow model to pin down the similarities between the academic research flows.
From looking at our consolidated flow model, we could pin point the pain points that academics face in their workflow and developed a new preferred flow diagram that our solution would help enable.
Additionally, we took all of our interpretation notes, and created an affinity diagram to develop user derived insights from our data.
Affinity Diagramming
The insights that we derived out of our interview data:
- Researches handle diverse content
- Researchers frequently build upon previous work
- Researchers all have their own personalized workflow process
- Many researchers run into issues with the software that they currently use
- Collaboration is important to many research projects
Next Steps
To learn how our team utilized this information to create a solution, please move onto the next article in this process.
