This summer, I had the pleasure of working with the Ideba team as a research intern. The internship title was a familiar one, but I was nervous that my previous experiences wouldn’t translate well. During the school year, I study Human Biology and work in a neuroscience laboratory developing nanoparticles for targeted drug delivery. I have spent hundreds of hours staring a data sheets, aiding in experiments, and learning how to effectively communicate scientific findings to different populations, but I had never performed a Win/Loss analysis before this summer.
Thankfully, I received incredible support and guidance from the team. By the second week, I was building my business vocabulary and becoming much more comfortable with qualitative analysis. As I wrap up my time at Ideba, I wanted to reflect on and share some of the similarities I have identified between my quantitative and qualitative research experiences.
The Importance of Organization
Whether there are 5,000 people in a dataset or more than 30 questions in an interview guide, it is critical to have a plan to organize the data. It is a daunting task, and there is a lot of pressure to get it right on the first go. Unfortunately, there is no “one size fits all” for data organization. It is trial and error. What works for someone’s brain might not work for another’s. Good data organization practices include consistently labeling files, establishing a backed-up location for the data and work, and ensuring usability, such as adding notes in the code, so others understand its purpose. Within that same realm, I have learned another practice that I will be applying to my future work: be kind to your future self.
I was completely overwhelmed by the Win/Loss spreadsheets at first. I saw a completed version and was struck with information overload. However, I was soon able to realize how efficiently the spreadsheets were designed. In the spreadsheets, bullet-pointed highlights accompany the quotes we have pulled for the interviews, allowing us to quickly refresh our memory days or weeks after the interview. We front-load much of the work with this format. As a result, creating presentation slides is a breeze. All of the information is easy to access, and I do not need to reread hundreds of quotes to identify common threads.
Teamwork and Collaboration
While it is a cliché saying, teamwork really does make the dream work. The research process is not an easy one; experiments fail, interviewees do not elaborate on their responses, and deadlines appear seemingly out of nowhere. Research projects can be lonely at times, too, when working in a lab or virtually. It is important to lean on your team in these moments. Certainly, there are more steps involved when working virtually, but weekly check-ins and quick conversations on platforms like Zoom Team Chat have helped me reduce the feelings of isolation. (We talk more about working virtually and staying connected in this blog post.)
I truly love the collaborative process and being able to bounce ideas off another person, especially in a research setting. Everyone brings a unique perspective to the table from their lived experiences, which I find to be invaluable. At times, test results and interview comments can be confusing and hard to interpret. A Google deep dive may provide the answers, but I have found I learn more when talking with someone about it. I find that I remember the solution better too.
Strangely, one of my favorite tasks this summer was proofreading presentation decks. Besides being happy to help out the team, I greatly enjoyed the opportunity to see what other people were working on and learn about their projects and presentation styles. I was able to draw inspiration from decks that I had proofread when I was given the opportunity to build out some of the slides. It became a win-win situation for both parties and turned what could have been a monotonous task into one I looked forward to doing.
Presenting Results
As a scientific communicator, presenting my work is often my favorite part of the process. There is a sense of pride, satisfaction, and competition when I share my findings with others. With both types of research, the end goal is to have gathered sufficient evidence to prove or disprove a hypothesis and provide understandable insights into the topic of study. For scientific research, this often results in writing a paper and creating a poster to present at conferences, while a presentation deck is the typical end product for market research.
Regardless of the presentation genre, I am a big proponent of including data visualization elements in my final products. There are many different camps and approaches in this world (I find myself best aligned with Knaflic’s principles), but the overarching idea is the same. They are a quick, easy way for viewers to see how the product compares to competitors or the control. Additionally, tables and graphs can make the information more digestible, accessible, and easier to remember. Tableau, a visual analytics platform has similar thoughts, “The importance of data visualization is simple: it helps people see, interact with, and better understand data. Whether simple or complex, the right visualization can bring everyone on the same page, regardless of their level of expertise.”
Admittedly, most data visualization formats cannot be executed when primarily working with qualitative data. However, even a simple inclusion of a table or bar graph with a CSAT study presentation can make a world of difference. When creating graphs, always remember to ensure your graph axes are correctly scaled and the colors are colorblind-friendly (BYU has an easy tool to ensure your graphs are accessible).
I cannot believe that my time with Ideba is almost complete. Thanks to the wonderful help and support from the team, I was able to dive into the world of market research and emerge with new skills and greater confidence in my skills as a researcher. Most importantly, I have relished the opportunity to push myself outside of my comfort zone. While it was nerve-wracking at first, I am proud of myself for taking the risk and so grateful to the Ideba team for the opportunity.
Lily Amaturo – Research Intern