In my first job, someone asked me to put together information on the number of bottles with safety closures sold around the world each year. No, this was not a random case interview question; this was a task to explore if the market size of a potential new industry was large enough to explore further.
My first thought was “how on earth could I figure out this number?” I searched online, but every link that popped up or every number I saw seemed unreliable or untrustworthy. My technical training generally related to quantitative methods, equations and techniques used for solving problems, but this was different. This assignment, and the information needed as an end-product, was ambiguous.
After a few attempts at delivering results for this request and failing to provide something useful, I decided I would forever stay away from ambiguous problems.
Flashforward six years and now I love/thrive when an ambiguous problem is thrown my way. Why? Because these are the problems where you really get to put your creativity to use and have your ideas make the greatest impact.
Anyone can follow a process or an equation, but the ability to solve ambiguous problems adds a crucial skill that differentiates you from everyone else with the same training. The three sections below outline how I was able to shift my mindset and finally enjoy solving ambiguous problems.
Expand Your Definition of Data
In technical fields, when we think data, we think of an excel spreadsheet or a database that can be queried. A traditional dataset for ambiguous problems may not exist, therefore expanding our definition of data can help us solve problems with an unclear path.
In the market sizing story from above, I had no expertise in this area, but there are people in the world who do. I could speak with them to collect their expert opinions on this topic. This process of interviewing experts and collecting your own data is called primary research.
Every time I am asked to solve an ambiguous problem, I use primary research to help me get started. I talk with people and build up my data set. I include the number of people I interviewed, note their field/level of expertise, and indicate how many times similar messages are mentioned. I summarize my findings from my primary research and make an educated decision about my path forward.
Expanding your definition of data to include datapoints collected while talking with experts reduces your reliance on a traditional dataset. When you are given a problem, you do not have to spend weeks or months idly waiting for the client to send their data. You can begin by conducting primary research on day one. Doing so will enable you to understand the actual problem you are trying to solve, and identify any gaps or blind spots.
Focus on the End Goal
In the market sizing example, the goal was not to figure out the exact number of these types of bottles sold. The goal was to understand if, based on the number of bottles sold and many other factors, the new market was large enough to explore further.
Of course, answers to these questions go hand-in-hand, yet focusing on the end goal is so important. Simply identifying the number of bottles is not the complete answer, although it did answer the initial question which was posed. Extenuating factors, such as quantity to be used in the bottles, identified risks, and industry competitors would all need to be considered to actually achieve the end goal of the assignment.
The steps involved for solving ambiguous problems are usually unknown and non-linear. When you focus on the end goal, you have a better understanding of the additional unknowns that must be answered before the task is complete. It also means that if you hit a roadblock trying to solve one unknown, you can remain productive by focusing on the others.
Put Together a Story
The answer to an ambiguous problem will never be just one sentence or one number. The ambiguity of the situation requires an explanation of methodology, assumptions, and rationale to ensure the audience buys into the solution. Therefore, it is especially important to take your audience on a journey that illustrates how you arrived at the identified conclusion.
In this story, you should do three things:
- Establish Creditability – if you are sharing insights from primary research, make sure to include the company name, its market share, and the responsibilities of the expert. Show that you talked to enough people across different companies to generate a conclusion.
- Answer Questions Before They are Asked – if the team explored a path that ended up being a dead end, include that in the presentation. If you thought a path was worth exploring, so will your audience. Taking your audience on the journey of what worked and what did not will prevent them from wandering down other solution paths during the presentation.
- Be Confident – if you put in the maximum effort to understand every unknown and thought outside of the box to identify data sources when none were available, then you will be confident with your answer. Perhaps not as confident as you would feel answering a math problem, but knowing that you expanded your data, identified the end goal, and delivered an insightful story supporting your conclusion, your audience will believe in your solution as much as you do.
Now, who is ready to start solving some ambiguous problems?
Paige Kassalen loves to put her creativity to use by solving problems in emerging technical fields, and has been an IEEE member since 2012. After graduating with a degree in electrical engineering from Virginia Tech in 2015, Kassalen began her career with Covestro LLC. in 2015, and soon became the only American engineer working with Solar Impulse 2, the first solar-powered airplane to circumnavigate the globe. This role landed Kassalen a spot on the 2017 Forbes 30 Under 30 list along with feature articles in Glamour, Fast Company and the Huffington Post.
After Solar Impulse, Kassalen helped Covestro develop its strategy for materials for the future of mobility, and shared her work at conferences around the United States. In 2020, Kassalen received a Master of Information Systems Management degree from Carnegie Mellon University and now applies her problem-solving skills to the finance industry, where she works with teams to develop big data strategies and implement innovative technologies.
