Data analysis is a jigsaw puzzle that does not play by the rules. There are an indefinite number of pieces and no “correct” pattern. However, the goal remains the same: wade through complexity to find something beautiful. Creating this beauty often depends on mathematical formulation to define the problem, analysis tools and statistics to find efficient solutions and business acumen to tell a story.To solve a problem, we must first understand its natural structure. Can it be modeled with algebraic expressions or does it draw from common statistical distributions? Questions such as these have grand implications on the solution methods and limitations. At Emory, my undergraduate math background trained me to think and classify in this way, so framing reality in mathematical models is intuitive to me.After the formulation we move to using analytic tools to efficiently connect the dots and statistical know-how to interpret the solution. I honed these skills in my five years at Navigant where I regularly explored, aggregated and interpret data. At CMU, I am taking classes like Machine Learning, Data Science and Unstructured Data Analytics which all teach and train new analytic tools while building on statistical interpretation skills.A strong analysis is important, but it goes nowhere if it is not properly communicated. Crafting a story became a quintessential part of my time as a consultant. Work often entailed boiling complex concepts into powerful statistics or graphics for lawyer’s discussions. The breadth of my business education at Emory has repeatedly helped me to identify and communicate key takeaways in these contexts.While solving data puzzles may sound like a daunting task, it is fun to me! As a passionate puzzle solver (not to mention a partial owner of an Escape Room), it is a way of life. I excel at structuring the question, identifying efficient tools to solve the problem, and telling a story.