Kevin Wald has worked in the Big Data space for the past five years with a focus on predictive analytics and management consulting. He has exposure to a wide range of industries with a focus on marketing analytics in the telecom and cable industries and operational analytics in the oil and gas industry. Kevin holds a BS in Industrial & Systems Engineering / Applied Math from the University of Washington, as well as a M.Eng in Operations Research and Information Engineering from Cornell University.

Q&A with Kevin Wald

Tell us about your background and how you got into technology.
Coming out of college, I was torn with which direction to take my engineering degree: stay technically focused with an engineering role or go into management consulting where I could work on a vast array of different challenges. I found the ideal balance at a predictive analytics consulting company where I could do some of both. I developed a passion for working with large amounts of data to discover insights and relationships using the tools and technologies associated with data science. I have been in the Big Data space ever since.
What is your role at WWT?
As an Engagement Manager in the Management Consulting Practice, I am responsible for leading and delivering projects conducted by the group. These projects cover big data, cloud and/or business strategy and can have a wide range of different requirements.
What innovation is happening in your technology focus area that has you really excited?
The democratization of data science has me very excited. Before, it took data scientists with PhDs large amounts of time to conduct relatively simple analyses. Now these analyses (and far more sophisticated ones) can be conducted much more quickly, and they are accessible to people with less technical backgrounds. These forces are only accelerating in this direction and will continue to make data science more accessible to everyone. However, as the use of data science spreads, there comes the risk of people not harnessing the tools and techniques correctly, potentially leading to miscalculations and serious problems down the road.
Describe a recent interaction with a customer that led to solving a problem.
A customer in the oil and gas space wanted to use a dataset with information about rock formations (well log curves) in their oil wells to make predictions. The problem was that this data set was very messy, having been acquired from dates ranging from the 1940s through the 2010s. What should have been 10 to 20 variables was actually ~2,500 variables. The customer needed help making sense of the data before they could even try gain any insights or solve any business problems. We were able to develop a data-driven process to consolidate this list of 2,500 variables down to 10 useful ones, which could then be used to drive insights and help the business.