Even if you don’t work in the data science field, data analysis skills and tools are still very likely to come in handy. We chatted with a few members of the edX marketing team to find out the ways these skills arise in their day-to-day work, how they were able to pick up on tools in this field despite not having a background in data science, and their best advice for those new to (and perhaps intimidated by) the world of data.
What’s one recent project where data skills came in useful?
Melissa, Product Marketing Manager, Degree Programs: Data is essential in decision-making when it comes to marketing. It informs how effective our past strategies were and how to improve our ability to inform learners about the educational opportunities edX provides. In a recent project which included the launch of a brand new program, we used an array of data points from different systems to help identify the best way to communicate with learners — including which messages to convey to whom, when and where to reach learners, and the ways in which to most effectively convey those messages.
As someone without a data background, how did you come to learn the analysis skills you use in your day-to-day?
Susan, Enterprise Product Marketing Manager: Like many people, I used to look at data primarily as a way to measure results of marketing activities. That was the approach I learned in marketing and business school classes as recently as just a few years ago. We’d consider questions such as: How many impressions or new customers did we gain? What was the revenue impact? In essence, we interpreted and reported on results. Today’s business environment requires more.
Knowing that I wanted and needed to up my data game, I enrolled in courses to build new skill sets (I took courses from the Marketing Analytics program on edX.org). I was able to build on these new data muscles by practicing and applying them on the job – looking at data sets, dashboards and other sources to determine trends, identify growth opportunities and make more informed business decisions. And this skill development is also really helped by the social learning environment here at edX. I’m surrounded by data scientists, BI practitioners, sales engineers and more. What better way to absorb information and practice what I’ve learned…and continue to learn?!?
What’s your best advice to learners who may be intimidated by learning data analysis from scratch?
Greg, SEO Manager: Taking on a side project like developing a website can teach you data analysis from scratch. For example, I built a photography website in college for my roommate’s company and ran Google Ads for them to gain clients. While my major in college was marketing, I had no prior experience in digital analysis or computer science.
Data analysis was advantageous to my roommate’s business because it was their money I was spending, so I had to make intelligent decisions with it each day. Understanding elementary concepts like what day of the week got the most leads, which ad copy variation results in more clicks, and the amount I could spend to obtain consistent leads while being profitable was all immensely valuable.
The second you have something real to work with, data analysis won’t seem intimidating because you are improving your odds at success.
How do you predict your role will continue to evolve in 2020?
Lucy, Email Marketing Coordinator: As 2020 progresses, I predict that the responsibilities of my role will shift to be more data oriented. In email marketing, we’re constantly relying on metrics and data to provide insights into best practices, consumer behavior, and whether our campaign strategies are effective. In the next year, we will be moving to a more robust ESP (email service provider), which will provide us with more opportunities to leverage data and segmentation and predictive analytics.
19 Feb 2020
22 Jan 2020