At a recent class in data analytics, a student asked instructor David Waterman a question that has no doubt occurred to many people pursuing data analytics careers.
“Do you think, in the future, that everybody is going to need to have data analysis skills?”
“I thought about it for a while,” recalls Waterman, a data scientist at U.Group in Washington, D.C., and an instructor at Georgetown University’s School of Continuing Studies (SCS). “And my answer was, ‘I don’t think it’s going to be in the future. I think we’re already there.’”
“Already there?” Does that mean that anyone who makes critical decisions in the course of their work or lives should enroll in courses like the one these students were taking: an intensive, 12-week program for specialists in data analysis? That would be helpful, certainly, but also unrealistic.
But what if a university were to offer a shorter program for people who aren’t data analysts or data scientists, but still need to make informed, unbiased decisions for themselves or the people they supervise. Georgetown piloted such a program in Spring 2023, which is also taught by Waterman: the four-week online Certificate in Data-Driven Decision Making.
“We spent quite a lot of time, as we always do in designing a certificate, looking at hundreds of job postings across all economic sectors, and we saw that the ability to analyze data, to understand data, and to use data correctly in the making of business decisions is just so critically important,” says Jeffrey Warner, Senior Director of Professional Development & Certificates at SCS. “We’re in the third decade of the 21st century. We are surrounded by data. We are all knowledge workers.”
Speaking the Same Language
The course uses a new format Georgetown designed during the pandemic called SDL+ (Self-Directed Learning Plus), which includes individual online study with a virtual class meeting every week. This structure gives busy working professionals both the flexibility to study on their own schedules and the experience of learning in the virtual classroom. Like all SCS programs, the certificate also incorporates the study of ethics and values, which is highly relevant for a field focused on the collection, analysis, and dissemination of data.
“I thought the cohort model was really helpful” because of the variety of professions represented in the class, says Vanessa Berruetta, People Strategy Specialist for Nava PBC, a public benefit corporation that helps the federal government provide essential services. “I’m in HR, and we had someone else in marketing. So, having everyone bring their real-life example [to the class] was really helpful.”
The class starts with an overview of data analysis, “just to make sure everyone’s got the same framework, talking the same language,” Waterman says. “And then, we start talking about the actual decision-making process itself—beyond just going with your gut. What processes do you currently have in place to help you make decisions, and is data a part of that or not? And how can you incorporate data into those practices?”
Avoiding the Pitfalls
Information comes in many forms, so the class covers various ways to frame, visualize, and use data to tell a story. While these tools can be extremely useful, they can also be highly subjective and run the risk of injecting bias into the analysis. This is just one of the many pitfalls to using data effectively.
Another is looking at a given set of data in isolation.
“The biggest mistake that I see is that people think the data they see in front of them is a full picture of the absolute truth of reality,” Waterman says. “And the real way that data analysts think about it is that data can always be sliced and diced in different ways and tell different stories. And so, you really need to keep in mind that just because there are numbers on a page, they do not make up the definitive, authoritative answer to the question. There’s lots of different ways you can answer the same question with the same data.”
But, if used carefully, data can help identify and solve problems. For example, Berruetta conducts a lot of exit interviews for her company, talking to employees who leave to pursue other jobs or activities. Shortly after starting the course, she found she could use data to get a better handle on which employees were leaving and why. She noticed a spike in departures among highly experienced engineers and surmised that there may need to be more growth opportunities for those nearing the top of their profession.
“Instead of just presenting all the data I have, I’ll look for trends so that I’m able to help our teams draw conclusions,” Berruetta says. “That’s what I really liked about the course. I could apply it to the work I was doing.”