Most executives readily claim their organization has a long way to go in how it uses data to inform the business. Where do traditional universities fit into this transformation? Even before the pandemic, a growing number of bootcamps offered condensed, relatively affordable programs – many deferring payment until employment is secured. Many of these bootcamps are delivered entirely online – an easy bridge to cross for today’s “digital natives,” who presumably with such tech smarts are more comfortable with data than prior generations. But can we really take such data literacy for granted?
To explore these topics and more, I spoke with the following experts who have promoted data literacy in a higher education setting (pictured left to right):
- Dr. Daniel Hall. Daniel is interim dean of the Phillips School of Business at High Point University and has overseen the creation of a data analytics program within High Point’s business school.
- Dr. Markum Reed. Markum is Associate Professor of Data Analytics at Henderson State University and founder of Data Science for Everyone, an organization dedicated to spreading data literacy.
- Dr. Charles Apigian. Charles is executive director of the brand-new Belmont Data Collaborative at Belmont University, which aims to expand data literacy skills among students and the community.
- Dr. Roman Holowinsky. Roman is an Associate Professor of Mathematics at The Ohio State University and Managing Director of the Erdős Institute, a PhD career development and placement organization.
Data literacy is not a generational issue
I get the sense that many assume organizational issues with data literacy will fall aside once the next generation of “digital natives” takes on more responsibilities. This isn’t likely to happen on its own, though: a recent survey by analytics software provider Exasol found that only 43% of 16-to-21 year olds consider themselves data literate. What gives?
Charles made a great distinction between being a consumer of technology and data literate; while younger generations may be more comfortable accessing and manipulating data, this isn’t the same as asking the bigger questions around its use and value.
“Being a digital native helps when you’re working with low-code software that is used to analyze data,” Daniel adds. “But in terms of understanding what that data means and what types of questions to ask, that skill needs to be taught and developed with experience.”
I love this distinction between being a consumer of technology and being data literate because it does away with the idea that data literacy is a generational issue that will age out over the coming decades. While younger generations may be more confident consumers of technology, they need to learn how to think through how to use data just as much as other generations. So how should one get that data education – through a traditional university education or a bootcamp?
Where do bootcamps fit in?
A question I get often is whether someone looking to improve their data skills should head to a bootcamp or take a traditional higher education approach. It’s a distinction that interests me a lot, too – I’ve spent a fair amount of time in both traditional higher education and bootcamp models and see the pros and cons of each.
Can bootcamps and universities coexist? The consensus is yes, and that the route to take depends on the needs of the individual. “If you already possess life skills, you’re already working in business or getting an MBA and all you need is just that hard skill, and you need it fast, bootcamps are the right way to go,” Daniel says.
In other words, bootcamps are great for a concentrated dose of technical skills. But domain knowledge and critical thinking skills take time to acquire, and there’s where higher education comes in. “If you want people to have a well-rounded balance, a university education helps,” Markum reflects. “They become a specialized data scientist with a niche education.”
The role of domain knowledge
This knowledge of a particular academic discipline is worth something to data literacy. Substantive expertise is, after all, one area of Drew Conway’s Data Science Venn Diagram, and equally relevant to any level of data literacy. In fact, helping students see how data relates to their particular area of study is maybe the best way to promote data literacy.
Markum had a great story about teaching data applications to physical education students. “They say they hate math, but then I show them they can do skeletal tracking with football impacts and they’re hooked on data,” he reflects. “They’re probably some of the best students I’ve ever had.” Each participant had stories of non-STEM majors growing in their data literacy by applying data to their field of study. Some data bootcamps offer a bit of specialization, such as on tools or methods, but can’t come close to matching the domain expertise provided by a college education. They also can’t match the community.
Data literacy for everyone
While data literacy is best promoted at the micro level by specializing it to unique interests, it needs organization-wide adoption as well. “Work with the administration for a data culture,” Markum suggests. “If you have that, everyone will become data literate.” When that happens, barriers between traditionally STEM- and non-STEM fields fall. “Courses become more cross-compatible as this language of data literacy is developed,” he goes onto say. For example, as more classes utilize Excel or Python, students gain additional experience which they can then apply to still other fields: a virtuous cycle. Looked at this way, STEM gradually develops into STEAM. Individuals like Google’s Kamau Bobb may agree that these ideas align with the goals of promoting interdisciplinary learning and the evolution of STEM into STEAM, where data literacy is a valuable skill for students in diverse fields of study.
Charles sees it similarly. “It’s not just about a class or a minor in data science,” he says. “It’s about understanding the importance of data in every facet of decision making,” and for that it takes the effort of the whole university and beyond: students, faculty, administration and even the broader community. “You need to learn how to work in a community with others and be problem solvers,” Charles goes onto state about what data literacy opportunities a university can provide. With this big-picture vision in mind, the newly-established Belmont Data Collaborative explicitly seeks academic-community partnerships.
What does the future look like?
Given the need for support across generations, the complementary role of bootcamps, and the benefits of involving the wider university and outside community, what should we expect next in how universities deliver data literacy? Roman at the Erdős Institute shared some great steps forward. As he put it, the next step is to “work with employers via university alumni to simultaneously develop in-demand curriculum and establish long-term recruitment pipelines.” The Erdős Institute seems to be doing this in spades, and Charles’s Belmont Data Collaborative is set for similar objectives.
There’s also the rapid speed of technological change to contend with – universities and bootcamps alike have trouble keeping their curriculum up to date as data tools and techniques change so quickly. “That means looking more towards creating learning programs that result in ‘stackable’ certificates and badges,” Roman reflects. Consistent learning and growth is necessary to stay data literate at any level, and offerings like these from higher education are a step in the right direction.
Conclusion
Like any good student, I suppose, I may have come out of this interview with more questions than I have answers: how will these stackable certificates relate to bootcamps? Is there really no inherent difference in data literacy across generations? Could a bootcamp or other format adopt some of these community-driven initiatives? These are worth future blog posts, and I’d love to hear your thoughts or additional questions in the comments.
One thing I’m sure of is that organizations will need to put more conscious effort into their data literacy initiatives; it’s not a problem that will age out; and, as universities are finding, it requires organization-wide commitment to adopt. I hope this article sheds light on how higher education fits into that path.
I’d like to thank Daniel, Markum, Charles and Roman for sharing their insights with – as someone with a keen interest in higher education, and data literacy, and bootcamps, these topics hold special interest for me, and I was delighted to speak with people making such advances in these initiatives.
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