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This is a guest post by Nii Attoh-Okine, Professor of Civil and Environmental Engineering and Director of Big Data Center at the University of Delaware. Nii, originally from Ghana, does research in Resilience Engineering and Data Science. His new book, Resilience Engineering: Models and Analysis will be out in December 2016 with Cambridge Press. Nii is also working on a second book, Big Data and Differential Privacy: Analysis Strategies for Railway Track Engineering, which will be out Fall 2016 with John Wiley & Sons. 

Big data has been a major revolutionary area of research in the last few years—although one may argue that the name change has created at least part of the hype. Only time will tell how much. In any case, with all the opportunities, hype, and advancement, it is very clear that underrepresented minority students are virtually missing in the big data revolution.

What do I mean? The big data revolution is addressing and tackling issues within the banking, engineering and technology, health and medical sciences, social sciences, humanities, music, and fashion industries, among others. But visit conferences, seminars, and other activities related to big data: underrepresented minority students are missing.

At a recent Strata and Hadoop conference in New York, one of the premier big data events, it was very disappointing and even alarming that underrepresented minority students (participants and presenters) were virtually nonexistent. The logical question that comes to mind is whether the big data community is not reaching out to underrepresented minority students or if underrepresented minority students are not reaching out to the big data community.

To address the importance of addressing and tackling the issues, there are a two critical facts to know, the first on the supply side, the other on the demand side:

1. The demographics of the US population are undergoing a dramatic shift. Minority groups underrepresented in STEM fields will soon make up the majority of school-age children in the states (Frey, 2012). This means that currently underrepresented minorities are a rich pool of STEM talent, if we figure out how to tap into it.
2. “‘Human resource inputs are a critical component to our scientific enterprise. We look to scientists for creative sparks to expand our knowledge base and deepen our understanding of natural and social phenomena. Their contributions provide the basis for technological advances that improve our productivity and the quality of lives. It is not surprising, therefore, that concern about the adequacy of the talent pool, both in number and quality, is a hardy perennial that appears regularly as an important policy issue.’ This statement, borrowed from Pearson and Fechter’s book, Who will Do Science?: Educating the Next Generation, remains a topic of serious debate” (A. James Hicks, Ph.D., NSF/LSAMP Program Director).

The issue at large is how the big data community can involve the underrepresented minority students. On that front I have some suggestions. The big data community can:

• Develop ‘invested mentors’ from the big data community who show a genuine interest in advising underrepresented minority students about big data.
• Forge partnerships with colleges and universities, especially minority-serving institutions.
• Identify professors who have genuine interest in working with underrepresented students in big data related research.
• Invite some students and researchers from underrepresented minorities to big data conferences and workshops.
• Attend and organize information sessions during conferences oriented toward underrepresented minority students.

The major advice to the big data community is this: please do make the effort to engage and include underrepresented minority students because there is so much talent within this group.