My undergraduate mentor, Richard Moreland, recently passed away. Dick was not an I-O psychologist—as he put it, he was a social psychologist who liked to dabble in I-O. His work in the area of team processes and newcomer socialization is hugely influential in the social sciences and has been recognized by every major honor and award imaginable. To me, though, Dr. Moreland was an incredibly generous and devoted teacher, and a kind and gentle person. I had no idea how famous he was when I wandered into his office in 2002, looking for research experience. He set me up with a project, coding interviews about recruitment source effects. As he explained, there were two possible explanations for why word-of-mouth recruitment worked better than formal recruitment. It was a puzzle—and I got to help figure out the solution! I threw myself into the task. At the end of the semester, we analyzed the results together (using Minitab, no less) and made a poster. I remember that it was late into the night on a Sunday before he was satisfied that the margins on the poster looked good enough for me to present at our department’s research fair. Because this was before the days when Microsoft would automatically justify your margins for you, we had to use trial-and-error, adding or shortening words until they all lined up just right.

It is a great honor to be representing SIOP both internally and externally. I began volunteering for SIOP in my first month of graduate school, and I have continued to do so since. However, as I learned more and more about how each role and committee was nested into the larger SIOP governance ecosystem, it felt like layers of an onion were being peeled away bit by bit. I would like to take a moment to reveal some of the SIOP onion for all of you reading this column. I think knowing what goes on behind the scenes helps me appreciate SIOP even more. If this isn’t for you, please skip ahead. I promise my feelings won’t be hurt!

The scope of technology’s influence—on how we work, buy, and interact—makes studying its impact an inherently multidisciplinary endeavor. SIOP’s recent Future of Work task force identified the necessity of adopting multidisciplinary perspectives as one of the top forces impacting I-O psychology, alongside AI-driven automation, the changing nature of work, new data science methods, and emerging technology and tools (Fink, Battista, Behrend, Kolmstetter, Kraiger, & Macey, 2018). Despite these recommendations, as a field we often limit our sources, citations, and targets of the work we produce to the journals with which we’re intimately familiar. This can be an acceptable approach for subject areas published primarily in I-O or business journals. However, it is a risky model for fast-advancing technology topics (or traditional topics being disrupted by technology) drawing research and conceptual attention from, and contributing to, fields outside our own—including from the field of information technology itself.

“The Bridge: Connecting Science and Practice” is a TIP column that seeks to help facilitate additional learning and knowledge transfer to encourage sound, evidence-based practice. It can provide academics with an opportunity to discuss the potential and/or realized practical implications of their research as well as learn about cutting edge practice issues or questions that could inform new research programs or studies. For practitioners, it provides opportunities to learn about the latest research findings that could prompt new techniques, solutions, or services that would benefit the external client community. It also provides practitioners with an opportunity to highlight key practice issues, challenges, trends, and so forth that may benefit from additional research. In this issue, we explore how to translate the science and theory behind teamwork into practical training interventions with Kelley Slack and Lacey Schmidt of Minerva Work Solutions, PLLC (aka Minerva).

Big Data is often characterized by “3Vs:” volume (very, very big), velocity (generated fast and furiously), and variety (multiple sources, and types of data, both structured and unstructured). The Big Data phenomenon is the confluence of the data, sophisticated analytic methodology, and readily available computing power. Big Data is making its splash with I-O psychology. Given the coverage provided in the 2018 SIOP annual conference, there could have been a theme track, like “Machine Learning for the Masses.” This is good news because the some of the more esoteric aspects of dealing with Big Data are becoming accessible thanks to the pioneers among us who have summarized and disseminated what they have learned. Part of this education has dealt with analytic methods applicable to “data big and small” (Putka & Landers, 2018), a call to I-O psychology that we now have more tools available, the better to answer questions of interest, and not only for data by the deluge.