The Data Makerspace: A Social Structure for Value Creation in Data and Analytics Programs

In this article, we discuss recent research findings related to social structures and analytics functions within organizations. We leverage the well-established notion of “makerspaces” as a tool for building social structures aligned with supporting an effective data and analytics enterprise.

We also offer a brief pragmatic example of how this idea has been enacted at Clemson University.

In a recent meta-analysis, Oesterreich et al (2022) used an existing sociotechnical framework to synthesize knowledge about how business value is created from large-scale data and analytics programs in the industry.

They analyzed over one hundred studies which included thousands of participants that investigated both social systems (i.e., human factors and organizational structures) and technical systems involved in the execution of analytics programs.

They found clear evidence that the role of social systems outweighs that of technical systems in most cases. While the obvious recommendation for data and analytics managers, and their executive leaders, is to cultivate structures to support these sociotechnical systems, there isn’t a definitive menu or playbook from which to do so.

Oesterreich et al do recommend creating “a nurturing organizational environment in which employees can be upskilled through training” but do not offer specific guidelines to enact that strategy.

Whatever structures are created to support the social system, insights from industrial and organizational psychology research tell us that those structures need to be autonomy-supportive, and can build relationships and meaningfully bolster individuals’ sense of competence associated with those systems (Manganelli, 2018).

Furthermore, there are a host of individual attitudes, values, identities, and behaviors needed to drive data culture, literacy, and ethics.

In this article, to address the role of social structures in the creation of business value from data and analytics programs, we propose adapting the general concept of makerspaces to establish curated, informal, and professional environments via “data makerspaces.”

Loertscher et al (2013) describe a makerspace as a “creative and uniquely adaptable learning environment with tools and materials, which can be physical and/or virtual, where students have an opportunity to explore, design, play, tinker, collaborate, inquire, experiment, solve problems, and invent.”

Makerspaces need not be limited to academic or educational uses. The same principles can readily be deployed in the workplace in support of sociotechnical systems.

Mersand (2021) noted diverse arenas in which the makerspace concept has been applied (including circuitry, technology, crafting, computer programming, and woodworking) and systematically reviewed the literature to examine the individual outcomes of makerspaces using the activity theory framework above to describe the components of a makerspace.

In any organization, opportunities to experiment, fail,, and reconceptualize one's understanding of data can be hard to come by. The scaffolding needed for meaningful engagement may not be present and access to authentic problems and data may not be available. Coordination or awareness across functions may not be strong.

Clemson University, like many large public R1 universities, is a complex organization established to support the next generation of research and discovery while educating thousands of residential students to become leaders across multitudes of professions.

Such a mission necessitates a diverse set of functions including housing and dining; parking and traffic; teaching and learning; athletics; marketing and communications; admissions and recruiting; advising and student services; and fundraising - to name just a few.

As more and more information systems emerge, an increasing number of data professionals are needed to operate and leverage these systems to inform strategic operations and decision-making. In response, Clemson built a data and analytics community of practice to unite individuals with complementary skills, perspectives, and objectives to better understand the institutional landscape through the lens of data.

The community of practice created a space to share knowledge of activities across functions, collaborate when appropriate, and collectively navigate enterprise policies, procedures, and systems. This work was accomplished through monthly meetings, professional development opportunities, and an annual conference.

Against this backdrop, we identified the further need for a data makerspace environment to collaborate and experiment with core data that directly supports the fundamental mission of the University, namely student success.

We were able to bring together individuals (and data!) from enrollment management, undergraduate studies, and institutional research to organize, develop, and test statistical models that represent our collective values and objectives within a tangible, low-risk environment.

The work created from our data makerspace setting isn’t intended to “live forever” or even leave that setting but it informs work being done across the university and serves as a proof-of-concept for ideas and further analysis. Specifically, we are able to readily frame and resolve questions that involve the student life-cycle without having to launch a large-scale enterprise project.

We believe that both the community of practice and data makerspace are supportive of talent development and retention within the data community. These social structures create a space of trust that allow individuals to test their skills and develop in new ways without risk of failure within a structure that supports natural cross-pollination and avoids the frustration of duplication of effort.

While each meeting, data makerspace session, or conference does not, on its own, change institutional data culture, the sum of these activities creates an environment by which the culture can evolve in ways aligned with the values, beliefs, and perspectives of the individuals who make it possible.

These relationships continue outside the data makerspaces and benefit other efforts throughout the University because individuals have learned how to work together and approach challenges. As they enter new collaborative spaces, this past history helps build credibility and trust with new people.

References:

1. Manganelli, L., Thibault-Landry, A., Forest, J., & Carpentier, J. (2018). Self-Determination Theory Can Help You Generate Performance and Well-Being in the Workplace: A Review of the Literature. Advances in Developing Human Resources, 20(2), 227–240. https://doi.org/10.1177/1523422318757210

2. Mersand, S. The State of Makerspace Research: a Review of the Literature. TechTrends 65, 174–186 (2021). https://doi.org/10.1007/s11528-020-00566-5

3. Oesterreich, T. D., Anton, E., Teuteberg, F., & Dwivedi, Y. K. (2022). The role of the social and technical factors in creating business value from big data analytics: A meta-analysis. Journal of Business Research, 153, 128–149. https://doi.org/10.1016/j.jbusres.2022.08.028

About the Authors:

Ben Wiles is the inaugural Chief Student Success and Empowerment Officer at Clemson University, having previously served as inaugural Chief Data Officer for six years. With prior work launching an enterprise analytics function at Purdue University as well as serving as Assistant Head of the Department of Mathematics, his career is centered around higher education with over 20 years of experience teaching in mathematics, statistics, and STEM education.

Wiles’ advocacy for the value of data and algorithms is driven by diverse research experiences in automotive electronics and safety systems, computational number theory, educational psychology, and special education. He has used extensive datasets to execute large-scale institutional curricular reform and enhance educational services under the human, fiscal, and physical resource constraints characteristic of a university setting.

Matt Fields is an accomplished professional with a strong academic foundation, he holds a Master of Science in Counseling and an MBA. Currently serving as the Manager of Student Success Technology at Clemson University since August 2021, he oversees the project management of two university initiatives, Curriculog and CU Navigate. 

Fields is an instructor for Business Foundations at the university as well as a trainer for the SC Foster Parent Association. Before coming to Clemson, he worked as an Operations Director in workforce development for ten years, where he earned his Global Career Development Facilitator certification.

Perryn Freeman is the Director of Information Systems for the Enrollment Management division at Clemson University. In this role, she manages information systems and student data. Perryn has over fifteen years of experience in admissions, enrollment management, and information systems. She received her Bachelor's degree in Communication from the College of Charleston and her Master's degree in Public Administration from Clemson University.