Advancing Analytics — 10 Crucial Reasons for Industry-Academia-Community Collaboration

In my role as Executive Director of the University of Cincinnati Center for Business Analytics, I speak with many data and technology business leaders throughout a variety of industries. One question that I often ask is, “What are your biggest challenges?” It’s a fairly straightforward question that elicits a straightforward answer. The leaders will talk about data sourcing, privacy, lineage, quality, and integration. They will also often add aspects of organizational change and alignment plus quantifying business impact to support their initiatives.

An area that often goes unrecognized by the rest of the organization is talent acquisition, refreshing staff skills, and keeping pace with emerging technologies. Without refreshing the organization through these actions, it can become stale and irrelevant. It’s an area where CDOs and CAOs often need assistance from their external community. There is a case to be made that collaboration among universities, industry associations and networks, and individual businesses can play a key role in addressing these factors.

The diagram below illustrates how this collaboration can work.

Businesses and industry can benefit from a multi-point relationship in the following ways:

1. Talent acquisition and development: Universities produce a pool of talented graduates with expertise in analytics and technology. The industry can tap into this talent pool via collaborations for recruitment purposes to refresh their ranks. Moreover, it can also participate in shaping the curriculum, offering internships/co-op positions, and providing real-world projects, thereby contributing to the development of a workforce that is better aligned with industry needs.

2. Fostering innovation and knowledge transfer: Collaboration between universities and the industry can also facilitate the transfer of knowledge and technology between the two sectors. Industry professionals can benefit from exposure to academic research and theoretical frameworks. This exchange of ideas often leads to the development of innovative solutions and new applications of data analytics technology.

3. Access to cutting-edge research: Universities are hubs of research and innovation. They conduct studies, experiment, and develop new methodologies in various fields, including data analytics. Industry sectors can get access to the latest advancements and research findings, which can help them stay competitive and innovative in their respective markets.

4. Interdisciplinary perspectives: Data analytics is not limited to a single discipline but draws from various fields such as statistics, computer science, mathematics, ethics, and domain-specific knowledge. Universities typically have departments and faculties spanning these disciplines, fostering interdisciplinary collaboration and holistic approaches to problem-solving. Industry-academia collaboration can leverage this interdisciplinary expertise to tackle complex data analytics challenges effectively.

5. Being future-ready for the upcoming generation: The upcoming generation of the workforce, often referred to as Generation Z (born roughly between 1997 and 2012), is adept with technology and digital tools. They tend to value data-driven decision-making and are comfortable with analytics. Universities are interfacing with this group daily and understand their orientation. As the industry strives to constantly be future-ready, they need to take into consideration what the workforce of the future prefers and what they need. Universities can provide a window into those requirements.

There is also a flip side to this relationship. Universities can benefit from this relationship in the following ways:

1. Practical applications: The industry provides real-world contexts for the application of business analytics techniques. Universities can learn which business challenges are top-of-mind as well as how various analytical methods and tools are applied to solve practical business problems from industry players. Understanding these applications can help universities tailor their curriculum to focus on relevant skills and knowledge that are directly applicable in industry settings.

2. Handling institutional-sized data: Industry partners can also bring a solid understanding of implementing current best data practices. Handling institutional-sized data can be a complex and challenging task, but several key principles and technologies can help manage and analyze large datasets effectively. Learning from industry partners about how to “do” institutional data can be just as important as technology knowledge transfer.

3. Data governance and management: Industry players often deal with large volumes of diverse data sources, requiring robust data governance and management practices. Universities can learn from industry best practices in data governance, data quality assurance, data integration, and data security. Incorporating these practices into their curriculum ensures that students are equipped with the skills necessary to handle data effectively and responsibly in their future careers.

4. Emerging technologies: Industry partners are at the forefront of adopting emerging technologies in business analytics, such as machine learning, artificial intelligence, and big data platforms. Universities can learn about the latest adoption of these technologies from industry and incorporate them into their curriculum to ensure that students are familiar with cutting-edge tools and techniques.

5. Business acumen: Industry professionals typically have a deep understanding of business processes, industry trends, and competitive dynamics. Universities can learn from industry players how to integrate business acumen into their business analytics curriculum, teaching students not only analytical skills but also how to apply those skills to drive business value, how to communicate the results of analytical methods to decision-makers, and how to make data-driven decisions.

By learning from industry, universities can ensure that their business analytics programs are relevant, practical, and aligned with the needs of the industry, thereby better preparing students for successful careers in business analytics.

A group that can be a critical catalyst to these relationships are the industry associations and professional networks. Every major community in America has them, whether they be highly structured with defined leadership roles and organized events or unstructured network meetups. These groups can address all four boxes in the value proposition and are often the glue that brings these experts together.

The key benefit of these communities is knowledge sharing and exchange of ideas and experiences. Data and technology community groups, universities, and industry each possess unique expertise, perspectives, and resources. Collaborating allows for the exchange of knowledge and best practices among these different stakeholders.

Community groups can share insights from grassroots initiatives and diverse perspectives, universities can contribute cutting-edge research and academic expertise, and industry can provide practical insights and real-world challenges. This collective sharing of knowledge fosters innovation and accelerates learning across the ecosystem.

In summary, collaboration among data and technology community groups, universities, and industry fosters economic growth and enhances the competitiveness of regions and individual businesses. By investing in research, innovation, and talent development, communities can attract investment, create jobs, and stimulate economic activity leading to the advancement of knowledge, innovation, talent development, and societal impact.

By leveraging technology for social good initiatives, such as education, healthcare, sustainability, and civic engagement, stakeholders can address pressing societal challenges and improve quality of life. Universities can engage in community-based research and service-learning initiatives, partnering with local organizations and stakeholders to address community needs. The industry can support social impact initiatives through corporate social responsibility programs, philanthropy, and employee efforts.

By leveraging each other's strengths and resources, all sectors can achieve greater success in harnessing the power of data analytics to address complex challenges and drive positive change.

About the Author:

Tim Cholvat is the Executive Director of the University of Cincinnati Center for Business Analytics. The Center for Business Analytics is part of the Lindner School of Business.

Before joining the University of Cincinnati, Tim spent 30+ years in the private sector bringing solutions to both internal and external clients. He has spent time with Procter & Gamble, CSC (now DXC), HPE, and SAS Institute in the fields of Information Technology and Analytics.

Cholvat has a bachelor’s degree from the University of Toronto with majors in Computer Science and Economics and Quantitative Methods. He also has an MBA from Xavier University in Cincinnati.