The Impact of Analytics on the Higher Education Workforce

The 2019 Enterprise Summit: Analytics brought together professionals from the areas of IT, business and finance, and institutional research to explore the promise of analytics and how collaboration across the enterprise can advance institutional analytics strategy. The Enterprise Summit was co-hosted by EDUCAUSE, the Association for Institutional Research (AIR), and the National Association of College and University Business Officers (NACUBO) with support from a program committee comprising members from each association.

Three primary themes emerged from this year's event:

• The importance of data and the urgency of analytics
• The impact of analytics on the higher education workforce
• Making a difference through communication and governance

This is the second in a three-part series of blog posts summarizing each theme's issues and recommendations.

Analytics Is Changing the Higher Ed Workforce

As colleges and universities increasingly rely on data and analytics for everything from facilities management to developing and monitoring institutional strategic goals, the higher education workforce has had to evolve to keep up. Faculty and staff need new skills and competencies in order to navigate and work within a data-oriented culture, and new job functions are emerging to meet changing institutional needs for data management, data analysis, and communication.

The ability to foster a data-oriented culture is an emerging competency required of institutional leaders. Leaders from across all areas within the college and university should ask strategic questions and request supporting data to back up answers, model reliance on data for decision-making, and support staff and faculty in data literacy efforts. In their presentation at the Summit, presidents from EDUCAUSE, AIR, and NACUBO shared a draft joint statement about the importance of analytics titled, "Analytics Can Save Higher Education. Really." This statement specifically calls out the importance of institutional leadership commitment to analytics.

Most effective data and analytics success stories include a foundational commitment with strong buy-in from the top. Recognizing that presidents and chancellors are critical to this comprehensive approach to data and analytics, we encourage all institutional leaders to provide the critical leadership that expands 'pockets of excellence' into an institutional culture that embraces innovation, change, and continuous evaluation for improvement.

In addition to describing the critical nature of leadership commitment, the statement asserts that analytics is a "team sport" that requires participation across the institution, implying that broad data literacy should be an important institutional goal. As student success initiatives and other analytics projects are established and start to mature at colleges and universities, involvement in those projects naturally expands to include more stakeholders and more functional units. Staff and faculty from across a wide swath of the institution have roles to play in the development of successful analytics. Therefore, data literacy should be considered a key competency across many institutional departments and functions.

EDUCAUSE research has identified three workforce-related attributes that contribute to a successful analytics project: the institution provides effective training, the staff are skilled in analytics, and the leadership is committed to evidence-based decision-making. Conversely, inadequate staffing in data and analytics, and leadership that is not committed to analytics can hamper progress on these initiatives.¹

A research study undertaken by EDUCAUSE in collaboration with the National Association of Student Personnel Administrators (NASPA) and AIR found that inadequate staffing may be limiting student success efforts across higher education as well. Only one-third of survey respondents reported that they have adequate staff for data-related functions. Between a fifth and a third of respondents have nothing in place with regard to analytics staffing for student success initiatives, jeopardizing efforts to improve student success.²

It seems clear that the pursuit of an analytics-capable institution involves changes to the workforce that enable and increase analytics-related skills, competencies, and roles. Many presentations and discussions at the Enterprise Summit focused on the skills required to meet changing needs for data-related functions, the emerging roles related to analytics, and recommendations for meeting these workforce challenges.

New Skills and New Roles

As colleges and universities begin to treat data as an institutional asset, they also begin to develop strategies for managing that asset appropriately. This process often leads to a better understanding of the need for particular skill sets related to data and analytics. Summit presenters and attendees maintained that higher education is seeing an increase in the need for data-related skills in two categories: technical skills and soft skills. Technical skills are needed for managing, manipulating, and analyzing the data, and soft skills are necessary for analyzing institutional problems, communicating about outcomes, and managing the collaborative relationships required for a mature analytics culture. Individuals who possess both sets of skills are in high demand, not only in higher education but across many other industries as well. The demand outside of higher education for employees with technical and soft skills is complicating the hiring process for institutions that may not be able to pay as well as employers in those other industries.

These skills are becoming important for institutional leaders in IT, IR, and business and finance as well. These leaders are well-positioned to drive analytics initiatives forward by advocating for and supporting a data-oriented culture.

Completely new roles are also emerging that are important for success in analytics endeavors. As with many other data-related positions, these new roles require a very diverse set of competencies ranging from highly technical skills, such as data analysis, to highly interpersonal skills, such as communication and presentation skills.

• The data scientist role serves as a system analyst, data manager, and a communication liaison, strengthening capabilities in everything from systems administration to data visualization.³
• The chief data officer role encompasses leadership in the use and interpretation of data as well as data management and governance.⁴

Both of these roles are important for colleges and universities to consider as they advance their analytics initiatives. Someone at the institution should be responsible for these functions and activities, even if it is not possible to hire or appoint a person who is dedicated to performing these tasks.

Recommendations

Recommendations related to workforce challenges emerged in three areas: hiring strategies, existing staff, and data literacy basics.

Hiring Strategies

• Bring human resources into the analytics conversation to help rethink hiring strategies, update job descriptions, and focus on new skills that are needed.
• Look for ways to engage new hires with higher education values and the institutional mission. Effectively communicating mission and values can also help to attract potential employees to a higher education career.
• Consider hiring recent graduates who show promise in the skills needed and provide training to increase their capabilities.

Existing Staff

• Expand existing institutional roles to include the responsibilities of the data scientist and the chief data officer.
• Look for existing staff who are curious, who want to learn, and who have some background in data and analytics. Fill in their gaps with training.
• Create a community for data users to share their ideas with each other and advocate for the data and tools that will help them do their jobs.
• Use the data governance process to designate individuals to take on particular data-related roles.
• Leverage software implementations to catalyze change in staff roles.

Data Literacy Basics

• Develop a fundamental understanding of the importance of data use in staff and faculty across the institution.
• Use data governance to ensure appropriate data access and transparency.
• Define data standards and clarify definitions, and be sure staff and faculty use and understand them.
• Educate data users about issues related to data ethics and privacy.

Workforce as a Key to Meeting Institutional Goals

Analytics can help to measure progress on institutional goals, predict the results of changes in strategic direction, and provide information about daily activities, among other things that are important to institutional success and longevity. But simply having access to the data isn't enough. A data-oriented and data literate workforce is required to manage the data, understand the business need, ask the right questions, analyze the results, and communicate the analysis. In his session at the Summit, D. Christopher Brooks described the evolution of analytics and its connection to the workforce. Fifteen years ago, according to Brooks, higher education was just starting to understand the potential of leveraging our data infrastructure to help with decision-making. With that understanding, colleges and universities began to focus on analytics as a way to address important institutional strategies, leading to culture change that has resulted in more collaborative relationships as they work toward common goals such as student success. Now colleges and universities are recognizing the ways in which the workforce might need to be reimagined and restructured in order to support institutional goals going forward.⁵

Colleges and universities are sitting on vast stores of data that have the potential to provide information about far-ranging aspects of the institution, from improving daily efficiencies to understanding strategic change. However, to take full advantage of that potential, institutions need to identify the necessary workforce skills and competencies, ensure that emerging roles are being addressed, and take steps to develop a data-literate workforce.

Notes

1. Phil Goldstein and Richard Katz, Academic Analytics: The Uses of Management Information and Technology in Higher Education, research report (Louisville, CO: ECAR, December 2005). ↩
2. Amelia Parnell, Darlena Jones, Alexis Wesaw, and D. Christopher Brooks. Institutions' Use of Data and Analytics for Student Success, research report (Louisville, CO: ECAR, April 2018). ↩
3. For further discussion of the role of the data scientist, see Cecilia Earls, "Big Data Science: Establishing Data-Driven Institutions Through Advanced Analytics," EDUCAUSE Review (website), May 6, 2019. ↩
4. EDUCAUSE examined the CDO role as part of an IT workforce study. See Jeff Pomerantz, IT Leadership in Higher Education, 2016: The Chief Data Officer, research report (Louisville, CO: ECAR, August 2017). ↩
5. D. Christopher Brooks, "Student Success Is Everyone's Responsibility," (presentation, 2019 Enterprise Summit, Long Beach, CA, April 2019). ↩

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Betsy Tippens Reinitz is the Director of the Enterprise IT Program at EDUCAUSE.