Lehigh University's data strategy: A roadmap to a university's analytics transformation

Yenny Anderson

Vice Provost for Institutional Research and Strategic Analytics, Lehigh University

The views and opinions expressed are those of the author and do not necessarily represent the views or opinions of EAB.

Topic and problem

As the Vice Provost for Institutional Research and Strategic Analytics at Lehigh University, I oversee institutional research, data analytics, and data governance initiatives. In 2017, I participated in a collaborative effort across the University to assess and identify data systems and analytics capability gaps. As a result, a pilot project with a set of strategies to promote innovations in analytics was developed.

Since then, many dashboards have been created to offer campus leaders data resources to discern trends, get timely data updates, and connect data points across different functional areas. However, given the vast amount of data produced, many data are still underutilized. These data can be helpful in identifying the institution’s strengths, areas that need improvement, and ways to gain a competitive advantage.

Issues prompting underutilization of data include having multiple systems generating data and no clear inventory of the contents in those systems, lack of awareness of existing systems that may be useful to other units, users lacking proper data access or training, and inconsistent use of terminology for similar data elements. Not having a clearly defined roadmap or data strategy prevents Lehigh from widely embracing analytics to leverage data for competitive advantage and operational efficiency.

Solution

Developing a data strategy is timely because the University is currently engaged in the planning phase of a new strategic plan expected to be ready for implementation by the end of spring 2023. The data strategy will guide the university in identifying which data are most important to track and how they should be collected, shared, and used.

The proposed data strategy consists of four broad phases, outlined below:

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Phase one: Identify at least three big strategic university goals and objectives that are achievable and align these to broad data.

Phase two: Focus on data requirements and data governance.

Phase three: Focus on technology and staffing resources. (We are developing a cloud-based data lake using Amazon Web Services (AWS) Platform and leveraging our Tableau resources.)

Phase four: Execute the strategy, paying close attention to implementation and change management issues that may arise.

Developing a data strategy focusing on strategic university goals will help identify areas in need of improvement to increase operational efficiency and use analytics to develop insights that lead to gaining a competitive advantage in these key areas. As we gain experience, we then continue to expand the data strategy to include other strategic university goals.

Reflections

I am grateful to have participated in the EAB Rising Higher Education Leaders Fellowship and appreciate the wealth of information provided within EAB’s Data Governance Center of Excellence resource center. The program provided a strong networking platform and the opportunity to engage in meaningful conversations with fellows of diverse perspectives.