Case Studies

Motivation: A professional school at Yale was debating whether to launch a new graduate degree program.

Our work: In our initial engagement, we conducted a market analysis to surface industry trends, labor market demand, and salary benchmarks for graduates with similar credentials. We assessed the potential program’s value proposition in the broader education market and examined how a new offering might position the school competitively. We also developed a structured framework to help the school evaluate the strategic and operational fit of the proposed program. Once the school decided to move forward, we re-engaged with our client to establish a steering committee, define roles and responsibilities, and set a multi-year workplan. We also interviewed peer institutions that had launched comparable programs, using their operational insights to refine the implementation timeline and management approach.

Results: Our work enabled the school to make an evidence-based decision to launch a new degree program, develop a realistic timeline, and build early momentum toward implementation, which will occur in the coming years.

Motivation: A clinical services provider within the university wanted to understand what was causing variable patient wait times across departments and identify potential solutions.

Our work: We sought to propose data-driven solutions that would align departmental staffing levels with patient demand. We began by interviewing clinical and administrative staff to understand workflow challenges and scheduling practices. We then built a predictive model that incorporated projected plan membership, patient visit forecasts, seasonality trends, no-show rates, types of care, and maximum wait times. Our analysis revealed that seasonality had a strong influence on demand, and the extent of that influence varied across departments. Additionally, we identified opportunities to increase the percentage of provider time spent in-clinic by reducing administrative burdens and increasing patient-centric time.

Results: Our model enabled our client to estimate staffing needs by department with greater precision, allowing them to efficiently dedicate resources required to meet patient demand in each department.

Motivation: University leaders sought to address a perceived need for increased student support in large Yale College lecture courses.

Our work: First, we analyzed course rating and enrollment data to identify pain points within current models of teaching large lecture courses. Next, we interviewed faculty across Yale and peer institutions to identify barriers to expanded support and uncover successful teaching practices. We created a set of recommendations focused on implementing different models for supplementing faculty and TAs, expanding the roles of undergraduate learning assistants (ULAs), and piloting new technology, all with the goal of maximizing individual student learning support. We also offered practical next steps, including specific courses where these recommendations could best be first implemented. 

Results: Our client is implementing a pilot program in select large Yale College lecture courses with new TA models to enhance instruction and responsiveness, with new instructional models rolling out in select Yale College large lecture courses as pilots.

Motivation: Yale is a complex organization that uses data in a variety of ways to support its mission. In response to challenges like siloed data, inconsistent data quality, and limited data access, university leaders sought to create a clear and consistent data strategy to support decision-making across Yale.  

Our work: We partnered with stakeholders across Yale to assess the current state of data use and management. This included interviews with data stewards, peer benchmarking, and an internal diagnostic of data governance, infrastructure, skills, and culture. We helped define a set of guiding principles for data access, use, and stewardship, and outlined options for formalizing leadership roles in data governance. We also evaluated the distribution and structure of analytics teams across the university and recommended ways to strengthen collaboration between local units and central resources. 

Results: Our work led to the creation of a new University Data Office and a clear roadmap for advancing data governance and analytics. The university is now positioned to improve data quality, access, and use, supporting better decisions across campus.

Motivation: An operational unit sought greater effectiveness across a set of independently operating teams.

Our work: To assess the current state across departments, we conducted in-depth interviews with leaders from each team to understand their people, processes, and technology. From this, we identified over 30 common tasks performed and designed a survey to capture how more than 100 staff were involved in these tasks throughout the year. The resulting dataset, structured in a clean and easy-to-use analytical product, enabled flexible analysis of task ownership, timing, and tools used across the organization. We also developed a visual map of task distribution, helping leaders quickly see which functions were shared and where roles overlapped.

Results: Using our analysis, leaders were able to identify functions to prioritize for migration to a unified team, staff members to participate in new trainings and working groups, and tools and technology to be standardized across the organization.