In an increasingly data-driven world, higher education institutions need to mature their analytics capabilities to better leverage data to inform strategic planning and decision-making, ultimately improving everyday operations, student experiences and outcomes, and institutional sustainability. At most institutions, data are being collected from many angles, but to what extent are institutions using these data to inform various functional operations? And to what extent are they prioritizing data and analytics as part of their strategic efforts? The 2024 EDUCAUSE Analytics Landscape Study addresses these questions and more. In this report, we summarize the "current state of affairs" of analytics in higher education, taking a look at several key areas of analytics:

• Specific use cases
• Readiness in data- and analytics-related practices and capabilities
• Analytics workforce capabilities
• Strategic efforts toward analytics
• Policies and processes governing institutional analytics practices
• Department/unit and stakeholder roles and engagement
• Barriers and challenges to using analytics
• Current institutional resources supporting the use of analytics

For the purposes of this study, we refer to the following types of analytics and definitions:

Data Analytics Types

• Descriptive analytics: Data are used to summarize and describe what happened or is happening.
• Diagnostic analytics: Data are used to identify and understand why something happened or is happening.
• Predictive analytics: Data are used to make predictions about future trends or events.
• Prescriptive analytics: Data are used to recommend actions that can be taken to achieve desired outcomes.

Functional Analytics Types

• Operational analytics: Data are used to assess/improve administrative and operational processes.
• Financial analytics: Data are used to assess/improve financial resources and strategies.
• Academic analytics: Data are used to assess/improve academic programs and research activities.
• Student success analytics: Data are used to assess/improve student outcomes.
• Learning analytics: Data are used to assess/improve the teaching and learning processes and learning outcomes.

Sentiments Toward Analytics

• A majority of respondents (79%) feel that their institutional leaders are interested in or are fully committed to analytics.
• A majority of respondents (69% or more) feel that the various functional types of data are accurate and useful for analytics as their institution.
• Student success data, academic data, and financial data are deemed to be the most accurate and useful compared to operational data and learning data.

Use Cases and Users

• The most frequent use of analytics is to inform specific operational functions such as admissions/enrollments and compliance with accreditation standards and regulatory requirements.
• The most common analytics use cases (used "a lot") for each functional type are as follows: operational data were most often used to inform admissions and enrollments (53%); financial data were most often used to inform budgeting (38%); academic data were most often used to inform course scheduling (31%); student success data were most often used to identify at-risk students (35%); learning data were most often used to inform student performance (36%).
• Higher education institutions use descriptive analytics more frequently than diagnostic, predictive, or prescriptive analytics.

Resources and Support

• Respondents from smaller institutions have lower levels of access to resources and support for their use of analytics.
• The most common resources institutions offer to support the use of analytics are access to tools/software, technical support, training and professional development opportunities, and access to policies/guidelines for data collection, storage, and ethical use.
• The technologies most commonly available to support analytics at institutions are learning management systems (LMSs) that have analytics capabilities integrated, BI tools, and statistical analysis software.

Workforce

• Institutions are lacking in dedicated analytics leadership and staff positions. Majorities said their institution does not have a chief analytics officer (69%) or a chief data officer (57%). Only 25% or fewer reported that their institution has staff dedicated solely to the different types of analytics.
• Respondents largely indicated that they are understaffed in all analytic areas. Only 32% or less reported having enough/sufficient staff in each functional area.
• Approximately 60% of respondents or less feel that analytics staff have an appropriate level of skills/competency, and 63% of respondents said that increasing analytics staff expertise would help improve the use of analytics at their institution.

Strategic Planning and Governance

• The top three primary motivators for institutions' strategic efforts toward analytics are improving student success and outcomes, increasing retention, and increasing enrollments.
• The most common elements included/addressed in institutions' analytics strategies are data security and privacy standards and policies; standards/policies for data collection, storage, and use; and a data governance structure.
• A majority of respondents (64%) said that at their institution some units are working on strategic efforts, while only 23% said that most units are working on efforts.

Analytics and AI

• Less than a quarter of respondents said that their institution is using AI tools to support analytics, and only few respondents (12% or less) said their institution is in the process of acquiring new AI tools to support analytics.
• Among respondents whose institution is considering/planning to acquire or is already acquiring new AI tools for analytics, a majority (68%) indicated that these tools will be used to supplement (rather than replace) existing analytics tools and practices.
• A little over a third of respondents (35%) said that their institution is engaging in strategic efforts toward the use of AI in analytics, and a plurality (46%) said that these efforts are collaborative (shared between those working on analytics and AI strategy).