Home / Blogs / Why Every University Needs a Data Warehouse?

Table of Content
The Automated, No-Code Data Stack

Learn how Astera Data Stack can simplify and streamline your enterprise’s data management.

Why Every University Needs a Data Warehouse?

April 18th, 2024

The role of business intelligence in higher education has amplified in recent times. Modern universities strive to leverage education analytics to efficiently manage resources, drive improvements in operational processes, and enhance the teaching and learning process.

The availability of large volumes of data provides universities an excellent opportunity to derive critical insights for decision-making. While utilizing all this data for decision-making seems appealing, consolidating data from disparate sources and storing it in a centralized repository is a challenging task. This is where a data warehouse comes into play.

Leverage Data with a University Data Warehouse

A university data warehouse is a central repository for gathering and consolidating data from disparate sources across the institution for business intelligence and analytics. It serves as a single source of truth that provide a holistic view of otherwise heterogeneous, raw data to decision-makers.

Data Sources Used by a University

Universities deal with reams of data every day. Most of this data is siloed because it is collected on a departmental or functional level. For example, an admissions department would collect and store data differently compared to the department of student affairs.

  • An admissions office collects current and historical data on the number of applicants, the number of accepted or rejected students, their academic and social backgrounds, etc.
  • A department of student affairs maintains data on students’ activities, their social and academic journey, student societies, and university events.
  • Individual subject departments maintain data on course modules, curriculum, students’ performance, professors and their performance, research endeavors, departmental events, etc.
  • A virtual learning environment and learning management system collect data on student interaction, behavior, and academic performance.
  • Alumni affairs department stores alumni data, including their professional journeys, contributions, and interactions with the university.
  • An accounts department maintains the institution’s financial records, including data on the scholarships extended to the students.

There are several more data centers in a university, including the enrollment office, department for research and grants, HR for employees, libraries, housing office, etc., that also contribute to the influx of data.

Moreover, the siloed data is stored in a variety of different sources such as ERP and financial systems, virtual learning software, cloud databases, JSON files, excel sheets, etc. This further makes it challenging for analysts to draw a 360-degree view of the data for decision-makers.


University Data Warehouse Architecture

University Data Warehouse Architecture

Business intelligence in higher education is crucial for success, but universities must have a unified view of data to derive valuable insights. University data warehouse architecture facilitates this by allowing analysts to gather raw data from different sources, prepare this data for analysis, store it in a storage layer, and consume it in BI and visualization tools for analytics.

Here are the four main layers of a university data warehouse:

  1. Data Source Layer

This layer refers to the internal and external data sources. The most common data sources include ERP systems, learning management systems, and internal databases.

  1. Staging Layer

This layer serves as a temporary storage area for data extracted from multiple data sources for data processing during the extract, transform and load (ETL) process.

  1. Storage Layer

This layer consists of unified storage for storing data in a centralized location — either on-prem or on the cloud. A storage layer can also exist in the form of data marts; these are data warehouse subsets for different departments, such as offices for student affairs or different schools in a university.

  1. Analytics Layer

This layer that can be used to generate actionable insights from a university’s data. BI tools query relevant data from the storage layer and share insights through reports, trends, visualizations, graphs, and charts.

These layers work in unison to provide decision-makers with a 360-degree view of the data. As a result, universities can gather meaningful insights to improve service delivery, student journey, teaching methodologies, funding, and more.

Data-Driven Analytics in Universities

Deploying a robust data warehouse architecture opens endless opportunities for universities to improve their services and operations. Here are some ways how business intelligence in higher education institutions can be leveraged to derive actionable analytics.

Improving Student Journey:

Student Journey

Universities gather massive volumes of student data throughout their academic and social journey. Analyzing this data holistically in relation to other data points on the student can help generate valuable insights about their journey. Let’s look at some of the stages in the student journey that can be influenced through data warehousing.

  • Admissions

Admission is the first step in the student journey. Universities can analyze the application data, for example, the number of application requests vs. the actual submissions. These insights can be helpful in addressing the bottlenecks in the admission process that prevent students from completing their applications.

Universities can also review alumni data to analyze how students’ decisions about school and course selection influence their careers. Subsequently, the right counseling can be offered to students based on their early choices.

  • Orientation

Orientation is where students are exposed to different facets of the actual university life. Participation in orientation events, social activities, and mentoring programs can shape the student’s experience throughout their journey. Universities can use predictive analytics to see how early socialization affects student behavior later.

Such analyses can help institutions introduce the right policies and events to bring out the intended behavior in students. Disparate data on participation in orientation events and post-orientation student behavior can be queried together and analyzed through a data warehouse as well.

  • Learning

A university can analyze how different teaching methodologies can affect learning outcomes across different courses. It can also see how internal factors in a class—e.g., group size, learning mode, assessment type, etc.—have an influence on student learning.

Instructors can also review insights on a more granular level to adapt their teaching methodologies for individual students. By reviewing students’ performance during a previous semester, they can adapt the content and pace of the course to meet the needs of the students. Teaching staff can also provide more personalized learning to students with learning difficulties.

  • Alumni

Alumni data can be leveraged to draw insights into post-study outcomes. With a unified data warehouse, alumni data can be used in unison with data on student performance and behavior to see what factors impact post-study outcomes the most. Subsequently, universities can improve post-study outcomes by addressing performance or behavior-related issues.


Identifying Donation Patterns:

Dashboard for Donation patterns of a Higher Education Institution

Many universities rely on donations from philanthropists and independent organizations to remain financially healthy and continue expanding their programs. By using the data stored in a data warehouse, universities can identify top contributors and the programs that attract the highest funding. These insights would allow them to prioritize donors and beneficiaries in fund-raising campaigns.

For example, alumni often donate significantly to their alma matters. A university can monitor the alumnus’ donation trends over time, their potential donation capacity, and geographical locations to identify funding opportunities. This analysis can be correlated with the post-study status of alumni to further gauge the potential amount of funding.

Improving Grant Management:

Grants bring the capital needed for funding a university’s research projects. A centralized data warehouse allows universities to identify professors with the highest grant acceptance rate. Subsequently, they should be put on the task of preparing and reviewing all grant proposals.

Moreover, the university can identify and prioritize leading contributors, including industries, organizations, and public entities. Lastly, programs and departments at the institution that attract more grants can be given priority in the grant-writing process.

Space-Use Analysis

Universities can carry out a space-use analysis to optimize space usage and cut down on associated expenses. They can analyze average footfall data to identify university premises, including departments, labs, classrooms, cafeterias, etc., that require additional space as well as those that are underutilized.

Space usage can also be examined against enrollment projections to forecast the space requirements to accommodate new students. For example, the University of Western Carolina used enrollment trends and instructional space analysis to propose strategic recommendations for their space plan.

These were just some of the many ways how a centralized data repository can help universities leverage business intelligence in higher education. An educational data warehouse lies at the center of all data-driven decision-making that can help optimize operations and service delivery.

Setting up a University Data Warehouse with Astera DW Builder

Astera DW Builder is a data warehouse automation tool that helps organizations build an agile, end-to-end data warehouse architecture within weeks. It streamlines and simplifies complex data warehouse development tasks through continuous automation and no-code capabilities. Astera DW Builder can set up a university data warehouse in just four steps:

  • Automatically create and configure metadata-rich data models based on university data sources and analytics use-case.
  • Deploy data models on-premise or on the cloud and create a functional data warehouse that consolidates all your data sources.
  • Populate the data warehouse with self-driven ETL data pipelines that provide standardized data for analytics.
  • Connect the data warehouse with reporting and analytics tools through the OData module.

Now that you know why a data warehouse is necessary for driving business intelligence in higher education, it’s high time you saw a personalized demo of the product to see Astera DW Builder in action. Click here to schedule a demo now or sign up for a free trial!


Exploring the Connection Between Data Governance and Data Quality
Astera’s Guide to Insurance Data Quality and Governance
Information Governance vs. Data Governance: A Comparative Analysis
Considering Astera For Your Data Management Needs?

Establish code-free connectivity with your enterprise applications, databases, and cloud applications to integrate all your data.

Let’s Connect Now!