Why Data Governance Matters in Higher Education

Data is at the core of decision-making within higher education institutions. Whether it’s monitoring student success, improving resource allocation, or ensuring compliance, universities rely on accurate and well-managed data to function effectively. However, without strategic systems in place to govern that data, both human and technology systems, institutions face significant challenges. 

This blog post, the first in a series on data governance in higher education, highlights the tangible problems that arise from poor data management in higher education, such as data silos, inconsistent reporting, and fragmented insights into student progress. It also explores how implementing robust data governance policies and systems can mitigate these issues and strategically position universities for success. 

The Challenges of Poor Data Management in Higher Education

Data Silos Across Departments and Systems

One of the most pervasive challenges in higher education is the presence of data silos. Universities often collect data across multiple departments—admissions, financial aid, academic advising, and student services—but these systems rarely communicate effectively. 

Imagine a student whose academic progress is tracked in one system, financial aid in another, and engagement in extracurricular activities in yet another. Advisors, tasked with providing holistic support, struggle to piece together a comprehensive picture. This fragmented view can lead to missed opportunities for intervention, such as failing to identify a student excelling academically but facing financial hardship.

Data silos slow down service delivery, hinder communication among stakeholders, and reduce the ability to create a cohesive and informed strategy for academic or administrative initiatives.

Inconsistent Reporting Across Departments

Another common pain point is the lack of standardized reporting practices across university departments. Without a single source of truth, reporting is often riddled with inconsistencies, making it difficult to draw meaningful conclusions that are trusted by all. 

Departments generate reports using disparate metrics and definitions, resulting in conflicting interpretations and delayed decision-making. Institutional leaders face the daunting task of reconciling these inconsistencies, hindering their ability to make informed, timely decisions. Consider the chaos of allocating resources based on enrollment or student success figures that vary significantly depending on the departmental report consulted.

Difficulty Tracking Student Progress Holistically 

A significant challenge directly impacting student success is the inability to monitor individual students’ progress holistically. Instead of leveraging centralized data, departments often operate on isolated platforms, failing to integrate key metrics like academic performance, retention rates, extracurricular activities, or even mental health counseling engagement.

This fragmented approach to tracking progress limits institutions' ability to intervene early when students are at risk of falling behind. Consequently, schools end up reacting to problems rather than proactively preventing them. 

The Need for a Single View of the Student

At its core, the inability to obtain a single, unified view of a student remains one of the biggest hurdles for institutions striving to improve the student experience. Admissions offices, financial aid departments, academic advisors, and counseling services all maintain critical pieces of data. Still, without proper integration, they fail to present a comprehensive profile of a student’s needs, challenges, and accomplishments. 

Such disjointedness often leaves students repeating their information or requirements multiple times, creating frustration and confusion. It also prevents institutions from personalizing services based on reliable insights.

How Data Governance Offers Solutions 

Data governance provides a structured solution to these challenges through standardization, integration, and centralized management. Implementing data governance practices ensures consistency, builds efficiency, and amplifies the usability of data across an institution. As a result, the effective use of data governance builds confidence with users throughout the organization to make decisions quickly and with confidence.  Here’s how it works in practice:

1 - Standardization and Policies

A strong data governance framework begins with clear policies and standardization. This involves defining data ownership, establishing data quality benchmarks, and implementing consistent practices for data collection and reporting. 
For higher education institutions, this translates into:

• Defining clear guidelines for how departments record enrollment activities or retain student records. 

• Establishing clear definitions of key metrics such as retention, full vs. part time students, student engagement, and cost of instruction. 

• Setting uniform templates for reporting academic performance metrics across all schools within a university.

• Promoting adherence to data-privacy laws to ensure compliance with regulations like FERPA, GLBA, and GDPR. 

By establishing agreed-upon norms, institutions can reduce inconsistencies and ensure that data quality meets acceptable standards.

2 - Centralized Technology and Data Integration 

Investing in advanced data management and integration tools is critical for modern higher education institutions, as these technologies directly underpin and enable core Data Governance principles. Centralized technology that seamlessly integrates key source systems, like enterprise resource planning (ERP), student information systems (SIS), and Learning Management Systems (LMS), moves universities beyond fragmented, ad hoc data integrations. 

By unifying information from diverse systems handling financial aid, academic financials, registrations, and extracurricular activities, universities can construct a cohesive and reliable data ecosystem. This consolidation directly supports the Data Governance principles of data quality and accessibility. For instance, data integration tools can enforce data validation rules during the transfer of information between systems, ensuring accuracy. By creating a centralized data repository or data warehouse, institutions make data readily available to authorized users across departments, eliminating data silos and promoting collaborative decision-making. This aligns with governance goals of data sharing and usability.

Additionally, cloud-based solutions significantly enhance scalability and provide advanced analytics capabilities, empowering stakeholders to make faster, data-driven decisions with confidence. Cloud infrastructure allows for flexible data storage and processing, adapting to the institution's evolving data needs. Advanced analytics tools, integrated with the cloud, facilitate the generation of insightful reports and visualizations, supporting governance objectives related to data transparency and informed decision-making. These technologies enable the implementation of data lineage tracking, allowing institutions to trace data back to its source, which is critical for auditability and compliance. In essence, these technological investments are not merely about acquiring tools; they are about establishing a technological foundation that actively supports and reinforces the principles of Data Governance, ensuring data is managed as a strategic asset.

3 - Promoting Data Literacy and Culture 

The successful implementation of data governance extends beyond technological infrastructure and policy frameworks. Even the most robust systems will falter without a strong foundation of data literacy and a culture that values data-driven decision-making. Building this culture necessitates empowering staff across all departments to effectively use, interpret, and act upon data. It's about transforming data from a technical abstraction into a practical tool for everyday operations and strategic planning. Here are some strategies for higher ed that have been helping for fostering a data-aware environment:

• Offer Targeted Data Literacy Training: Recognizing that different teams possess varying levels of data familiarity, institutions should provide tailored data-training workshops. These workshops should cover fundamental data concepts, tools for data analysis, and best practices for data interpretation. By addressing the specific needs of each department, universities can ensure that all employees are equipped to leverage data effectively in their roles. "We devised an enterprise data management plan for the university that had four key areas,” says Becky Frieden, Senior Director of Enterprise Applications and Decision Support Systems at New York Tech. The first pillar of that plan was a commitment to a rigorous data governance program, including a devoted charter and a data fluency class for users to help improve trust in their data sources.

• Establish Departmental Data Champions: To bridge potential knowledge gaps and provide ongoing support, designate "data champions" within each department. These individuals serve as local experts, assisting their peers with data-related inquiries and promoting adherence to data governance policies. They act as liaisons between departments and the central data governance team, ensuring that data concerns are addressed promptly and effectively.

• Facilitate Cross-Departmental Collaboration: Encourage collaboration by hosting regular cross-departmental meetings to align data strategies and address shared challenges. These meetings provide a platform for employees to exchange knowledge, share best practices, and develop a collective understanding of data as a shared resource rather than a departmental silo. This fosters a culture of transparency and collaboration, breaking down data barriers and promoting a unified approach to data management.

• Develop Accessible Data Documentation and Resources: Creating clear and accessible documentation for data definitions, data sources, and data usage guidelines is crucial. This will enable all staff to easily access the necessary information to support their data related activities. In addition, creating readily available data resources, such as data glossaries, tutorials, and best practice guides will help to empower staff to use data with confidence. Ohio University went through this exercise as part of their HelioCampus implementation, which helped them create new data models, incorporate user feedback, and build out documentation and training libraries to encourage adoption. 

Practical Strategies to Address Data Challenges 

University administrators looking to start tackling these pain points in data governance should:

1. Start Small, but Strategically – Focus on key areas with high-impact potential, like attendance tracking or performance evaluation. Use these wins to gather support for larger-scale projects. 

    

2. Engage Leadership – Secure buy-in from institutional leadership by tying governance policy proposals directly to student success metrics and return on investment (ROI). 

    

3. Leverage Vendor Expertise – Work with technology providers who understand the unique challenges of higher education, ensuring the tools you adopt fit your needs. 

Transforming Higher Education Through Data Governance

Data governance holds the power to transform universities, turning chaotic systems into streamlined, intelligent frameworks that inform and empower decision-making. From breaking down silos to creating a holistic view of every student, robust governance uplifts stakeholders to deliver on their mission. 

If your university is ready to address its data management challenges and unlock its full potential, start by examining your current workflows, systems, and attitudes toward data. By investing in long-term governance strategies, institutions will move closer to achieving their goals—and improving their students' experiences along the way.

FAQs: