Data management strategy in higher education: a blueprint for excellence

The data landscape in higher education is as complex as in any major business in the world. Colleges and universities function as small towns, so to say that post-secondary education delivers services and offerings (from well-being and health to entertainment) to its customers – students – merely scratches the surface. Their operations cover admissions, course enrolment, degree awarding, student housing and athletics, among many others. Less obvious (and often overlooked) business processes include human resources, financials, budget, faculty affairs, research, enterprise resource planning, student services, dining, facilities and information technology. 

Such intricate functions generate monumental amounts of data to be integrated, managed and analysed. Universities therefore need a strategy and best practices in place, created in collaboration with the president, the business and information technology so systems work properly, planning can be effective and data-informed decisions can be made. This can be through, for example, IT or other executive initiatives from the president, institutional research, a chief data officer or executive of administration and finance.

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This guide to data management in higher education outlines essential strategies and emphasises the evolution of data-management practices. The aim is to empower institutions to leverage data for better decision-making and operational efficiency.

Before deciding how to manage data, it is first important to assess overall campus data maturity. Groups such as data management, data governance, strategic consulting or assessment can lead this process. This will assist in understanding the state of business intelligence and provide not only context but also point to areas to address first. For example, executive leadership might be asking about machine learning, predictive analytics or artificial intelligence, but it wouldn’t make sense to start there if there was not, for example, a central documented and governed data warehouse or data lake. While many resources are available that define data maturity, this image from Gartner is widely used. 

Once this initial assessment is completed, the real work can begin. The information below is presented in order, but note too that many activities are done simultaneously and one need not be finished before you move on to the next. In fact, most of these are never completely finished due to changes in technology, systems, leadership and initiatives.

Establish data governance

Data governance involves setting up regulations and policies compliant with standards such as, in the US, the Family Educational Rights and Privacy Act (FERPA) and the Health Insurance Portability and Accountability Act (HIPAA). Pivotal in managing and safeguarding data, data governance clarifies the roles within the data ecosystem, such as data trustees, data owners and data stewards, ensuring that everyone understands their responsibilities. Moreover, it involves mapping data across the enterprise to maintain data integrity and accessibility. Establishing effective data governance requires both executive support and grass-roots engagement to align with institutional goals and regulatory requirements. 

Define teams and strategy for integrations, data warehousing and reporting

Centralised teams dedicated to integrations, data warehousing and reporting are essential for effective data management and analytics. Integrations focus on the seamless movement of data across systems. For example, if enrolment status is needed to be shared from the student information system (SIS) into the student housing system to determine eligibility, an integration would need to be created so the systems can “talk” to each other. 

For data warehousing, methodologies such as Kimball, Inmon or Data Vault, depending on the institution’s needs, ensure that data is stored securely and structured for analytics and reporting. The goal is to build a data warehouse that aligns with governance practices, supports reliable and valid data analytics, and creates a single source of truth. 

Lastly, establishing a business intelligence team can streamline the creation of analytical tools for data-informed decision-making. 

Create a data reference model

A data reference model (DRM) outlines the journey of data from ingestion to its use in downstream systems or tools. This model serves as a blueprint for the architecture of data systems to ensure efficiency and compliance with governance and security policies. Reference models from leading tech companies such as Microsoft, Amazon and Snowflake can serve as examples for creating an effective DRM tailored to an institution’s specific needs.

Gain buy-in from stakeholders

Buy-in from faculty and administration and IT staff is critical for the success of data-management initiatives. This involves communicating the value and impact of proposed data strategies to all stakeholders from executive leadership and groups tasked with delivery. Engaging stakeholders in discussions early and often in the development process, as well as providing clear evidence of the benefits of data-management practices, are key strategies for gaining support.

Continuously improve

Data management requires regular review and adaptation to meet evolving needs and to leverage new technologies. Mechanisms for continuous improvement, such as regular audits, feedback loops and training programmes, ensure that data-management practices remain effective and aligned with the institution’s strategic objectives. Establishing iterative approaches, such as Kaizen, PDCA or Kanban, encourages innovation and adaptability in the face of changing regulations, technologies and educational landscapes. 

Data management within higher education requires thought and strategy. Establishing robust data-management practices not only ensures operational efficiency but also enhances decision-making and enables the enterprise to effectively conduct business. By assessing data maturity, establishing data governance, creating centralised data teams and adopting a dynamic data reference model, institutions can remain agile in an ever-evolving technological landscape. Looking forward, advanced analytics and AI promise to revolutionise data usage in higher education, making the pursuit of data excellence a journey rather than a destination.

Nick Chaviano is director of data services at Georgia Institute of Technology.

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