Like every other industry, higher education institutions are ingesting a significant volume of data in the hopes of leveraging that information to optimize analytical insights and reduce time to market. By analyzing data, institutions can better understand student life cycle behavior to improve the student experience, impact student enrollment and admissions, and identify other trends that might be affecting student outcomes or even institutional revenue.
However, before higher education institutions can extract actionable intelligence from their data, they must implement the right data management processes and technologies.
It is critical to properly manage data if it’s going to be used for strategic financial aid decisions, optimizing educational efficiency, increasing student enrollment and enhancing student performance. Mismanaging data leads to data mistrust, and data utilization at higher education institutions drops drastically. All of that valuable data that represents potential competitive advantage can quickly become a lost opportunity.
Properly managing data is difficult. As more data is ingested into a higher education institution, it becomes increasingly complex to manage. In addition, data is always in motion, complicating matters even more because typically access to data goes unmanaged while it is in transit. As data flows through the data supply chain, it’s subject to a variety of uses and transformations. As it continues to travel across numerous systems, processes, and environments, its quality is constantly at risk.
To realize optimal returns on data assets, it is important to ensure institutions have a firm understanding of their data, and assign data assets quality scores to inform data consumers and quantify data value. By incorporating a comprehensive data governance program into a higher education institution’s data management strategy, educators can ensure the ongoing accuracy and integrity of their data assets. In addition, education leaders can ensure that users have both the requisite data knowledge and easy access to utilize data assets for meaningful analysis.
Data governance provides a deeper understanding, traceability, and usage of available data assets. A data governance program enables data access, understanding and accountability across higher education institutions.
One key component to data governance is establishing data understanding to satisfy the needs of users within educational organizations. When data consumers understand what data is available and exactly what it means, they can choose the right resources for analysis. Data governance also provides data quality transparency across the entire data supply chain.
By integrating data integrity capabilities into a comprehensive data governance strategy, higher education institutions can increase visibility and automation while also improving the usability and reliability of data assets. Integrated capabilities allow for continuous data quality monitoring, and also provide a framework for establishing data quality scores. These scores are crucial to data monetization, and also give users valuable information regarding the reliability of the available data assets. Assets that have been applied and scored as accurate and reliable encourage utilization among other data consumers, making collaboration and transparency two other critical features of a successful data governance effort.
By aligning people, processes and technology through data governance, institutions ensure data remains trustworthy, understandable, and accessible, so business users can make accurate and reliable business decisions. However, higher education institutions require the right tools and technologies to foster data governance success.
Selecting Data Governance Technologies
Higher education data governance requires a data intelligence platform that delivers diverse capabilities to perform a variety of functions. Ideally, the platform will offer integrated solutions that include data governance capabilities to deliver an all-inclusive view of the school’s data landscape, from the data available, its owner, lineage and usage, to its associated definitions, synonyms and business attributes. It should allow users to easily and quickly define, track, and manage all aspects of their resources, enabling collaboration and knowledge sharing across the entire enterprise.
In addition, the solution should incorporate data quality capabilities for administering data integrity checks such as data profiling, consistency, conformity, completeness, timeliness, reconciliation and transaction tracking. These rules validate the quality of data across the entire institution and all the departments to assure enduring trust among users.
As institutions determine that they need to predict outcomes and take appropriate action, the platform should incorporate this governed, high-quality data to feed the analytical models that generate these insights. By ensuring the integrity of the data feeding your models, you can be confident in their recommendations.
By establishing data governance and quality as foundational elements of a higher education institution’s data management strategy, integrating them with other key capabilities such as analytics and enabling the right technologies, organizations can obtain and execute on the right insights to track student success, automate student outreach, manage sensitive financial aid and scholarship information and ultimately gain a competitive edge.
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