Data Governance: The Inexorable Present and Future for Higher Education Providers

Data is one of the key assets of an educational institution. Sitting on a mountain of student data, everyone at the institution is already aware of this fact. However, what most stakeholders are unaware of is how to take advantage of the information at hand, to achieve the diverse goals at an organizational, operational and student level. For instance, the right use of data can help boost student retention rates, as well as sustain or gain high global ranking for the university. Moreover, as students demand customized experiences, and evolving funding frameworks entail new requirements, there is a growing need for precise, reliable data that provides insights regarding enrollment, retention and graduation rates.

As a result, institutions are trying to leverage existing data to enhance time efficiencies, talent and resources such as infrastructure, while gauging the success of academic programs and student services. But the reality for a majority of higher educational establishments is that while there exists a deluge of data, it is unidentified, scattered and underutilized. The solution to addressing these challenges lies in enacting a proper data governance model.

The data governance imperative

A robust data governance framework comprises policies, systems and practices that foster transparency and seamless access to precise, trustworthy and consistent data. Reliable, centralized data promotes a shared vision, with the capacity to drive informed decision making at every level of the institution. Drawing from good governance, organizations can harness high-quality data to even democratize it, and accelerate outcomes such as increased annual student enrollment. But what makes data governance an immediate requirement?

For one, the future of every institution depends on it. By applying analytics on accurate data, higher education providers can now derive varied insights about students, such as identifying those likely to fail or drop out of a course. These insights can give universities and schools an opportunity to address the problem in time. Moreover, institutions can seek funding by presenting evidence-based reports on improved graduation rates, and predictive analytics reports to estimate future results.

However, a lot of the aggregated data is not clearly defined. For example, the term ‘students’ could refer to multiple data points such as full-time students, under graduates, graduates or those continuing education. A well-structured data governance plan will empower education providers to define their data, and eliminate ambiguous terminology. Furthermore, in many institutions, systems and processes don't function in unison, thereby causing uneven data distribution. For instance, one system may capture attendance and student performance, while another unconnected system will have information on demographics.  Subsequently, stakeholders have access to only partial information and cannot make an informed decision at any level. Since data governance is not the responsibility of only the IT department, it transitions the organization from compartmental reporting to cross-functional reporting, with all departments working toward realizing unified objectives.

Three key elements of a well-designed data governance framework:

  • Data governance council - Typically, the team comprises senior staff from different departments who can supervise the strategy and operations across the institution. The council is meant to define, review and validate business rules, data definitions, data quality, and data security. They also ratify data usability and data consistency.
  • Data stewardship committee - The brief for this team is to ensure compliance with the business rules established by the data governance council. They verify if the rules are being applied to the data, and authorize the publishing of data that is fit for stakeholder consumption.
  • Data automation committee - This team has two broad responsibilities. The first requires the committee to enable, automate and ratify the data governance council’s business rules that are applied to processes and data. Second, they are also responsible for ensuring that meta data is collected and saved in a metadata depository tool.

Conclusion

While data governance may either be absent or under-developed in most organizations, setting up a well-defined framework is critical to ensuring a sustainable future for higher education providers. The many complexities involved in establishing such a governance model can be addressed with technology. Whichever approach institutions employ, good data governance will result in good institutional governance. With a clear understanding of data ownership, accessibility, quality and security, stakeholders can be confident about the veracity of data, and secure a future inspired by innovation and informed decisions.

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