Andrew Drinkwater

Strategic Enrolment Management (SEM) is a planning practice centred on expressing an institution’s overarching strategic priorities in terms of the optimal number and mix of students enrolled and seeks to align enrolment with the values of the organization, such as supporting successful completions or transfers. Building transparency in SEM and enrolment forecasting is key to ensuring alignment and collaboration between your stakeholders, and data governance helps to support this transparency while keeping data safe and secure. Previously, I wrote about the intersection of SEM and enrolment forecasting and how these can be used in conjunction to help your institution achieve its goals over the longer term. Today I want to talk about how having a robust data governance program and platform can support SEM and enrolment forecasting at your institution. SEM and enrolment forecasting are already complicated enough – why on earth would you want to add data governance on top?

Without effective data governance, data can become meaningless, such as with competing definitions for the same terms. For example, as we move back to being able to offer classes in-person, let’s say you were trying to determine which delivery format would best suit the needs of your students, whether that be online, hybrid, or in-person. From the data you find, you note a decrease in student enrolment over the last few years when classes were only offered online – does that mean students prefer in-person classes? Before deciding to move all of your classes back to in-person, you look at other faculty enrolments and notice enrolment did not drop across the board, so now you’re wondering what other data is available to you to help you decide which delivery format to offer specific courses. You bring your question to Institutional Research and they ask you to provide several definitions to your query, some of which may include:

  • Is the number of students a headcount, full-time equivalent, or other?
  • Which type of faculty instruction do you want to consider – tenured professors, adjunct faculty, teaching assistants?
  • Do you want the answer to include only numbers on a specific campus, or should it include all locations?

Questions like those above make it clear that while often data is talked of as immutable, cut-and-dry facts, it is instead a tool whose origin and purpose must be clearly outlined in order to be useful. At its core, data governance is the formal execution and enforcement of authority over the management of data and data-related assets (Seiner, 2014). Building a data governance program is an iterative process. Seiner (2014) describes data governance as “an evolution, not [a] revolution,” noting,

Data governance won’t be completed all at once. Different organizations transition themselves into a data governance state in different ways. Some organizations focus early on specific domains or subject areas of data. Other organizations concentrate on specific business areas, divisions, units, or applications rather than implementing all across the organization at once. Still other organizations focus on a combination of two or three specific domains within business units using specific applications. (p.9)

One example I hear often is that some community members in higher education view SEM as the corporatization of the academy. While I respectfully disagree, I can see how this view is formed when the application of SEM is bumpy or appears to align only with financial goals. Data governance can help promote transparency in SEM and enrolment forecasting in a few ways:

  1. Promoting knowledge of data definition. Having solid definitions can help your stakeholders ensure that you and they are on the same page and avoid misunderstandings when we use the same word to mean different things (enrolment, anyone?). It can also help ensure that all stakeholders are consulted in the first place, avoiding “central” deciding a definition and forcing it on others.
  2. Promoting knowledge of how data is transformed. Tracing data lineage to get how your stakeholders are consuming it. For example, perhaps we have a dashboard that shows what our projections are for the future. With visual data lineage, your stakeholders could trace the data all the way from that dashboard, through the various steps, back to the source. It’s theoretically possible that they could even trace all the way back to the place in the student information system where they can see how their own class lists contribute to the broader institution.
  3. Making visible people who perform various data governance roles. For example, many institutions will have data owners or data stewards. Your stakeholders could reach out to these individuals if they spot something off with the data, or simply if they have a question.

One of the keys to both SEM and Enrolment Forecasting is trust amongst your stakeholders, and conveniently data governance promotes trust in the data that you share. By applying logical rules around who should have access to data, when, and why you can actually improve collaboration by intentionally sharing information in a secure and confidential environment.

Seiner, R. (2014). Non-invasive Data Governance. Basking Ridge, NJ: Technics Publications.