Data and analytics are being used to address a wide variety of strategic priorities in higher education—from supporting student success, to monitoring enrollment KPIs, to maintaining financial sustainability, to enhancing teaching and learning. Moreover, because of its proven ability to drive positive change, data-enabled decision-making—or the “analytics mandate”—has risen to become a top priority itself at many institutions. Yet despite the wide recognition of the importance of analytics, there seems to be a steady stream of publications from key stakeholders across the industry calling for increased action and investment.
Most higher education institutions have made investments in various components of the analytics and decision support ecosystem—data warehouses, business intelligence (BI) tools, outside consultants. As we looked to understand why the benefits of these investments have not been fully realized, we gleaned key insights from outside industries that are more mature in their adoption of analytics. What our research found is that higher education is missing a key ingredient: a mechanism to store and source data from disparate systems, in one central location, under a governed data model. This type of solution can help institutions further realize the potential of analytics through the widespread democratization of data and insight.
In the past an institution may have had three to four limited and centralized core systems on campus. Today this number could be in the dozens if not hundreds.
A proliferating ecosystem of technology
Over the last decade, the advent of cloud-based technology has ushered in a massive expansion in the number of systems on campus that produce and consume data. In the past an institution may have had three to four limited and centralized core systems on campus. Today this number could be in the dozens if not hundreds. This results in an ecosystem that is not only siloed but much more complex than ever before.
Key barriers to analytics readiness
In the past an institution may have had three to four limited and centralized core systems on campus. Today this number could be in the dozens if not hundreds.
Growing digital demand, manifested through new technologies and applications on campus, has overwhelmed IT organizations with integration projects that require connections between disparate systems. However, for most institutions, this demand has not been supplemented with increased resources, leading to a large backlog of IT projects and underdeveloped integrations.
At its core, data governance is about making sense of data that is produced on campus and ensuring that its meaning is consistent as it moves around the organization. This has become an increasingly difficult technical and cultural task, given the expanding ecosystem of technologies and the growing demand for data to fuel strategic decision-making.
Data users are clamoring for better access to data. This overburdens central decision support teams with basic data requests, crowding out strategic work. Central decision support teams estimate that anywhere from 25 to 100 percent of their capacity is dedicated to responding to ad hoc data requests, many of which are for basic institutional data such as enrollment figures. These requests come at significant opportunity cost.
What Is a data management platform (DMP)?
A DMP is a centralized framework for unifying and staging data according to common business use case definitions and making that data available to integrate into other systems or BI solutions. There are three defining characteristics of a DMP. A DMP must be open-purpose, vendor-agnostic, and future-proof.
Open-Purpose
Instead of requiring source system experts to enable access, the DMP democratizes data through an industry-specific data model that makes it available using real-life definitions, not those of source system tables. It also improves data security by all but eliminating the need for shadow systems. Data stakeholders can now get access to the information they need, whether directly from the DMP or via another application, such as a BI tool, while resting assured that data will stay out of the hands of those who don’t need it.
Vendor-Agnostic
While many source system vendors provide their own solutions to aggregate data, organizations need a flexible solution that enables an open architecture demanded by modern cloud applications. The number and types of technologies are increasing exponentially. Successful data management is not about constraining that growth or enforcing arbitrary limitations but rather providing a flexible framework that evolves with the needs of the organization.
Future-Proof
Indeed, organizational needs will undoubtedly evolve. New use cases, new users—all these things will require organizations to remain agile and value data as one of their most critical assets. This means that the DMP cannot remain static. It must allow for and facilitate change. Whether a move to a new cloud-based ERP or the adoption of a new mobile application, the DMP can minimize disruption, accelerate sustainable innovation, and enable organizations to fully activate their analytics mandate.