Key Takeaways
- Higher education institutions face challenges in harnessing fragmented college admissions data from multiple systems (CRMs, SISs, LMSs), which complicates the extraction of actionable insights.
- Implementing standardized methodologies, such as the Cross-Industry Standard Process for Data Mining (CRISP-DM), helps integrate disparate data sources, simplifying analysis and enhancing decision making.
- Effective data utilization involves transforming raw data into actionable insights, presented clearly to inform strategic decisions, and continuously refining processes to adapt to changing student demographics and regulations.
- Liaison’s technology provides an integrated approach to maintaining comprehensive data throughout the student journey.
In today’s data-driven world, higher education institutions find themselves awash in a sea of information that has the potential to transform their admissions strategies significantly. Yet harnessing this data effectively can be complicated and time consuming.
For admissions teams, overcoming these obstacles to utilize data comprehensively and strategically is essential. This approach ensures that decision making is guided by insights that align with institutional goals and student needs, rather than being overwhelmed by sheer volume or disjointed data points.
Overcoming the Challenge of Fragmented College Admissions Data
For many institutions, college admissions data is abundant but fragmented, residing across various systems such as CRMs, SISs, and LMSs. This dispersion can hinder the effective use of information, as pulling insights from multiple sources often leads to complications.
Additionally, many institutions fail to consider the complete data pipeline relevant to specific questions or challenges within the admissions process. For example, it’s common to retain data on enrolled students while neglecting information about applicants who were either accepted and did not enroll or were not accepted at all. This selective data retention can skew analytics and machine learning models, potentially leading to biased outcomes.
A unified dataset can simplify analysis and enhance decision making in the college admissions process. The key challenge lies in integrating these disparate data sources to form a coherent, actionable whole. Implementing standardized methodologies for data handling, like the Cross-
Industry Standard Process for Data Mining (CRISP-DM) can help by offering a systematic framework for analyzing data. For example, CRISP-DM is segmented into distinct phases: identifying the issue, comprehending the data, preparing the data, developing models, evaluating them, and deploying the solutions.
Liaison’s technology can also simplify higher-ed data analytics, offering an integrated approach that maintains comprehensive data throughout the student journey, from initial inquiry to enrollment and beyond. Liaison does this by integrating various tools, such as our Outcomes and TargetX CRMs, Centralized Application ServiceTM (CAS), and Othot advanced analytics platform, to help build and manage the recruitment funnel. This not only maintains a coherent data lineage, but also ensures that all applicant data contributes to a continuously improving college admissions process.
Using Big Data in Higher Education for Decision Making
Once the data is organized in a unified system, the challenge becomes using it. Transforming raw data into actionable insights is a pivotal aspect of data utilization, enabling institutions to make informed, strategic decisions. This process begins with a meticulous assessment of the raw data, identifying quality issues and patterns. Following this, the data must be cleaned and structured appropriately to ensure accuracy and usability.
The next step involves deep analysis using statistical tools and algorithms to derive important patterns and trends. This can include segmentation analysis to categorize data into meaningful groups or predictive modeling to forecast future trends based on historical data.
To be actionable, these insights must be presented in a clear, understandable format, often through graphs and charts, which allow decision makers to grasp complex information quickly and make informed choices. Liaison’s platform excels in this area by providing tools that translate complex datasets into clear, action-oriented insights for admissions teams.
Continuous improvement is another core principle of effective data use in college admissions. Institutions must not only collect and analyze data, but also refine and improve their processes on a regular basis to continue enhancing their admissions strategies. Liaison’s platform supports this dynamic approach by enabling institutions to track the efficacy of different strategies and make adjustments as needed. This adaptability is critical in responding to the ever-changing landscape of student demographics and higher education regulations.
Harnessing Data for Strategic Admissions Success
The effective use of data in admissions is not just about collecting information but about transforming this information into insights that drive strategic decisions. Liaison’s platform facilitates this transformation by offering robust analytics tools, integrated data management, and strategic insights that help institutions realize their admissions goals. By leveraging big data and harnessing the power of advanced analytics, higher education institutions can navigate the complexities of the admissions process with confidence and precision, ensuring that every decision is informed, equitable, and aligned with their broader educational missions.
Institutions that embrace these tools and strategies will be able to streamline their operations while also enhancing their ability to serve their students better. That, in turn, will enable them to achieve successful outcomes for everyone.
Learn more about how Liaison’s Othot platform is putting data to action with predictive and prescriptive analytics. Request a demo today.