Universities today face mounting pressure to enhance efficiency, improve student experiences, and make data-driven decisions across admissions and enrollment. However, integrating and streamlining these critical workflows is not just a technological challenge—it requires strong stakeholder alignment, clear objectives, and a commitment to centralizing accurate, reliable data. Without a unified approach, institutions risk inconsistent reporting, operational bottlenecks, and missed opportunities to optimize the student journey.

At Northeastern University, where thousands of students engage with multiple departments throughout the admissions process, the need for seamless data integration is paramount. That’s why Fivetran’s Kelly Kohlleffel sat down with Ilia Xheblati, Director of Analytics Engineering at Northeastern University, to get to the heart of embracing scalable, automated data solutions so universities can eliminate inefficiencies, streamline decision-making, and create a more connected, student-centric experience.

Bringing data-driven efficiency to university admissions and enrollment

Admissions and enrollment are the backbone of any university’s operations, particularly at large institutions like Northeastern University, where thousands of students interact with various departments. As universities strive to do more with data, integrating and streamlining these core workflows requires more than just technology implementation — it demands stakeholder alignment and a clear vision of how data can improve daily operations.

According to Xheblati, the first step in driving this transformation is asking the right questions:

• How can you centralize data to eliminate inconsistencies?
• What problem are you solving, and how does it impact different teams?

Without a unified source of truth, frustration builds across departments. Disparate reports lead to conflicting numbers, making it difficult to trust data. By focusing on centralized, accurate, and accessible data, universities can streamline decision-making, enhance enrollment analysis, and improve the overall student experience.

Scaling data movement: Replacing manual processes with automation

While data engineers often build and maintain data pipelines, the reality is that manual and DIY processes are time-consuming, difficult to scale, and require ongoing maintenance as source systems evolve.

According to Xheblati, scaling data movement isn’t about complexity — it’s about efficiency. While the technical process of moving data can be taught, the real challenge is the time required to build, maintain, and continuously adapt to changes in source systems. Ongoing updates, schema changes, and maintenance overhead make DIY approaches increasingly difficult to sustain.

The turning point for Xheblati’s team came when they compared a manual data movement process with Fivetran’s automated approach through a proof of concept. The results were clear:

• Speed and simplicity: Fivetran reduced setup time to minutes, seamlessly moving data into Snowflake.
• Scalability: Without a dedicated data engineer or IT department support, manual processes would have created bottlenecks, necessitating a platform like Fivetran.
• Trust in automation: Initially, concerns over automation led to rigorous quality control checks. Once teams verified data accuracy and standardization, adoption was easy.

For organizations without the resources for a dedicated data engineering team, automated tools unlock efficiency, reliability, and faster collaboration, and for organizations that already maintain data engineers, the scarce engineering time can be directed to higher-value projects. Either way, by removing the burden of constant pipeline maintenance and IT dependencies, businesses can scale data operations more effectively, allowing teams to focus on strategic insights rather than infrastructure upkeep.

A pragmatic approach to adopting new data tools

Implementing new technologies in a large organization can be a daunting task, but the key to success often lies in starting small. Identifying early adopters within your team — those eager to test new solutions and overcome existing challenges — is a crucial first step. These early adopters act as the foundation for testing and iterating on processes before scaling them across a larger organization.

However, adopting new tools isn’t just about finding the right technology — it’s about clearly communicating benefits and aligning expectations across stakeholders. This means emphasizing the return on investment (ROI) in tangible terms by focusing on both the financial benefits — such as cost savings from reduced engineering effort — and the technical benefits, like faster workflows and simplified processes.

Clear communication with stakeholders is also essential to gain buy-in. Articulate how the tools will make tasks easier, faster, and more reliable — which gets everyone at the organization aligned on the value these solutions bring. This collaborative, incremental approach ensures that expectations are set, benefits are understood, and the adoption process is seamless, effective, and scalable.

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