Higher Education & Data Analytics
Big Data Analytics Project
Project Story
The Maltepe University Big Data project addressed the need to evaluate data generated across different institutional processes more holistically. A big data approach is not just about storing more information; it is about making data from different sources meaningful, analyzable, and connected to institutional questions.
We first considered which data could answer which needs and which analyses would help stakeholders. This approach made the technical infrastructure a means to support planning and evaluation, rather than an end in itself.
Challenge
Academic, student, and administrative processes at universities generate data across different sources and formats. Reviewing this information separately makes it harder to identify institution-wide trends and produce consistent answers from multiple sources.
The challenge was not simply to collect large volumes of data, but to preserve meaning, quality, and context. Analysis needed to be clear for decision-makers and flexible enough to expand as new needs emerged.
Our Approach
We began by identifying data sources and the institution’s analytical needs. We clarified which data would support which institutional questions and created a shared analytical framework across sources. Data modeling and analysis were designed around the university’s evaluation needs, not just technical transfer.
We addressed collection, organization, and preparation for analysis as part of one big data approach. The work focused on consistent data structures, meaningful relationships across sources, and analytical outputs that users could understand.
Technology choices followed project goals, with scope, data quality, and planned analysis shaped together. This created a foundation that can evolve as new sources and analytical needs arise.
Focus Areas
Outcome
The Big Data project established an analytical foundation for Maltepe University to evaluate data from different sources through a shared lens. A more structured approach helps the institution examine trends and relationships beyond individual records.
The project was designed to support data-driven planning and evaluation. It provides an adaptable data approach for the university’s future analytical needs without relying on a specific technology brand or an unpublished performance claim.
