Just like you wouldn’t use a sledgehammer to hang a picture nail, university IT must find the right tools for the analytics challenge.
Data analytics has become commonplace on college campuses as universities have developed into repositories for all sorts of data focusing on everything from student success and recruitment, to enrollment management, faculty retention, finance and budgeting. And those are merely the administrative records collected. Universities also house some of the largest research institutions in the country, resulting in the creation of tremendous amounts of data on a daily basis.
However, storage can be the most prohibitive obstacle in creating a data analytics system. As the application of data grows, so too will the volume collected. University IT departments will have to find ways to handle the massive amounts of data that needs to be stored and accessed.
But in a time of shrinking budgets, universities are hard pressed to find ways to effectively manage this deluge.
Know the data first
There are many types of data. It can be categorized as:
- Structured data, which can reside in clear fields within a database. Structured data is normally associated with the academic or administrative offices. Often times the data behind the student information system or other administrative ERP systems are structured data.
- Unstructured data, which is information that doesn’t fit into a traditional database format
- Semi-structured, which has components of both.
Unstructured and semi-structured data—such as video or social media – is where the data explosion is really taking place at the university level. Colleges need to be able to store and provide access to this data at high speeds to multiple sources in order to turn that data into actionable information.
There are three methods most often employed related to data storage: flash storage; traditional mechanical disk storage; and cloud storage. Much as you wouldn’t use a sledgehammer to hang a picture nail in your living room, when it comes to the storage of data, you need to be sure to use the right tool for the job.