With modern technology, institutions can be redesigned as smarter universities to deliver a host of benefits beyond learning.
For years, higher education institutions have collected vast amounts of data about their students, programs, and facilities. However, many have not been able to effectively use learner, scholar or institution data to improve resources, processes and workflows.
But now, with modern technology, institutions can be redesigned as smarter universities to deliver a host of benefits beyond learning.
- By analyzing their data, institutions can select the most innovative approaches to increasing student engagement and success rates.
- Analytics can help institutions determine ways to improve retention and graduation rates to increase revenue, such as receiving notifications when a student’s engagement is low to try and prevent the student from dropping out .
- Using chatbots, educators can deliver personalized content to students to enhance teaching/learning.
- With dashboards, alerts, and communication between faculty and students, institutions can improve school counseling by empowering students with information.
- Data analysis can improve researchers’ access to information, leading to better use of scientific literature and analysis.
Challenges to overcome
However, these benefits are not just there for the taking, and institutions and researchers face several challenges when it comes to effectively using their data:
- Store and analyze large volumes of existing and future data.
- Data such as documents, photos, audio recordings, and videos are unstructured and cannot be stored in a database, making it difficult to search and analyze.
- Disparate data is complicated to integrate.
- It can be difficult to get valuable information quickly.
- Validating data from various sources can be problematic.
- Data governance is necessary to make data more accurate and usable, but can be complex with policies and technologies.
- Data security of advanced analytical tools – often in multiple places – used for unstructured data and non-relational databases can be challenging. Securing a large institution and complying with data privacy laws and regulations can be difficult.
Cloud computing presents a viable solution to unify all data on a single platform to facilitate and accelerate access and simplify analysis of valuable information:
- The implementation of cloud computing offers institutions unlimited scalability at a reduced cost. This means better infrastructure with flexibility, access to data, ease of monitoring and improved quality of data processes.
- Cloud computing provides institutions with increased functional capabilities, such as better data analysis and access to machine learning algorithms that can improve decision making.
- Since the data is stored in a central location, institutions can access the data from anywhere on various platforms.
- Through data analytics, institutions can create personalized learning environments and better teaching methods for students and improve administrative processes to reduce potential dropout and failure rates.
- For researchers, cloud computing can fundamentally change the way they interact with data, devices, and each other. It offers them the advantages of being open, flexible, fast, cost effective, scalable, efficient and responsive.
cloud computing infrastructure
The most suitable cloud option for educational institutions to take advantage of these benefits is through the use of data warehouses, data lakes, and data stores.
A data lake stores raw data across the institution even if that data is not yet in use. In turn, the data warehouse stores all the data that has been prepared for research, reporting, and advanced analytics. It is often the preferred source for quickly generating reports and visualizing organized data. The data store provides views on a reduced set of data that a specific research unit or department can use.
With these three components, the infrastructure will look like this:
- The data lake is the raw data storage unit in the data warehouse. From there, the data can be extracted for analysis and research purposes.
- The data warehouse models research data to support research and advanced analysis or machine learning.
- Data stores are created from the data warehouse and organize data into searchable views that can use the data for search and analysis using built-in dashboards.
By implementing this infrastructure, educational institutions ensure that they have a robust solution that performs well and always makes data available for research, analytics, and artificial intelligence implementations. This infrastructure effectively ensures that institutions can meet data retention requirements related to grant-funded research, is fully scalable, and can easily be adapted to accommodate new technologies. It supports ensuring that data is secure and compliant with laws and regulations. To help eliminate complexities and extensive coding, data automation technology is available to create, manage, and migrate to data lakes, data warehouses, and data marts.
Micah Horner is Product Marketing Manager at TimeXtender, a low-code, drag-and-drop data builder that empowers organizations to make better business decisions, while supporting compliance and governance of their data infrastructure. He is passionate about technology, storytelling and strategic messaging.